The Role of Artificial Intelligence in Enhancing Diagnostic Radiology: Applications and Advancements in Medical Imaging in Saudi Arabia

The Role of Artificial Intelligence in Enhancing Diagnostic Radiology: Applications and Advancements in Medical Imaging in Saudi Arabia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Role of Artificial Intelligence in Enhancing Diagnostic Radiology: Applications and Advancements in Medical Imaging in Saudi Arabia Amirah Fahad Alshammeri, Bashayr Sulaiman Almarzooq, Rana Nizar Raghib, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7724943/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Artificial intelligence (AI) is changing the face of healthcare, especially in the field of diagnostic radiology, and improving the accuracy and efficiency of medical images. In Saudi Arabia, AI is revolutionizing workflows, able to advance early disease detection, as well as reduce the number of human mistakes. However, issues such as regulation and specialized training requirements persist. Purpose This research examines the AI awareness and knowledge of diagnostic radiologists in Saudi Arabia with an emphasis on AI and medical imaging. It assesses AI awareness in trainees and professionals in radiology and discusses possibilities for integrating AI into the teaching curricula. In addition to the key highlight, the study aimed at finding regional variations in perceptions about the role of AI in diagnostic radiology. Materials and Methods A retrospective methods chart review and survey was performed over the course of five months (June-September 2014), to survey 500 different radiologists and radiology residents. Data were gathered through surveys sent out through radiological societies and social media. The statistical analysis was conducted using version 26 of the Statistical Package for the Social Sciences (SPSS) with a p-value less than 0.05. Ethical approval was granted by the University of Hail. Results For example, out of 433 respondents, 80.6% were aware of the existence of AI, and 82.2% were aware of the potentially positive effects of AI for improved diagnosis. However, concerns about job displacement and machine errors were noticed. Moreover, there was a greater proportion of interest in AI education, with a total of 81.5%, radiologists in the central areas showing a higher level of knowledge in the northern region (P = 0.023). Those with more education had greater awareness of AI (P = 0.001). Conclusion AI has great potential for benefits to the field of diagnostic radiology in Saudi Arabia, with challenges regarding regulation, training, and the issue of privacy that need to be addressed. The study highlights the significance of integrating AI educational content into the medical curriculum, aligning with Saudi Vision 2030. Artificial-Intelligence (AI) Diagnostic Radiology Medical Imaging Saudi Arabia Radiologists' Knowledge AI Integration in Healthcare Figures Figure 1 Figure 2 1. Introduction Artificial intelligence (AI) is swiftly changing a variety of industries, and understanding the growing importance of using AI in healthcare, namely diagnostic radiology, is among them [ 1 , 2 ]. The science of artificial intelligence aims to develop computer systems capable of performing tasks typically handled by humans, including speech recognition, decision-making, and language translation. From the point of view of diagnostic radiology, for working with physicians to read medical images, the AI may allow for becoming more efficient and more accurate for medical diagnoses [ 3 , 4 ]. The use of AI in medical imaging has experienced significant exponential growth over the last few years, driven by advancements in AI, machine learning algorithms, and deep learning. This technological shift enables the application of AI to aid in the analysis of complicated medical images, thereby assisting in a better and quicker diagnosis [ 5 , 6 ]. The integration of AI in diagnostic radiology offers several advantages, ranging from improved decision-making to enhanced patient care. The ability of AI to crunch numbers and identify patterns from medical images has the potential to help speed up early diagnosis and the specific treatment strategy [ 7 ]. Recent studies have demonstrated that AI applications, such as machine learning algorithms, can analyze medical images to detect abnormalities with higher sensitivity and accuracy than human radiologists [ 8 ]. For example, the time involved in examining an image is significantly reduced with the help of AI, which aids in faster diagnosis and treatment processes. In addition, the ability of AI to minimize radiation exposure from unnecessary scans is another key advantage, further enhanced by potential improvements in patient safety and cost-effectiveness [ 9 , 10 ]. These advancements have witnessed AI form a pillar in the continuous advancement of medical imaging, where it offers tools to augment the capabilities of diagnostic radiologists. In Saudi Arabia, the adoption of AI in Healthcare, more especially in medical examination radiology, has achieved impressive development in recent years. The Saudi healthcare sector has been making significant strides towards the integration of AI technologies to enhance patient outcomes and streamline healthcare delivery. Recent reports suggest that AI-based systems are being introduced in Radiology departments across the country to support the radiology diagnostic workflow, aid in image interpretation, and improve overall clinical decision-making [ 11 , 12 ]. However, the implementation of AI is not without challenges. Despite the enormous potential, there have been several issues with the use of AI in medical imaging, including image distortion, resource constraints, and regulatory barriers, which have hindered the widespread adoption of AI in medical imaging. Even though this vast potential for medical imaging is enormous, one of the issues that arises with the use of AI is the issue of image distortion, a resource constraint, and a regulatory constraint, which has been the bane of the large-scale use of AI in medical imaging [ 12 ]. These challenges highlight the need for continued research and development in the field to ensure that the integration of AI into clinical practice is effective and safe. This study aims to determine the level of knowledge and understanding among diagnostic radiologists in Saudi Arabia regarding AI and its application in medical imaging practices. It will enhance the concept of training and education on AI for radiologists, and also provide an opportunity to apply it to diagnostic radiology. By looking at what currently exists with the adoption of AIs in the country, the study aims to identify the potential areas to further integrate AI in Radiology training programs. The findings will give insights on the contributions of AI towards the enhancement of the diagnostic radiology practices in Saudi Arabia. And, it will add to the ever-increasing body of knowledge in this area. This research is very important in understanding how AI can improve the diagnosis process and reshape the future of radiology science in Saudi Arabia. 2. Materials and Methods 2.1. Study Design and Participants This study used a cross-sectional online survey methodology to determine the knowledge and attitudes of radiologists, residents, and seniors in the field of radiology in Saudi Arabia regarding AI in medical imaging. The survey, conducted in September 2024, targeted professionals and students from five regions of Saudi Arabia: Central, Northern, Eastern, Western, and Southern. A convenience sampling method was used for recruiting people from different sources, including radiological societies, professors of these activities, and social networks. Participation in the study was voluntary, and submission was based on availability. A total of 500 people were invited to complete the survey with a total sample size of 320 people who were able to complete the survey. 2.2. Survey Instrument and Data Collection The instrument for collecting data was a structured and self-administered questionnaire written in English. The questionnaire was divided into four sections. Demographic Data: This section gathered data on the participants' region, age, gender, and educational level. General AI Knowledge: Consisting of five items, this section measured the fundamental understanding and perceptions about artificial intelligence in the participants. AI in Medical Imaging Knowledge: A more specific set of questions, totaling ten items, assessed knowledge about applying AI in radiology, such as lowering radiation doses and identifying pathology. Clinical Practice and Curriculum: This section included seven items that addressed opinions around participants ' views towards AI education and its practical application in radiology curricula. The survey was distributed electronically by using Google Health. The participants were assured of their anonymity, and responses were gathered in September 2024. The survey was intended to be easy to complete (mean completion time 10–15 minutes). Ethical considerations called for no personal identifying information to be collected. The participants were informed that their answers were only for research purposes. 2.3. Data Analysis The collected data were analyzed with IBM (SPSS) Statistics version 26. Demographic characteristics and the responses of participants were summarized with frequencies and percentages using descriptive statistics. In the Electronics and Information Sciences Lab, students were tested on topics related to chemical or physical changes to help determine their general knowledge. To analyze the overall understanding of participants, a knowledge score was created by summing the number of correct responses (1 point for each correct response). A score of 60% and above was categorized as 'good knowledge, and scores below 60% were considered to be 'poor knowledge.' Chi-square test was used to analyze associations between demographic variables and levels of knowledge. A p-value < 0.05 was considered statistically significant, indicating a meaningful relationship between the variables. 2.4. Human Ethics and Consent to Participate This study received approval from the Research Ethics Committee (REC) of the University of Hail, Saudi Arabia (Approval No: H-2024-489, Date: 4/11/2024). All participants gave electronically initiated informed consent before participating in a survey. They were told of the study's purpose, confidentiality, and their right to withdraw from the study at any time and without consequences. The research study was conducted in accordance with ethical guidelines for conducting research with human participants. It made allowance for maximum regard for confidentiality, with respect to data, at all times. 3. Results and Analysis 3.1. Participants' Characteristics The demographic characteristics of the 433 study participants are explained in Table 1 and serve as an essential context for the perception analysis of AI usage in the diagnostic radiology field in Saudi Arabia. The survey initially was addressed to a whole population of nearly 500 respondents, with the effective participation comprising 433, which can be seen as a good effective population participation rate. Table 1 shows that the Southern and Western region respondents accounted for the highest percentages of respondents, 28.5 percent and 27.4 percent, respectively, while the respondents in the Central (14.6%), Northern (16.4%), and Eastern regions (13.1%) had also low percentages of respondents. This indicates regional disparities, which may represent regional differences in the accessibility of healthcare infrastructure and exposure to advanced imaging technologies, both of which are important to the uptake of AI-driven diagnostic systems. Table 1 Personal characteristics of the study participants (n = 433). Characteristic Category n % Region Central Region 40 14.6 Northern Region 45 16.4 Eastern Region 36 13.1 Western Region 75 27.4 Southern Region 78 28.5 Age (years) 18–28 374 86.4 29–39 47 10.9 40–50 12 2.8 Gender Male 126 29.1 Female 307 70.9 Education Bachelor degree 372 85.9 Resident 4 0.9 Diploma 26 6.0 Specialist 10 2.3 Consultant 21 4.8 Moreover, from this table, we also see that there was a higher proportion of female respondents (70.9%) as compared to male respondents (29.1%). This distribution is consistent with trends in Saudi Arabia, where females have an increasing presence in medical education and the health sciences over the last few years. The predominance of female voices in this study highlights the significant role they play in influencing perceptions about integrating AI into radiological practice. It helps ensure that, as well as the views of emerging female healthcare professionals, those of established female healthcare professionals are also taken into account when studying the readiness for technological adoption. Furthermore, it shows that most of the participants are bachelors (have a bachelor's degree, 85.9%), and smaller proportions of diplomates (6.0%), residents (0.9%), specialists (2.3%), and consultants (4.8%). These results, coupled with the age distribution, which indicated that 86.4% of them were between 18 and 28 years of age, suggest that the sample population consisted mainly of early-career professionals or trainees. This group may have less clinical experience, but shows greater adaptability and receptiveness to innovation, and is therefore likely to be an essential driver regarding future implementation of AI in diagnostic radiology. By contrast, the relative lack of consultants and specialists reflects that the perspectives of senior practitioners (those with the most familiarity with clinical workflows) were less prominent in this data set. 3.2. Knowledge and Perception of AI Table 2 shows the outcome of a survey that was conducted with 433 individuals about their knowledge and perceptions of AI in medical imaging. The responses indicate a general understanding of the relevance and influence of AI in the healthcare sector. A significant result of 63% of the participants said they gained knowledge of what AI is in whole, and there are 80.6% of participants said that they possess general knowledge about AI. This denotes a high level of familiarity with AI, which is essential to grasp AI's potential to serve in medical imaging. Moreover, a large majority of 83.1% of them recognized AI as a game-changer technology in the health industry, representing a general acceptance of the growing role played by AI in medical practices. When zooming in on certain aspects of the perceptions surrounding AI in medical imaging, results show that while a majority of the participants feel the impact of AI in medical imaging is positive, there's significant concern around implications for the workforce. For example, 48% of respondents believe that AI will be a threat when it comes to job security, and 45% worry that AI could be a replacement for radiographers. However, despite such apprehensions, a high percentage (82.2%) agrees that AI would have a positive effect on medical imaging. This presents a contrast between the benefits of AI, including reduced radiation dose levels (72.1%) and increased accuracy in pathology detection (71.6%), and the concerns about its impact on employment in the healthcare sector. Table 2 Study participants' knowledge and perception of artificial intelligence in medical imaging (n = 433). Knowledge Domain Yes n Yes % No n No % Not sure n Not sure % Do you know the full meaning of AI? 273 63.0 45 10.4 115 26.6 Do you have any knowledge about AI in general? 349 80.6 32 7.4 52 12.0 Do you think AI is a bad technology? 47 10.9 306 70.7 80 18.5 Do you think AI poses a threat to job security? 208 48.0 132 30.5 93 21.5 Do you think AI is bringing changes to the health sector? 360 83.1 22 5.1 51 11.8 AI incorporated into current imaging modalities 291 67.2 33 7.6 109 25.2 AI helps reduce radiation dose levels 312 72.1 34 7.9 87 20.1 AI plays a role in patient positioning 258 59.6 88 20.3 87 20.1 AI helps detect pathologies in CT and MRI scans 310 71.6 33 7.6 90 20.8 AI increased the accuracy in chest pathology identification 298 68.8 31 7.2 104 24.0 Awareness AI is an emerging trend in medical imaging 315 72.7 55 12.7 63 14.5 Aware AI is emerging in Hail’s radiography sector 184 42.5 124 28.6 125 28.9 AI would have a positive impact on medical imaging 356 82.2 36 8.3 41 9.5 Concern that AI will displace radiographers 195 45.0 164 37.9 74 17.1 Acknowledge the possibility of machine errors with AI 331 76.4 27 6.2 75 17.3 Additionally, when it comes to implementing AI into existing imaging techniques, we see a high level of faith in AI's capabilities among the data. For example, 67.2% of the respondents recognized the integration of AI in modern imaging processes, and 71.6% of the respondents’ thought AI has a substantial role in Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) scan pathology detection. However, a significant number of participants were unsure about AI's full potential, including 25.2% who were uncertain about AI's role in current imaging and 17.3% who acknowledged the possibility of machine error with AI. These findings indicate that although AI's benefits are widely accepted, further education and training are needed to address doubts and boost medical professionals' confidence in integrating AI into their daily clinical practice, particularly in Saudi Arabia's rapidly evolving radiology sector. 3.3. Clinical Practice, Education, and Training The research results obtained from the data in Table 3 reflect the views held by the participants on the aspect of the integration of AI in clinical practices and medical imaging fields in Saudi Arabia for educational curricula. A good proportion of respondents (71.6%) have thought that AI can also improve medical imaging education, and 66.1% admit that clinical practice helps them understand the role of A.I. Similarly, a majority of 71.6% agreed that clinical practice plays a role in mounting knowledge about the applications of AI, which implies that the exposure to practical diagnosis scenarios is an essential factor that creates awareness of the potential of AI applications. Despite these positive findings, almost one-fifth (19–23%) of the participants expressed uncertainty about these benefits, suggesting a need for more organized training and awareness Programmes, at least for students and practitioners. Table 3 Clinical practice and curriculum for AI role in medical imaging among study participants (n = 433). Clinical Practice & Curriculum Yes n Yes % No n No % Not sure n Not sure % Believe AI would improve education in medical imaging 310 71.6 40 9.2 83 19.2 Clinical practice helps appreciate the AI role 286 66.1 46 10.6 101 23.3 Clinical practice increases AI knowledge 310 71.6 39 9.0 84 19.4 Current curriculum should incorporate AI modules 283 65.4 67 15.5 83 19.2 Would like to learn more about AI prospects 353 81.5 40 9.2 40 9.2 Have ever used AI in your career 175 40.4 195 45.0 63 14.5 Have heard about AI applications in radiology 242 55.9 136 31.4 55 12.7 In addition, the results show a high rate of enthusiasm towards the official curriculum integration of AI. Also, about 65.4% of the respondents approved the integration of AI modules in medical education, and 81.5% of them agreed to learn more about the AI prospects in radiology. These findings reflect the demands of artificial intelligence for healthcare professionals in applying training related to medical imaging. However, 15.5% disagreed with curricular integration, and slightly less than 20% were unsure about the immediate relevance of AI to clinical training, which reflects a lack of clarity regarding the immediate relevance of AI in clinical training. Addressing this through the reform of the curriculum and stand-alone workshops in the medical institutions in Saudi Arabia could potentially be the path forward for aligning the education of medicine with the ongoing development of digital healthcare reform in Saudi Arabia under Vision 2030. Interestingly, the survey has also shown a divide in the actual use of AI within the careers of the people who took the survey. Only 40.4% of respondents have taken advantage of AI in their professional practice, while 45% have not, and 14.5% don't know. This discrepancy highlights the challenges faced in adopting AI technology in clinical settings, which might be attributed to factors such as limited access to AI technology tools, inadequate training, or skepticism about the effectiveness of AI. Furthermore, while a large percentage of participants have heard of applications of AI in radiology (i.e., 55.9%), there is still a significant amount (44.1%) that have either not heard of or are uncertain about these applications. These findings indicate conspicuous awareness and interest in AI; however, there are still gaps in AI practical implementation knowledge in the field of medical imaging in Saudi Arabia. Further, Fig. 1 shows the algorithm development methods taken into account by participants in the study ("Machine Learning" and "Deep Learning" being the most frequently used approaches). Both approaches explain over 30% of the answers, part of the consideration of the increased relevance of AI in the diagnostic radiology field. "Basic Learning" is close behind, indicating that some traditional methods are still in use. "Other" methods are the least reported, and this may suggest that less common techniques lack application in the context of radiology. This distribution demonstrates the growing integration of advanced AI methods, such as machine learning and deep learning techniques, in radiology, in keeping with the ever-increasing need for AI-inspired diagnostic enhancements in Saudi Arabia. 3.4. Overall Knowledge Level and Associated Factors Table 4 shows the correlation between several factors and the results of participants' knowledge and AI about medical imaging characteristics. The results indicate a significant effect of geographical location on the degree of understanding of AI's essence, with people from the central region demonstrating the highest level of good knowledge (80%), followed by other regions. In contrast, in the Northern, Eastern, and Southern regions of the country, and among people recruited from these areas, the percentage with good knowledge is lower, with the last one being noted as 53.8%. Note that the p-value of 0.023 indicates the region is a significant factor in AI knowledge, suggesting potential regional differences in AI resource availability or training. The table also shows that while age and gender don't have any significant impact on AI knowledge (p-values of 0.792 and 0.636, respectively), education level has a considerable impact. Notably, those with a Bachelor's degree are better positioned regarding knowledge (58.3%) compared to their higher-educated counterparts or those with other qualifications, with a statistically significant level of 0.048. Consultants also demonstrated the highest percentage of good knowledge (71.4%), adding support to the hypothesis of a possible association between professional status and AI knowledge. These results are indicative of the role of geographical and educational factors in understanding and utilizing AI in diagnostic radiology in Saudi Arabia. Table 4 Factors associated with participants' knowledge and perception of AI in medical imaging. Factor Category Poor n Poor % Good n Good % p-value Region Central Region 8 20.0 32 80.0 0.023* Northern Region 23 51.1 22 48.9 Eastern Region 18 50.0 18 50.0 Western Region 29 38.7 46 61.3 Southern Region 36 46.2 42 53.8 Age (years) 18–28 152 40.6 222 59.4 0.792 29–39 20 42.6 27 57.4 40–50 6 50.0 6 50.0 Gender Male 54 42.9 72 57.1 0.636 Female 124 40.4 183 59.6 Education Bachelor degree 155 41.7 217 58.3 0.048* Resident 2 50.0 2 50.0 Diploma 11 42.3 15 57.7 Specialist 4 40.0 6 60.0 Consultant 6 28.6 15 71.4 Figure 2 is a comparison of categories and percentages based on factors specifically concerning the areas of regions, professional titles, age, and gender in diagnostic radiology. Moreover, the "Good-Region" vs. "Poor-Region" bars help to point out the regional differences, with the Central Region being the highest percentage of good outcomes at 80%. This variation presents an opportunity for AI to resolve regional disparities in diagnostic radiology. AI could improve the quality and efficiency of medical imaging, as well as the quality across regions. AI tools could also help enhance the training and resources of individuals who struggle in certain situations. Furthermore, Table 5 examines the correlation between the level of education, clinical performance, and curriculum for AI in medical imaging. The results present a lovely pattern between more educated individuals (i.e., with more than a university-level means of education) being the ones interested in learning about AI. Specifically, 83.3% of university-level groups are interested in knowing more about AI compared to 70.5% of Higher education-level groups. This difference value has a significant level of 0.04, which is a little higher than the threshold level, i.e., 0.05 of the p-value. This, in turn, could mean that university education is better, as it prepares people to engage with new technologies, such as AI. Furthermore, a significant difference develops in the use of AI in careers, where among the percentages, 62.3% with higher education would have used AI in their professional practices compared with only 36.8% university-educated participants (p-value of 0.001). This may indicate that a higher education can offer greater opportunities for exposure to AI tools and applications in a professional setting. The awareness of AI applications is also correlated with education beyond university, with 70.5% of those with no more than higher education having heard of AI applications in radiology, compared with 53.5% of those who are university-educated (p-value of 0.033). This illustrates the broader impact and methods of AI in professional fields, as well as the role of advanced education in promoting AI literacy and adoption in Saudi Arabia's medical imaging sector. Table 5 Relation between educational level and clinical practice & curriculum about AI in medical imaging. Clinical Practice & Curriculum Category University n University % Above University n Above University % p-value Would like to learn more about AI Yes 310 83.3 43 70.5 0.049* No 31 8.3 9 14.8 Not sure 31 8.3 9 14.8 Have you ever used AI in your career Yes 137 36.8 38 62.3 0.001* No 181 48.7 14 23.0 Not sure 54 14.5 9 14.8 Have heard about AI applications Yes 199 53.5 43 70.5 0.033* No 125 33.6 11 18.0 Not sure 48 12.9 7 11.5 * P-value fulfills the criteria of p < 0.05. Overall, the above study reveals that the medical imaging community of Saudi Arabia is becoming increasingly familiar with AI, and the factors of the region and educational approach have a significant impact on medical imaging knowledge. Participants with a higher level of education and from the Central region report a greater understanding of AI's potential. While there's a general acknowledgment of AI's positive contribution to diagnostic accuracy, there are still AI fears of job security, in particular for radiographers, associated with it. There's a high interest in knowing about introducing AI in clinical settings concurrently, as well as the integration of AI into curricula, as it shows the importance of AI in medical education. The results indicate that while the adoption of AI in the field of radiology is moving forward, there shall be greater education and training required for a satisfactory integration. 4. Discussion The role of AI in improving diagnostic radiology in Saudi Arabia. This article gives valuable insights into the knowledge and perception of AI among diagnostic radiologists. The results show a significant awareness of AI, as more than half (80.6 per cent) of the study's participants say they know some amount of information about the technology. However, there is a divide in an in-depth understanding of the technology itself, for only 63% of the participants were absolutely clear on just what AI meant. This awareness is more notable amongst the younger age group in radiology, with a higher degree of knowledge of 86.4% (young participants, aged 18-28 years, compared to other age groups). Notably, although AI is recognized as a pivotal tool in reducing the dose levels of radiation and enhancing the accuracy of diagnosis when using CT and MRI scans, the issue of job insecurity and fear of errors in machines continues to be a greater challenge. Specifically, 48% of them expressed concerns about AI affecting the security of jobs, while a good majority of them (83.1%) agreed that AI is bringing changes in the healthcare sector. These findings highlight the two-facedness of AI's perception in the medical imaging space - while there is excitement about the possibilities of this technology, there is uncertainty about the implications it will have on employment. Additionally, the study emphasizes the importance of education and training in integrating AI technologies into clinical practice. More than 70% of participants believe AI will help to improve education in medical imaging and their clinical practice. This was reflected in the large number of respondents (81.5%) who said they'd like to understand more about AI. One of the most notable findings is the gap in exposure to AI that exists across different educational levels. University-trained participants were more likely to show interest in AI learning (83.3% compared with 70.5% for those with higher education beyond university), and had more hands-on experience with AI (62.3% versus 36.8%). This implies that educational background is a pivotal factor in promoting awareness of AI Technologies and fostering engagement. In addition, a majority of them (65.4%) believed that AI modules should be used in the current curriculum. Therefore, there should be specific training designed to prepare radiologists for integrating AI in medical imaging. A lack of meaningful AI training in existing educational strategies could be a significant obstacle to greater adoption of AI, as highlighted by the relatively low percentage of participants who have worked with AI in their careers (40.4%). Further analysis of the data reveals regional variances in understanding and acceptance of AI in radiology. Radiologists in the central region have the most knowledge in AI, with 80% of them reporting a good sense of AI. In contrast, those from the northern and eastern areas demonstrated a greater percentage of poor knowledge regarding AI, which suggested the regional disparity in the implementation of AI education and resources. This disparity raises the possibility that so-called targeted interventions and localized training programs may help address these gaps. The results of this work highlight a significant opportunity to integrate AI into radiology training programs, particularly in less developed regions. The need for customized and region-specific approaches in the education of AI, along with continuous professional development opportunities, is the secret to the successful incorporation of AI in diagnostic radiology in Saudi Arabia. As AI continues to evolve, redressing the inequalities in education highlighted throughout this investigation would be instrumental in ensuring that radiologists throughout the country are equipped to maximize the power of AI for the time to come, driving up patient outcomes and the accuracy in medical imaging. 4.1. Comparison Analysis The references discussed below give valuable insights about which state of affairs AI is in the Saudi Arabian Radiology practices at this point of time. Studies such as those conducted by Hamd et al. and Tajaldeen et al. [13–15], and Shafiq et al. [16], all show recognition of the potential impact of AI for the improvement of clinical practices and, in particular, for the improvement of scanning quality and diagnostic accuracy. However, an emerging theme throughout these studies is our knowledge and training deficit and a significant portion of radiology professionals recognizing there has been a lack of understanding of AI applications. For example, Tajaldeen et al. [14] reported that the very low awareness meant that most radiologists do not practice regular use of AI in their practice (82%). Similarly, our study highlights a substantial disparity between theory and practice. Despite being aware of AI concepts, 55.9% of radiologists do not use AI in their day-to-day practice. This gap highlights the need for targeted educational programs, which was also identified by Alelyani et al. [17], regarding a mixed perception by Saudi radiologists about the role of AI in their professional practice in terms of job security and the need to receive training. Our study adds value to these gaps in AI knowledge and training, specifically with our large and diverse sample of 433 radiologists from different regions of Saudi Arabia. Noteworthy is the study data showing regional disparities in AI awareness, with a greater understanding among radiologists in central regions compared to northern areas, which could also explain regional emphasis as observed in the studies by Baghdadi et al. [17] and Aldhafeeri et al. [18] that showed varying levels of AI trust and knowledge in the Saudi population. In addition, the cost of education on knowledge of AI is significant, and there was a significant correlation between education levels and AI awareness. This is crucial for clinical implementation since 71.6% of our participants agree on the usefulness of AI to improve diagnostic outcomes, especially when using CT and MRI scans. Thus, our paper significantly adds a voice to the conversation regarding both the regional disparity and educational needs while providing demographic information on the young age and predominance of females in the medical workforce in Saudi Arabia. 4.2. Limitations The research focusing on the role of AI technology in improving diagnostic radiology in Saudi Arabia exhibits some limitations that should be considered when evaluating the findings of the study. Sampling Bias: The study has adopted a convenience sampling method, which may not be entirely representative of the diverse opinions of all radiologists, residents, and students in all regions of Saudi Arabia. The overrepresentation of those from the Southern and Western regions and the underrepresentation of those from other areas may cause a regional bias, which limits the generalizability of the results. Self-Reported Data: The data on self-administered surveys (online) may suffer from response biases. Participants may give socially desirable responses or fail to correctly interpret the questions, which could introduce inaccuracies in the results, particularly when the topics are subjective (e.g., concerning knowledge and perception of AI). Limited Use of AI in Practice A considerable proportion of respondents (45%) were not using AI in their professional careers. This limitation suggests that the results may be less representative of the actual challenges and opportunities of integrating AI into clinical practice, given that many participants had limited hands-on experience with AI tools. 4.3. Future Research Directions Future studies on the role of AI in enhancing diagnostic radiology in Saudi Arabia should focus on finding solutions for significant challenges, especially regarding regulatory issues, patient privacy concerns, and incorporating AI into the education system. As AI continues to transform medical imaging, more research is necessary to investigate how well AI applications are working in a variety of healthcare settings, including rural and underserved areas. Additionally, research should explore the potential for AI to help reduce disparities in healthcare access and quality. Future research can also be done on the implications of AI on the professional roles of radiologists, including how it will influence the workforce and requirements for ongoing professional development. Exploring ways to collaborate on AI-human decision-making and evaluating the long-term effects of integrating AI on the quality of patient care will be important. Finally, research can focus on developing standardized AI tools and frameworks that can be implemented throughout the healthcare system, thereby aiding Saudi Vision 2030 goals in innovating the Saudi healthcare system. 5. Conclusion In conclusion, the potential benefits that AI offers to the development and improvement of diagnostic radiology in Saudi Arabia are immense and can provide for improved accuracy and efficiency in medical imaging. The study found that a majority of radiologists and trainees have high hopes for the potential of AI to make diagnostic processes faster and more accurate, thereby reducing the risk of human error, which in turn improves the accuracy of disease detection. However, the use of AI is not without its challenges, such as regulatory limitations, the need for specialized training, and concerns about job displacement and machine errors, which must be addressed to harness the potential of AI to its fullest. This research underlines the importance of integrating AI education into radiology training programs in line with the larger objectives of Saudi Arabia's Vision 2030, aimed at modernizing the healthcare system. Radiologists' interest in continuing education confirms the need for a structured set of curricula that can fill the knowledge gap. By addressing these challenges and promoting AI literacy, Saudi Arabia can maximize the advantages of AI, ensuring its effective integration into clinical practice, driving forward healthcare outcomes across the country. Abbreviations Terms Abbreviation Terms Abbreviation AI Artificial intelligence SPSS Statistical Package for the Social Sciences ML Machine Learning DL Deep Learning REC Research Ethics Committee CT Computed Tomography MRI Magnetic Resonance Imaging Declarations Funding: No funding was provided for this study. Ethics Statement: The study had been approved by the Research Ethics Committee (REC) of the University of Hail, Saudi Arabia (Approval No: H-2024-489, Date: 4/11/2024). All participants gave electronic fields informed consent before participating in the survey Author Contribution Author Contributions: All authors made significant contributions to them, the conception, design and writing of the manuscript. Data Collection and analysis were performed by Amirah Fahad Alshammeri, Bashayr Sulaiman Almarzooq, Rana Nizar Raghib, Amal Fahad Aljibreen, Jory Mohammed Saleh Alshammrai, Shahad A. Alafnan, and Faisal Fahad Mohammed Alshammari, Writing and critical revision of the manuscript by Amirah Fahad Alshammeri, Bashayr Sulaiman Almarzooq, Rana Nitzer Raghib, Amal Fahad Aljibreen, Jory Mohammed Saleh Alshammrai, Shahad A. All the authors reviewed and approved the final manuscript. Acknowledgement Acknowledgements: The authors wish to thank the University of Hail for the ethical approval of this study and for the assistance with the research. We also thank all the respondents for their time and contributions to our survey. Special thanks to the University supervisors for assisting us. Data Availability Data Availability: The data for the results presented in this study are available on demand from the corresponding author. References Alghamdi SA, Alashban Y, Alhailiy AB, Alharbi FF, Al-Nahrawi AE. Perceptions of artificial intelligence among computed tomography technologists in Saudi Arabia: Influence of demographics and training on AI adoption. J Radiat Res Appl Sci 2025;18:101355. https://doi.org/10.1016/j.jrras.2025.101355. [Accessible on: 05 September 2025]. Almuhanna A, Almohsen D, AlSultan D, Alhuraish D, AlRatrout F, Alzanadi R, et al. Attitudes and Awareness of Medical Students Toward Teleradiology and the Application of Artificial Intelligence in Diagnostic Radiology: A Cross-Sectional Study. J Med Educ Curric Dev 2025;12. https://doi.org/10.1177/23821205251358005. [Accessible on: 05 September 2025]. AlSharhan S, AlMarzouq W, Alshaikh H, Aljubran H, Alghamdi R, AlQahtani S, et al. Perceptions of Artificial Intelligence Among Otolaryngologists in Saudi Arabia: A Cross-Sectional Study. J Multidiscip Healthc 2024;Volume 17:4101–11. https://doi.org/10.2147/JMDH.S478347. [Accessible on: 06 September 2025]. Aldahery S, Qwader R, Asiri R, Al-Alfy A, Bushara L, Qurashi A, et al. Exploring Interventional Radiology: A Multicentre Study on Saudi Medical and Radiology Technology Students’ Perspectives. Adv Med Educ Pract 2025;Volume 16:749–60. https://doi.org/10.2147/AMEP.S514876. [Accessible on: 06 September 2025]. Allam AH, Eltewacy NK, Alabdallat YJ, Owais TA, Salman S, Ebada MA, et al. Knowledge, attitude, and perception of Arab medical students towards artificial intelligence in medicine and radiology: A multi-national cross-sectional study. Eur Radiol 2023;34:1–14. https://doi.org/10.1007/s00330-023-10509-2. [Accessible on: 06 September 2025]. ALruwail B, Alshalan A, Thirunavukkarasu A, Alibrahim A, Alenezi A, Aldhuwayhi T. Evaluation of Health Science Students’ Knowledge, Attitudes, and Practices Toward Artificial Intelligence in Northern Saudi Arabia: Implications for Curriculum Refinement and Healthcare Delivery. J Multidiscip Healthc 2025;Volume 18:623–35. https://doi.org/10.2147/JMDH.S499902. [Accessible on: 09 September 2025]. Faroog Z, Dirar QSE, Zaidi ARZ, Khan MS, Mahamud G, Ambia SR, et al. Knowledge and attitude of medical students towards artificial intelligence in ophthalmology in Riyadh, Saudi Arabia: a cross-sectional study. Annals of Medicine & Surgery 2024;86:4377–83. https://doi.org/10.1097/MS9.0000000000002238. [Accessible on: 09 September 2025]. Amiri H, Peiravi S, rezazadeh shojaee S sara, Rouhparvarzamin M, Nateghi MN, Etemadi MH, et al. Medical, dental, and nursing students’ attitudes and knowledge towards artificial intelligence: a systematic review and meta-analysis. BMC Med Educ 2024;24:412. https://doi.org/10.1186/s12909-024-05406-1. [Accessible on: 09 September 2025]. Aljehani NM, Al Nawees FE. The current state, challenges, and future directions of artificial intelligence in healthcare in Saudi Arabia: systematic review. Front Artif Intell 2025;8. https://doi.org/10.3389/frai.2025.1518440. [Accessible on: 09 September 2025]. Mohseni A, Ghotbi E, Kazemi F, Shababi A, Jahan SC, Mohseni A, et al. Artificial Intelligence in Radiology. Radiol Clin North Am 2024;62:935–47. https://doi.org/10.1016/j.rcl.2024.03.008. [Accessible on: 09 September 2025]. Hassankhani A, Amoukhteh M, Valizadeh P, Jannatdoust P, Sabeghi P, Gholamrezanezhad A. Radiology as a Specialty in the Era of Artificial Intelligence: A Systematic Review and Meta-analysis on Medical Students, Radiology Trainees, and Radiologists. Acad Radiol 2024;31:306–21. https://doi.org/10.1016/j.acra.2023.05.024. [Accessible on: 10 September 2025]. Alyami AS, Majrashi NA, Shubayr NA. Radiologists’ and Radiographers’ Perspectives on Artificial Intelligence in Medical Imaging in Saudi Arabia. Current Medical Imaging Formerly Current Medical Imaging Reviews 2024;20. https://doi.org/10.2174/0115734056250970231117111810. [Accessible on: 10 September 2025]. Hamd Z, Alorainy A, Aldhahi M, Gareeballah A, F Alsubaie N, A Alshanaiber S, et al. Evaluation of the Impact of Artificial Intelligence on Clinical Practice of Radiology in Saudi Arabia. J Multidiscip Healthc 2024;Volume 17:4745–56. https://doi.org/10.2147/JMDH.S465508. [Accessible on: 11 September 2025]. Tajaldeen A, Alghamdi S. Evaluation of radiologist’s knowledge about the Artificial Intelligence in diagnostic radiology: a survey-based study. Acta Radiol Open 2020;9. https://doi.org/10.1177/2058460120945320. [Accessible on: 11 September 2025]. Baghdadi LR, Mobeirek AA, Alhudaithi DR, Albenmousa FA, Alhadlaq LS, Alaql MS, et al. Patients’ Attitudes Toward the Use of Artificial Intelligence as a Diagnostic Tool in Radiology in Saudi Arabia: Cross-Sectional Study (Preprint) 2023. https://doi.org/10.2196/preprints.53108. [Accessible on: 11 September 2025]. Shafiq P, Mehmood Y, Nisar S, Abdulaziz Alanazi R, Alanazi HA, Alenezi HN, et al. Is Artificial Intelligence a Threat to Radiologists? Perception of Radiologists in Saudi Arabia. Journal of Pioneering Medical Sciences 2025;14:170–7. https://doi.org/10.47310/jpms2025140525. [Accessible on: 11 September 2025]. Alelyani M, Alamri S, Alqahtani MS, Musa A, Almater H, Alqahtani N, et al. Radiology Community Attitude in Saudi Arabia about the Applications of Artificial Intelligence in Radiology. Healthcare 2021;9:834. https://doi.org/10.3390/healthcare9070834. [Accessible on: 11 September 2025]. Aldhafeeri FM. Perspectives of radiographers on the emergence of artificial intelligence in diagnostic imaging in Saudi Arabia. Insights Imaging 2022;13:178. https://doi.org/10.1186/s13244-022-01319-z. [Accessible on: 11 September 2025]. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7724943","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":533768388,"identity":"33b14e91-b3a4-4222-8c68-e856c2c3c998","order_by":0,"name":"Amirah Fahad Alshammeri","email":"","orcid":"","institution":"University of Hail","correspondingAuthor":false,"prefix":"","firstName":"Amirah","middleName":"Fahad","lastName":"Alshammeri","suffix":""},{"id":533768389,"identity":"bd5a21ff-470f-4153-86e5-35b695684c25","order_by":1,"name":"Bashayr Sulaiman Almarzooq","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAUlEQVRIiWNgGAWjYBACexDxAMb7wMCQAKQM8GoxbGCAKAMBxhnEaDE4gKSFmYcoW/oPMH5IzLHLl29vPnjbts0mj4G9eZsEwx87nFrsJRKYJRK3JVs29hxLts5tSytm4DlWJsHYlozbFqDzgVqYDZglcsykc9sOJzYAGRKMDcy4/XL+APOPxG31BmwgLZYgLfJvzIAOq8et5UACG9CWwwY8IC2MYFt4gFrYDuNxWGKbReK24wYSPMeSLXvOpSW28aQVWyS2Hcftff7Dh2983FZtAAqxGz/KbBL72Q9vvPHhTzVOLcD4a4AzJUAEG4hIwKMBBUgQq3AUjIJRMApGFgAARRZO3piYkqMAAAAASUVORK5CYII=","orcid":"","institution":"University of Hail","correspondingAuthor":true,"prefix":"","firstName":"Bashayr","middleName":"Sulaiman","lastName":"Almarzooq","suffix":""},{"id":533768390,"identity":"a7e64f56-e65c-4d73-aab8-c90859decda1","order_by":2,"name":"Rana Nizar Raghib","email":"","orcid":"","institution":"University of Hail","correspondingAuthor":false,"prefix":"","firstName":"Rana","middleName":"Nizar","lastName":"Raghib","suffix":""},{"id":533768391,"identity":"d4551854-fadb-4e5f-ad14-a37cbc3989c1","order_by":3,"name":"Amal Fahad Aljibreen","email":"","orcid":"","institution":"University of Hail","correspondingAuthor":false,"prefix":"","firstName":"Amal","middleName":"Fahad","lastName":"Aljibreen","suffix":""},{"id":533768392,"identity":"cd0e1d1b-2084-475a-9431-2f73973dc9a4","order_by":4,"name":"Jory Mohammed Saleh Alshammrai","email":"","orcid":"","institution":"University of Hail","correspondingAuthor":false,"prefix":"","firstName":"Jory","middleName":"Mohammed Saleh","lastName":"Alshammrai","suffix":""},{"id":533768393,"identity":"3cbfbbb7-dee2-4be6-9dd6-fc31ec9bb7d5","order_by":5,"name":"Shahad A. Alafnan","email":"","orcid":"","institution":"University of Hail","correspondingAuthor":false,"prefix":"","firstName":"Shahad","middleName":"A.","lastName":"Alafnan","suffix":""},{"id":533768394,"identity":"38f2de9d-37d6-4188-96c8-10c72559d126","order_by":6,"name":"Faisal Fahad","email":"","orcid":"","institution":"University of Hail","correspondingAuthor":false,"prefix":"","firstName":"Faisal","middleName":"","lastName":"Fahad","suffix":""}],"badges":[],"createdAt":"2025-09-26 23:09:56","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7724943/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7724943/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":94397670,"identity":"28a31653-1af7-4476-b1d5-92a5ad43adfc","added_by":"auto","created_at":"2025-10-27 13:56:45","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":194203,"visible":true,"origin":"","legend":"","description":"","filename":"updatedfigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-7724943/v1/f8002738edf5cac4b721de16.docx"},{"id":94399455,"identity":"23dd0e3c-ace9-44ac-b2d4-81dd40ddf2ac","added_by":"auto","created_at":"2025-10-27 13:57:34","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":273609,"visible":true,"origin":"","legend":"","description":"","filename":"2RoleofAIinEnhancingDiagnosticsRadiologoyMOA.docx","url":"https://assets-eu.researchsquare.com/files/rs-7724943/v1/04f0620451fc64af9e2ee352.docx"},{"id":94397561,"identity":"2e72b34c-8466-4766-815a-50ad15ee9da7","added_by":"auto","created_at":"2025-10-27 13:56:43","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":25620,"visible":true,"origin":"","legend":"","description":"","filename":"updatedtables.docx","url":"https://assets-eu.researchsquare.com/files/rs-7724943/v1/189873596b9d2d207198bede.docx"},{"id":94397590,"identity":"f1cbfcb7-f921-4677-82c3-f667f300247a","added_by":"auto","created_at":"2025-10-27 13:56:43","extension":"json","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":9500,"visible":true,"origin":"","legend":"","description":"","filename":"624c34232d6b4515b3d48da1e3d8146c.json","url":"https://assets-eu.researchsquare.com/files/rs-7724943/v1/fa602fb40423e4abbc6ffcbd.json"},{"id":94399741,"identity":"c0ab4641-5296-4a9a-98a4-382c7496e445","added_by":"auto","created_at":"2025-10-27 13:57:43","extension":"xml","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":152307,"visible":true,"origin":"","legend":"","description":"","filename":"624c34232d6b4515b3d48da1e3d8146c1enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7724943/v1/712010b72d89733cc7468fbf.xml"},{"id":94399093,"identity":"b0d3e5b5-d46d-4663-92b6-74420580e96d","added_by":"auto","created_at":"2025-10-27 13:57:21","extension":"png","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":40396,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7724943/v1/15b3cc7de6fa1a5cd21111de.png"},{"id":94398396,"identity":"5982006c-f7ca-42bc-8e44-60ab578fca02","added_by":"auto","created_at":"2025-10-27 13:57:04","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":138280,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7724943/v1/0975284b7ec9045867de0756.png"},{"id":94397659,"identity":"4f709485-84f1-49de-81d7-5854fd955ad7","added_by":"auto","created_at":"2025-10-27 13:56:45","extension":"jpeg","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":181047,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7724943/v1/35d9d05ec69613fa89c28523.jpeg"},{"id":94398007,"identity":"f171cea1-1512-48ba-858f-d17969125f69","added_by":"auto","created_at":"2025-10-27 13:56:56","extension":"png","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":138280,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7724943/v1/fa45c0f222ecb67cd4be18ed.png"},{"id":94399714,"identity":"2ca21026-68e5-4101-8adc-f94017b81a0b","added_by":"auto","created_at":"2025-10-27 13:57:42","extension":"png","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":10201,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7724943/v1/ccf3640a430d1792a3800835.png"},{"id":94398581,"identity":"8d9c0e4c-fc1d-4fe9-b89c-227b6579c804","added_by":"auto","created_at":"2025-10-27 13:57:08","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":40247,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7724943/v1/dbe53d556f9c4f656980e237.png"},{"id":94398224,"identity":"33eab95b-f8a5-42e5-b1ca-9be2a87e02f6","added_by":"auto","created_at":"2025-10-27 13:57:01","extension":"png","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":29511,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7724943/v1/31b0e793eed1b20ff0f2acd0.png"},{"id":94398139,"identity":"35f4e087-f7fc-47ed-87f0-1a6ac43d8fca","added_by":"auto","created_at":"2025-10-27 13:57:00","extension":"png","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":40247,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7724943/v1/805136fc611161a6c2adfc0c.png"},{"id":94396919,"identity":"d5c83d42-3176-4e97-8b9b-3fa4006ddc38","added_by":"auto","created_at":"2025-10-27 13:56:21","extension":"xml","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":149254,"visible":true,"origin":"","legend":"","description":"","filename":"624c34232d6b4515b3d48da1e3d8146c1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7724943/v1/50cb8f4c4e25d11f817f9170.xml"},{"id":94397842,"identity":"582b1e50-b81b-4b75-b83e-c5784eedde54","added_by":"auto","created_at":"2025-10-27 13:56:51","extension":"html","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":157542,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7724943/v1/628506d6cfe845fb151f3115.html"},{"id":94398089,"identity":"4e95e2d2-9f57-431f-a4d3-154e9230b985","added_by":"auto","created_at":"2025-10-27 13:56:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":75380,"visible":true,"origin":"","legend":"\u003cp\u003eAppropriate way to develop algorithms by AI as reported by the study participants.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7724943/v1/0fbbb5031aa998c589b30a21.png"},{"id":94397981,"identity":"560f2ec3-a5c8-4f33-a7e9-86f652ab80d8","added_by":"auto","created_at":"2025-10-27 13:56:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":253477,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of Categories and Percentages by Factor: Regional and Demographic Variations in Diagnostic Radiology Performance in Saudi Arabia.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7724943/v1/e56e1239ec7110d77cc390dd.png"},{"id":96711598,"identity":"cf1bfaca-5ff7-4f71-9050-337ed27ca579","added_by":"auto","created_at":"2025-11-25 10:12:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1283684,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7724943/v1/52be3f7e-48d4-4f64-a95e-6f36e9b55302.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Role of Artificial Intelligence in Enhancing Diagnostic Radiology: Applications and Advancements in Medical Imaging in Saudi Arabia","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eArtificial intelligence (AI) is swiftly changing a variety of industries, and understanding the growing importance of using AI in healthcare, namely diagnostic radiology, is among them [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The science of artificial intelligence aims to develop computer systems capable of performing tasks typically handled by humans, including speech recognition, decision-making, and language translation. From the point of view of diagnostic radiology, for working with physicians to read medical images, the AI may allow for becoming more efficient and more accurate for medical diagnoses [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The use of AI in medical imaging has experienced significant exponential growth over the last few years, driven by advancements in AI, machine learning algorithms, and deep learning. This technological shift enables the application of AI to aid in the analysis of complicated medical images, thereby assisting in a better and quicker diagnosis [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe integration of AI in diagnostic radiology offers several advantages, ranging from improved decision-making to enhanced patient care. The ability of AI to crunch numbers and identify patterns from medical images has the potential to help speed up early diagnosis and the specific treatment strategy [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Recent studies have demonstrated that AI applications, such as machine learning algorithms, can analyze medical images to detect abnormalities with higher sensitivity and accuracy than human radiologists [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. For example, the time involved in examining an image is significantly reduced with the help of AI, which aids in faster diagnosis and treatment processes. In addition, the ability of AI to minimize radiation exposure from unnecessary scans is another key advantage, further enhanced by potential improvements in patient safety and cost-effectiveness [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. These advancements have witnessed AI form a pillar in the continuous advancement of medical imaging, where it offers tools to augment the capabilities of diagnostic radiologists.\u003c/p\u003e\u003cp\u003eIn Saudi Arabia, the adoption of AI in Healthcare, more especially in medical examination radiology, has achieved impressive development in recent years. The Saudi healthcare sector has been making significant strides towards the integration of AI technologies to enhance patient outcomes and streamline healthcare delivery. Recent reports suggest that AI-based systems are being introduced in Radiology departments across the country to support the radiology diagnostic workflow, aid in image interpretation, and improve overall clinical decision-making [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, the implementation of AI is not without challenges. Despite the enormous potential, there have been several issues with the use of AI in medical imaging, including image distortion, resource constraints, and regulatory barriers, which have hindered the widespread adoption of AI in medical imaging. Even though this vast potential for medical imaging is enormous, one of the issues that arises with the use of AI is the issue of image distortion, a resource constraint, and a regulatory constraint, which has been the bane of the large-scale use of AI in medical imaging [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. These challenges highlight the need for continued research and development in the field to ensure that the integration of AI into clinical practice is effective and safe.\u003c/p\u003e\u003cp\u003eThis study aims to determine the level of knowledge and understanding among diagnostic radiologists in Saudi Arabia regarding AI and its application in medical imaging practices. It will enhance the concept of training and education on AI for radiologists, and also provide an opportunity to apply it to diagnostic radiology. By looking at what currently exists with the adoption of AIs in the country, the study aims to identify the potential areas to further integrate AI in Radiology training programs. The findings will give insights on the contributions of AI towards the enhancement of the diagnostic radiology practices in Saudi Arabia. And, it will add to the ever-increasing body of knowledge in this area. This research is very important in understanding how AI can improve the diagnosis process and reshape the future of radiology science in Saudi Arabia.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1. Study Design and Participants\u003c/h2\u003e\n \u003cp\u003eThis study used a cross-sectional online survey methodology to determine the knowledge and attitudes of radiologists, residents, and seniors in the field of radiology in Saudi Arabia regarding AI in medical imaging. The survey, conducted in September 2024, targeted professionals and students from five regions of Saudi Arabia: Central, Northern, Eastern, Western, and Southern. A convenience sampling method was used for recruiting people from different sources, including radiological societies, professors of these activities, and social networks. Participation in the study was voluntary, and submission was based on availability. A total of 500 people were invited to complete the survey with a total sample size of 320 people who were able to complete the survey.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2. Survey Instrument and Data Collection\u003c/h2\u003e\n \u003cp\u003eThe instrument for collecting data was a structured and self-administered questionnaire written in English. The questionnaire was divided into four sections.\u003c/p\u003e\u003cspan\u003e\n \u003col style=\"list-style-type: lower-roman;\"\u003e\n \u003cli\u003eDemographic Data: This section gathered data on the participants\u0026apos; region, age, gender, and educational level.\u003c/li\u003e\n \u003cli\u003eGeneral AI Knowledge: Consisting of five items, this section measured the fundamental understanding and perceptions about artificial intelligence in the participants.\u003c/li\u003e\n \u003cli\u003eAI in Medical Imaging Knowledge: A more specific set of questions, totaling ten items, assessed knowledge about applying AI in radiology, such as lowering radiation doses and identifying pathology.\u003c/li\u003e\n \u003cli\u003eClinical Practice and Curriculum: This section included seven items that addressed opinions around participants \u0026apos; views towards AI education and its practical application in radiology curricula.\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/span\u003e\n \u003cp\u003eThe survey was distributed electronically by using Google Health. The participants were assured of their anonymity, and responses were gathered in September 2024. The survey was intended to be easy to complete (mean completion time 10\u0026ndash;15 minutes). Ethical considerations called for no personal identifying information to be collected. The participants were informed that their answers were only for research purposes.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3. Data Analysis\u003c/h2\u003e\n \u003cp\u003eThe collected data were analyzed with IBM (SPSS) Statistics version 26. Demographic characteristics and the responses of participants were summarized with frequencies and percentages using descriptive statistics. In the Electronics and Information Sciences Lab, students were tested on topics related to chemical or physical changes to help determine their general knowledge. To analyze the overall understanding of participants, a knowledge score was created by summing the number of correct responses (1 point for each correct response). A score of 60% and above was categorized as \u0026apos;good knowledge, and scores below 60% were considered to be \u0026apos;poor knowledge.\u0026apos; Chi-square test was used to analyze associations between demographic variables and levels of knowledge. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant, indicating a meaningful relationship between the variables.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4. Human Ethics and Consent to Participate\u003c/h2\u003e\n \u003cp\u003eThis study received approval from the Research Ethics Committee (REC) of the University of Hail, Saudi Arabia (Approval No: H-2024-489, Date: 4/11/2024). All participants gave electronically initiated informed consent before participating in a survey. They were told of the study\u0026apos;s purpose, confidentiality, and their right to withdraw from the study at any time and without consequences. The research study was conducted in accordance with ethical guidelines for conducting research with human participants. It made allowance for maximum regard for confidentiality, with respect to data, at all times.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results and Analysis","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Participants' Characteristics\u003c/h2\u003e\u003cp\u003eThe demographic characteristics of the 433 study participants are explained in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and serve as an essential context for the perception analysis of AI usage in the diagnostic radiology field in Saudi Arabia. The survey initially was addressed to a whole population of nearly 500 respondents, with the effective participation comprising 433, which can be seen as a good effective population participation rate. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows that the Southern and Western region respondents accounted for the highest percentages of respondents, 28.5 percent and 27.4 percent, respectively, while the respondents in the Central (14.6%), Northern (16.4%), and Eastern regions (13.1%) had also low percentages of respondents. This indicates regional disparities, which may represent regional differences in the accessibility of healthcare infrastructure and exposure to advanced imaging technologies, both of which are important to the uptake of AI-driven diagnostic systems.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePersonal characteristics of the study participants (n\u0026thinsp;=\u0026thinsp;433).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003en\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCentral Region\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNorthern Region\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEastern Region\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWestern Region\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e27.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSouthern Region\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e28.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18\u0026ndash;28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e374\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e86.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29\u0026ndash;39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40\u0026ndash;50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e29.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e307\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e70.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBachelor degree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e372\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e85.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eResident\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDiploma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSpecialist\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eConsultant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eMoreover, from this table, we also see that there was a higher proportion of female respondents (70.9%) as compared to male respondents (29.1%). This distribution is consistent with trends in Saudi Arabia, where females have an increasing presence in medical education and the health sciences over the last few years. The predominance of female voices in this study highlights the significant role they play in influencing perceptions about integrating AI into radiological practice. It helps ensure that, as well as the views of emerging female healthcare professionals, those of established female healthcare professionals are also taken into account when studying the readiness for technological adoption.\u003c/p\u003e\u003cp\u003eFurthermore, it shows that most of the participants are bachelors (have a bachelor's degree, 85.9%), and smaller proportions of diplomates (6.0%), residents (0.9%), specialists (2.3%), and consultants (4.8%). These results, coupled with the age distribution, which indicated that 86.4% of them were between 18 and 28 years of age, suggest that the sample population consisted mainly of early-career professionals or trainees. This group may have less clinical experience, but shows greater adaptability and receptiveness to innovation, and is therefore likely to be an essential driver regarding future implementation of AI in diagnostic radiology. By contrast, the relative lack of consultants and specialists reflects that the perspectives of senior practitioners (those with the most familiarity with clinical workflows) were less prominent in this data set.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Knowledge and Perception of AI\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the outcome of a survey that was conducted with 433 individuals about their knowledge and perceptions of AI in medical imaging. The responses indicate a general understanding of the relevance and influence of AI in the healthcare sector. A significant result of 63% of the participants said they gained knowledge of what AI is in whole, and there are 80.6% of participants said that they possess general knowledge about AI. This denotes a high level of familiarity with AI, which is essential to grasp AI's potential to serve in medical imaging. Moreover, a large majority of 83.1% of them recognized AI as a game-changer technology in the health industry, representing a general acceptance of the growing role played by AI in medical practices.\u003c/p\u003e\u003cp\u003eWhen zooming in on certain aspects of the perceptions surrounding AI in medical imaging, results show that while a majority of the participants feel the impact of AI in medical imaging is positive, there's significant concern around implications for the workforce. For example, 48% of respondents believe that AI will be a threat when it comes to job security, and 45% worry that AI could be a replacement for radiographers. However, despite such apprehensions, a high percentage (82.2%) agrees that AI would have a positive effect on medical imaging. This presents a contrast between the benefits of AI, including reduced radiation dose levels (72.1%) and increased accuracy in pathology detection (71.6%), and the concerns about its impact on employment in the healthcare sector.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eStudy participants' knowledge and perception of artificial intelligence in medical imaging (n\u0026thinsp;=\u0026thinsp;433).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKnowledge Domain\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes n\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes %\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo n\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNo %\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNot sure n\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNot sure %\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDo you know the full meaning of AI?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e273\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e63.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e115\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e26.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDo you have any knowledge about AI in general?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e349\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e80.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e12.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDo you think AI is a bad technology?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e306\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e70.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e18.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDo you think AI poses a threat to job security?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e208\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e48.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e30.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e21.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDo you think AI is bringing changes to the health sector?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e360\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e83.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e11.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAI incorporated into current imaging modalities\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e291\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e67.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e109\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e25.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAI helps reduce radiation dose levels\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e312\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e72.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e20.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAI plays a role in patient positioning\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e258\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e59.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e20.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e20.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAI helps detect pathologies in CT and MRI scans\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e310\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e71.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e20.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAI increased the accuracy in chest pathology identification\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e298\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e68.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e104\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e24.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAwareness AI is an emerging trend in medical imaging\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e315\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e72.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e12.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e14.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAware AI is emerging in Hail\u0026rsquo;s radiography sector\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e184\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e42.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e124\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e28.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e28.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAI would have a positive impact on medical imaging\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e356\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e82.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e9.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConcern that AI will displace radiographers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e195\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e45.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e164\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e37.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e17.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAcknowledge the possibility of machine errors with AI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e331\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e76.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e17.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAdditionally, when it comes to implementing AI into existing imaging techniques, we see a high level of faith in AI's capabilities among the data. For example, 67.2% of the respondents recognized the integration of AI in modern imaging processes, and 71.6% of the respondents\u0026rsquo; thought AI has a substantial role in Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) scan pathology detection. However, a significant number of participants were unsure about AI's full potential, including 25.2% who were uncertain about AI's role in current imaging and 17.3% who acknowledged the possibility of machine error with AI. These findings indicate that although AI's benefits are widely accepted, further education and training are needed to address doubts and boost medical professionals' confidence in integrating AI into their daily clinical practice, particularly in Saudi Arabia's rapidly evolving radiology sector.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Clinical Practice, Education, and Training\u003c/h2\u003e\u003cp\u003eThe research results obtained from the data in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e reflect the views held by the participants on the aspect of the integration of AI in clinical practices and medical imaging fields in Saudi Arabia for educational curricula. A good proportion of respondents (71.6%) have thought that AI can also improve medical imaging education, and 66.1% admit that clinical practice helps them understand the role of A.I. Similarly, a majority of 71.6% agreed that clinical practice plays a role in mounting knowledge about the applications of AI, which implies that the exposure to practical diagnosis scenarios is an essential factor that creates awareness of the potential of AI applications. Despite these positive findings, almost one-fifth (19\u0026ndash;23%) of the participants expressed uncertainty about these benefits, suggesting a need for more organized training and awareness Programmes, at least for students and practitioners.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eClinical practice and curriculum for AI role in medical imaging among study participants (n\u0026thinsp;=\u0026thinsp;433).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClinical Practice \u0026amp; Curriculum\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes n\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes %\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo n\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNo %\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNot sure n\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNot sure %\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBelieve AI would improve education in medical imaging\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e310\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e71.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e19.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClinical practice helps appreciate the AI role\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e286\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e66.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e23.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClinical practice increases AI knowledge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e310\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e71.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e19.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent curriculum should incorporate AI modules\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e283\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e65.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e15.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e19.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWould like to learn more about AI prospects\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e353\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e81.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e9.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHave ever used AI in your career\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e175\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e40.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e195\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e45.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e14.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHave heard about AI applications in radiology\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e242\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e55.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e136\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e31.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e12.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn addition, the results show a high rate of enthusiasm towards the official curriculum integration of AI. Also, about 65.4% of the respondents approved the integration of AI modules in medical education, and 81.5% of them agreed to learn more about the AI prospects in radiology. These findings reflect the demands of artificial intelligence for healthcare professionals in applying training related to medical imaging. However, 15.5% disagreed with curricular integration, and slightly less than 20% were unsure about the immediate relevance of AI to clinical training, which reflects a lack of clarity regarding the immediate relevance of AI in clinical training. Addressing this through the reform of the curriculum and stand-alone workshops in the medical institutions in Saudi Arabia could potentially be the path forward for aligning the education of medicine with the ongoing development of digital healthcare reform in Saudi Arabia under Vision 2030.\u003c/p\u003e\u003cp\u003eInterestingly, the survey has also shown a divide in the actual use of AI within the careers of the people who took the survey. Only 40.4% of respondents have taken advantage of AI in their professional practice, while 45% have not, and 14.5% don't know. This discrepancy highlights the challenges faced in adopting AI technology in clinical settings, which might be attributed to factors such as limited access to AI technology tools, inadequate training, or skepticism about the effectiveness of AI. Furthermore, while a large percentage of participants have heard of applications of AI in radiology (i.e., 55.9%), there is still a significant amount (44.1%) that have either not heard of or are uncertain about these applications. These findings indicate conspicuous awareness and interest in AI; however, there are still gaps in AI practical implementation knowledge in the field of medical imaging in Saudi Arabia.\u003c/p\u003e\u003cp\u003eFurther, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the algorithm development methods taken into account by participants in the study (\"Machine Learning\" and \"Deep Learning\" being the most frequently used approaches).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBoth approaches explain over 30% of the answers, part of the consideration of the increased relevance of AI in the diagnostic radiology field. \"Basic Learning\" is close behind, indicating that some traditional methods are still in use. \"Other\" methods are the least reported, and this may suggest that less common techniques lack application in the context of radiology. This distribution demonstrates the growing integration of advanced AI methods, such as machine learning and deep learning techniques, in radiology, in keeping with the ever-increasing need for AI-inspired diagnostic enhancements in Saudi Arabia.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.4. Overall Knowledge Level and Associated Factors\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the correlation between several factors and the results of participants' knowledge and AI about medical imaging characteristics. The results indicate a significant effect of geographical location on the degree of understanding of AI's essence, with people from the central region demonstrating the highest level of good knowledge (80%), followed by other regions. In contrast, in the Northern, Eastern, and Southern regions of the country, and among people recruited from these areas, the percentage with good knowledge is lower, with the last one being noted as 53.8%. Note that the p-value of 0.023 indicates the region is a significant factor in AI knowledge, suggesting potential regional differences in AI resource availability or training. The table also shows that while age and gender don't have any significant impact on AI knowledge (p-values of 0.792 and 0.636, respectively), education level has a considerable impact. Notably, those with a Bachelor's degree are better positioned regarding knowledge (58.3%) compared to their higher-educated counterparts or those with other qualifications, with a statistically significant level of 0.048. Consultants also demonstrated the highest percentage of good knowledge (71.4%), adding support to the hypothesis of a possible association between professional status and AI knowledge. These results are indicative of the role of geographical and educational factors in understanding and utilizing AI in diagnostic radiology in Saudi Arabia.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFactors associated with participants' knowledge and perception of AI in medical imaging.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFactor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePoor n\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePoor %\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGood n\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGood %\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eRegion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCentral Region\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e80.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e0.023*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNorthern Region\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e51.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e48.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEastern Region\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e50.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e50.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWestern Region\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e38.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e61.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSouthern Region\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e46.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e53.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18\u0026ndash;28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e40.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e222\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e59.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.792\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29\u0026ndash;39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e42.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e57.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40\u0026ndash;50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e50.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e50.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e42.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e57.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.636\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e124\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e40.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e183\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e59.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBachelor degree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e155\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e41.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e217\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e58.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e0.048*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eResident\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e50.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e50.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDiploma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e42.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e57.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSpecialist\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e40.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e60.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eConsultant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e28.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e71.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e is a comparison of categories and percentages based on factors specifically concerning the areas of regions, professional titles, age, and gender in diagnostic radiology.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eMoreover, the \"Good-Region\" vs. \"Poor-Region\" bars help to point out the regional differences, with the Central Region being the highest percentage of good outcomes at 80%. This variation presents an opportunity for AI to resolve regional disparities in diagnostic radiology. AI could improve the quality and efficiency of medical imaging, as well as the quality across regions. AI tools could also help enhance the training and resources of individuals who struggle in certain situations.\u003c/p\u003e\u003cp\u003eFurthermore, Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e examines the correlation between the level of education, clinical performance, and curriculum for AI in medical imaging. The results present a lovely pattern between more educated individuals (i.e., with more than a university-level means of education) being the ones interested in learning about AI. Specifically, 83.3% of university-level groups are interested in knowing more about AI compared to 70.5% of Higher education-level groups. This difference value has a significant level of 0.04, which is a little higher than the threshold level, i.e., 0.05 of the p-value. This, in turn, could mean that university education is better, as it prepares people to engage with new technologies, such as AI. Furthermore, a significant difference develops in the use of AI in careers, where among the percentages, 62.3% with higher education would have used AI in their professional practices compared with only 36.8% university-educated participants (p-value of 0.001). This may indicate that a higher education can offer greater opportunities for exposure to AI tools and applications in a professional setting. The awareness of AI applications is also correlated with education beyond university, with 70.5% of those with no more than higher education having heard of AI applications in radiology, compared with 53.5% of those who are university-educated (p-value of 0.033). This illustrates the broader impact and methods of AI in professional fields, as well as the role of advanced education in promoting AI literacy and adoption in Saudi Arabia's medical imaging sector.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRelation between educational level and clinical practice \u0026amp; curriculum about AI in medical imaging.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClinical Practice \u0026amp; Curriculum\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUniversity n\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUniversity %\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAbove University n\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAbove University %\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eWould like to learn more about AI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e310\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e83.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e70.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.049*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e14.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNot sure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e14.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eHave you ever used AI in your career\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e137\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e36.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e62.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e181\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e48.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e23.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNot sure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e14.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eHave heard about AI applications\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e199\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e53.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e70.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.033*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e33.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e18.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNot sure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e11.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e* P-value fulfills the criteria of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eOverall, the above study reveals that the medical imaging community of Saudi Arabia is becoming increasingly familiar with AI, and the factors of the region and educational approach have a significant impact on medical imaging knowledge. Participants with a higher level of education and from the Central region report a greater understanding of AI's potential. While there's a general acknowledgment of AI's positive contribution to diagnostic accuracy, there are still AI fears of job security, in particular for radiographers, associated with it. There's a high interest in knowing about introducing AI in clinical settings concurrently, as well as the integration of AI into curricula, as it shows the importance of AI in medical education. The results indicate that while the adoption of AI in the field of radiology is moving forward, there shall be greater education and training required for a satisfactory integration.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe role of AI in improving diagnostic radiology in Saudi Arabia. This article gives valuable insights into the knowledge and perception of AI among diagnostic radiologists. The results show a significant awareness of AI, as more than half (80.6 per cent) of the study\u0026apos;s participants say they know some amount of information about the technology. However, there is a divide in an in-depth understanding of the technology itself, for only 63% of the participants were absolutely clear on just what AI meant. This awareness is more notable amongst the younger age group in radiology, with a higher degree of knowledge of 86.4% (young participants, aged 18-28 years, compared to other age groups). Notably, although AI is recognized as a pivotal tool in reducing the dose levels of radiation and enhancing the accuracy of diagnosis when using CT and MRI scans, the issue of job insecurity and fear of errors in machines continues to be a greater challenge. Specifically, 48% of them expressed concerns about AI affecting the security of jobs, while a good majority of them (83.1%) agreed that AI is bringing changes in the healthcare sector. These findings highlight the two-facedness of AI\u0026apos;s perception in the medical imaging space - while there is excitement about the possibilities of this technology, there is uncertainty about the implications it will have on employment.\u003c/p\u003e\n\u003cp\u003eAdditionally, the study emphasizes the importance of education and training in integrating AI technologies into clinical practice. More than 70% of participants believe AI will help to improve education in medical imaging and their clinical practice. This was reflected in the large number of respondents (81.5%) who said they\u0026apos;d like to understand more about AI. One of the most notable findings is the gap in exposure to AI that exists across different educational levels. University-trained participants were more likely to show interest in AI learning (83.3% compared with 70.5% for those with higher education beyond university), and had more hands-on experience with AI (62.3% versus 36.8%). This implies that educational background is a pivotal factor in promoting awareness of AI Technologies and fostering engagement. In addition, a majority of them (65.4%) believed that AI modules should be used in the current curriculum. Therefore, there should be specific training designed to prepare radiologists for integrating AI in medical imaging. A lack of meaningful AI training in existing educational strategies could be a significant obstacle to greater adoption of AI, as highlighted by the relatively low percentage of participants who have worked with AI in their careers (40.4%).\u003c/p\u003e\n\u003cp\u003eFurther analysis of the data reveals regional variances in understanding and acceptance of AI in radiology. Radiologists in the central region have the most knowledge in AI, with 80% of them reporting a good sense of AI. In contrast, those from the northern and eastern areas demonstrated a greater percentage of poor knowledge regarding AI, which suggested the regional disparity in the implementation of AI education and resources. This disparity raises the possibility that so-called targeted interventions and localized training programs may help address these gaps. The results of this work highlight a significant opportunity to integrate AI into radiology training programs, particularly in less developed regions. The need for customized and region-specific approaches in the education of AI, along with continuous professional development opportunities, is the secret to the successful incorporation of AI in diagnostic radiology in Saudi Arabia. As AI continues to evolve, redressing the inequalities in education highlighted throughout this investigation would be instrumental in ensuring that radiologists throughout the country are equipped to maximize the power of AI for the time to come, driving up patient outcomes and the accuracy in medical imaging.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.1. Comparison Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe references discussed below give valuable insights about which state of affairs AI is in the Saudi Arabian Radiology practices at this point of time. Studies such as those conducted by Hamd et al. and Tajaldeen et al. [13\u0026ndash;15], and Shafiq et al. [16], all show recognition of the potential impact of AI for the improvement of clinical practices and, in particular, for the improvement of scanning quality and diagnostic accuracy. However, an emerging theme throughout these studies is our knowledge and training deficit and a significant portion of radiology professionals recognizing there has been a lack of understanding of AI applications. For example, Tajaldeen et al. [14] reported that the very low awareness meant that most radiologists do not practice regular use of AI in their practice (82%). Similarly, our study highlights a substantial disparity between theory and practice. Despite being aware of AI concepts, 55.9% of radiologists do not use AI in their day-to-day practice. This gap highlights the need for targeted educational programs, which was also identified by Alelyani et al. [17], regarding a mixed perception by Saudi radiologists about the role of AI in their professional practice in terms of job security and the need to receive training.\u003c/p\u003e\n\u003cp\u003eOur study adds value to these gaps in AI knowledge and training, specifically with our large and diverse sample of 433 radiologists from different regions of Saudi Arabia. Noteworthy is the study data showing regional disparities in AI awareness, with a greater understanding among radiologists in central regions compared to northern areas, which could also explain regional emphasis as observed in the studies by Baghdadi et al. [17] and Aldhafeeri et al. [18] that showed varying levels of AI trust and knowledge in the Saudi population. In addition, the cost of education on knowledge of AI is significant, and there was a significant correlation between education levels and AI awareness. This is crucial for clinical implementation since 71.6% of our participants agree on the usefulness of AI to improve diagnostic outcomes, especially when using CT and MRI scans. Thus, our paper significantly adds a voice to the conversation regarding both the regional disparity and educational needs while providing demographic information on the young age and predominance of females in the medical workforce in Saudi Arabia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2. Limitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research focusing on the role of AI technology in improving diagnostic radiology in Saudi Arabia exhibits some limitations that should be considered when evaluating the findings of the study.\u003c/p\u003e\n\u003col style=\"list-style-type: lower-roman;\"\u003e\n \u003cli\u003eSampling Bias: The study has adopted a convenience sampling method, which may not be entirely representative of the diverse opinions of all radiologists, residents, and students in all regions of Saudi Arabia. The overrepresentation of those from the Southern and Western regions and the underrepresentation of those from other areas may cause a regional bias, which limits the generalizability of the results.\u003c/li\u003e\n \u003cli\u003eSelf-Reported Data: The data on self-administered surveys (online) may suffer from response biases. Participants may give socially desirable responses or fail to correctly interpret the questions, which could introduce inaccuracies in the results, particularly when the topics are subjective (e.g., concerning knowledge and perception of AI).\u003c/li\u003e\n \u003cli\u003eLimited Use of AI in Practice A considerable proportion of respondents (45%) were not using AI in their professional careers. This limitation suggests that the results may be less representative of the actual challenges and opportunities of integrating AI into clinical practice, given that many participants had limited hands-on experience with AI tools.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003e4.3. Future Research Directions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFuture studies on the role of AI in enhancing diagnostic radiology in Saudi Arabia should focus on finding solutions for significant challenges, especially regarding regulatory issues, patient privacy concerns, and incorporating AI into the education system. As AI continues to transform medical imaging, more research is necessary to investigate how well AI applications are working in a variety of healthcare settings, including rural and underserved areas. Additionally, research should explore the potential for AI to help reduce disparities in healthcare access and quality. Future research can also be done on the implications of AI on the professional roles of radiologists, including how it will influence the workforce and requirements for ongoing professional development. Exploring ways to collaborate on AI-human decision-making and evaluating the long-term effects of integrating AI on the quality of patient care will be important. Finally, research can focus on developing standardized AI tools and frameworks that can be implemented throughout the healthcare system, thereby aiding Saudi Vision 2030 goals in innovating the Saudi healthcare system.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn conclusion, the potential benefits that AI offers to the development and improvement of diagnostic radiology in Saudi Arabia are immense and can provide for improved accuracy and efficiency in medical imaging. The study found that a majority of radiologists and trainees have high hopes for the potential of AI to make diagnostic processes faster and more accurate, thereby reducing the risk of human error, which in turn improves the accuracy of disease detection. However, the use of AI is not without its challenges, such as regulatory limitations, the need for specialized training, and concerns about job displacement and machine errors, which must be addressed to harness the potential of AI to its fullest.\u003c/p\u003e\u003cp\u003eThis research underlines the importance of integrating AI education into radiology training programs in line with the larger objectives of Saudi Arabia's Vision 2030, aimed at modernizing the healthcare system. Radiologists' interest in continuing education confirms the need for a structured set of curricula that can fill the knowledge gap. By addressing these challenges and promoting AI literacy, Saudi Arabia can maximize the advantages of AI, ensuring its effective integration into clinical practice, driving forward healthcare outcomes across the country.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTerms\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTerms\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eArtificial intelligence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eSPSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eStatistical Package for the Social Sciences\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eML\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eMachine Learning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eDeep Learning\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eREC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eResearch Ethics Committee\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eComputed Tomography\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eMRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003eMagnetic Resonance Imaging\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e No funding was provided for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Statement:\u003c/strong\u003e The study had been approved by the Research Ethics Committee (REC) of the University of Hail, Saudi Arabia (Approval No: H-2024-489, Date: 4/11/2024). All participants gave electronic fields informed consent before participating in the survey\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthor Contributions: All authors made significant contributions to them, the conception, design and writing of the manuscript. Data Collection and analysis were performed by Amirah Fahad Alshammeri, Bashayr Sulaiman Almarzooq, Rana Nizar Raghib, Amal Fahad Aljibreen, Jory Mohammed Saleh Alshammrai, Shahad A. Alafnan, and Faisal Fahad Mohammed Alshammari, Writing and critical revision of the manuscript by Amirah Fahad Alshammeri, Bashayr Sulaiman Almarzooq, Rana Nitzer Raghib, Amal Fahad Aljibreen, Jory Mohammed Saleh Alshammrai, Shahad A. All the authors reviewed and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eAcknowledgements: The authors wish to thank the University of Hail for the ethical approval of this study and for the assistance with the research. We also thank all the respondents for their time and contributions to our survey. Special thanks to the University supervisors for assisting us.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData Availability: The data for the results presented in this study are available on demand from the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlghamdi SA, Alashban Y, Alhailiy AB, Alharbi FF, Al-Nahrawi AE. Perceptions of artificial intelligence among computed tomography technologists in Saudi Arabia: Influence of demographics and training on AI adoption. J Radiat Res Appl Sci 2025;18:101355. https://doi.org/10.1016/j.jrras.2025.101355. [Accessible on: 05 September 2025].\u003c/li\u003e\n\u003cli\u003eAlmuhanna A, Almohsen D, AlSultan D, Alhuraish D, AlRatrout F, Alzanadi R, et al. Attitudes and Awareness of Medical Students Toward Teleradiology and the Application of Artificial Intelligence in Diagnostic Radiology: A Cross-Sectional Study. J Med Educ Curric Dev 2025;12. https://doi.org/10.1177/23821205251358005. [Accessible on: 05 September 2025].\u003c/li\u003e\n\u003cli\u003eAlSharhan S, AlMarzouq W, Alshaikh H, Aljubran H, Alghamdi R, AlQahtani S, et al. Perceptions of Artificial Intelligence Among Otolaryngologists in Saudi Arabia: A Cross-Sectional Study. J Multidiscip Healthc 2024;Volume 17:4101\u0026ndash;11. https://doi.org/10.2147/JMDH.S478347. [Accessible on: 06 September 2025].\u003c/li\u003e\n\u003cli\u003eAldahery S, Qwader R, Asiri R, Al-Alfy A, Bushara L, Qurashi A, et al. Exploring Interventional Radiology: A Multicentre Study on Saudi Medical and Radiology Technology Students\u0026rsquo; Perspectives. Adv Med Educ Pract 2025;Volume 16:749\u0026ndash;60. https://doi.org/10.2147/AMEP.S514876. [Accessible on: 06 September 2025].\u003c/li\u003e\n\u003cli\u003eAllam AH, Eltewacy NK, Alabdallat YJ, Owais TA, Salman S, Ebada MA, et al. Knowledge, attitude, and perception of Arab medical students towards artificial intelligence in medicine and radiology: A multi-national cross-sectional study. Eur Radiol 2023;34:1\u0026ndash;14. https://doi.org/10.1007/s00330-023-10509-2. [Accessible on: 06 September 2025].\u003c/li\u003e\n\u003cli\u003eALruwail B, Alshalan A, Thirunavukkarasu A, Alibrahim A, Alenezi A, Aldhuwayhi T. Evaluation of Health Science Students\u0026rsquo; Knowledge, Attitudes, and Practices Toward Artificial Intelligence in Northern Saudi Arabia: Implications for Curriculum Refinement and Healthcare Delivery. J Multidiscip Healthc 2025;Volume 18:623\u0026ndash;35. https://doi.org/10.2147/JMDH.S499902. [Accessible on: 09 September 2025].\u003c/li\u003e\n\u003cli\u003eFaroog Z, Dirar QSE, Zaidi ARZ, Khan MS, Mahamud G, Ambia SR, et al. Knowledge and attitude of medical students towards artificial intelligence in ophthalmology in Riyadh, Saudi Arabia: a cross-sectional study. Annals of Medicine \u0026amp; Surgery 2024;86:4377\u0026ndash;83. https://doi.org/10.1097/MS9.0000000000002238. [Accessible on: 09 September 2025].\u003c/li\u003e\n\u003cli\u003eAmiri H, Peiravi S, rezazadeh shojaee S sara, Rouhparvarzamin M, Nateghi MN, Etemadi MH, et al. Medical, dental, and nursing students\u0026rsquo; attitudes and knowledge towards artificial intelligence: a systematic review and meta-analysis. BMC Med Educ 2024;24:412. https://doi.org/10.1186/s12909-024-05406-1. [Accessible on: 09 September 2025].\u003c/li\u003e\n\u003cli\u003eAljehani NM, Al Nawees FE. The current state, challenges, and future directions of artificial intelligence in healthcare in Saudi Arabia: systematic review. Front Artif Intell 2025;8. https://doi.org/10.3389/frai.2025.1518440. [Accessible on: 09 September 2025].\u003c/li\u003e\n\u003cli\u003eMohseni A, Ghotbi E, Kazemi F, Shababi A, Jahan SC, Mohseni A, et al. Artificial Intelligence in Radiology. Radiol Clin North Am 2024;62:935\u0026ndash;47. https://doi.org/10.1016/j.rcl.2024.03.008. [Accessible on: 09 September 2025].\u003c/li\u003e\n\u003cli\u003eHassankhani A, Amoukhteh M, Valizadeh P, Jannatdoust P, Sabeghi P, Gholamrezanezhad A. Radiology as a Specialty in the Era of Artificial Intelligence: A Systematic Review and Meta-analysis on Medical Students, Radiology Trainees, and Radiologists. Acad Radiol 2024;31:306\u0026ndash;21. https://doi.org/10.1016/j.acra.2023.05.024. [Accessible on: 10 September 2025].\u003c/li\u003e\n\u003cli\u003eAlyami AS, Majrashi NA, Shubayr NA. Radiologists\u0026rsquo; and Radiographers\u0026rsquo; Perspectives on Artificial Intelligence in Medical Imaging in Saudi Arabia. Current Medical Imaging Formerly Current Medical Imaging Reviews 2024;20. https://doi.org/10.2174/0115734056250970231117111810. [Accessible on: 10 September 2025].\u003c/li\u003e\n\u003cli\u003eHamd Z, Alorainy A, Aldhahi M, Gareeballah A, F Alsubaie N, A Alshanaiber S, et al. Evaluation of the Impact of Artificial Intelligence on Clinical Practice of Radiology in Saudi Arabia. J Multidiscip Healthc 2024;Volume 17:4745\u0026ndash;56. https://doi.org/10.2147/JMDH.S465508. [Accessible on: 11 September 2025].\u003c/li\u003e\n\u003cli\u003eTajaldeen A, Alghamdi S. Evaluation of radiologist\u0026rsquo;s knowledge about the Artificial Intelligence in diagnostic radiology: a survey-based study. Acta Radiol Open 2020;9. https://doi.org/10.1177/2058460120945320. [Accessible on: 11 September 2025].\u003c/li\u003e\n\u003cli\u003eBaghdadi LR, Mobeirek AA, Alhudaithi DR, Albenmousa FA, Alhadlaq LS, Alaql MS, et al. Patients\u0026rsquo; Attitudes Toward the Use of Artificial Intelligence as a Diagnostic Tool in Radiology in Saudi Arabia: Cross-Sectional Study (Preprint) 2023. https://doi.org/10.2196/preprints.53108. [Accessible on: 11 September 2025].\u003c/li\u003e\n\u003cli\u003eShafiq P, Mehmood Y, Nisar S, Abdulaziz Alanazi R, Alanazi HA, Alenezi HN, et al. Is Artificial Intelligence a Threat to Radiologists? Perception of Radiologists in Saudi Arabia. Journal of Pioneering Medical Sciences 2025;14:170\u0026ndash;7. https://doi.org/10.47310/jpms2025140525. [Accessible on: 11 September 2025].\u003c/li\u003e\n\u003cli\u003eAlelyani M, Alamri S, Alqahtani MS, Musa A, Almater H, Alqahtani N, et al. Radiology Community Attitude in Saudi Arabia about the Applications of Artificial Intelligence in Radiology. Healthcare 2021;9:834. https://doi.org/10.3390/healthcare9070834. [Accessible on: 11 September 2025].\u003c/li\u003e\n\u003cli\u003eAldhafeeri FM. Perspectives of radiographers on the emergence of artificial intelligence in diagnostic imaging in Saudi Arabia. Insights Imaging 2022;13:178. https://doi.org/10.1186/s13244-022-01319-z. [Accessible on: 11 September 2025].\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Artificial-Intelligence (AI), Diagnostic Radiology, Medical Imaging, Saudi Arabia, Radiologists' Knowledge, AI Integration in Healthcare","lastPublishedDoi":"10.21203/rs.3.rs-7724943/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7724943/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e\u003cp\u003eArtificial intelligence (AI) is changing the face of healthcare, especially in the field of diagnostic radiology, and improving the accuracy and efficiency of medical images. In Saudi Arabia, AI is revolutionizing workflows, able to advance early disease detection, as well as reduce the number of human mistakes. However, issues such as regulation and specialized training requirements persist.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePurpose\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis research examines the AI awareness and knowledge of diagnostic radiologists in Saudi Arabia with an emphasis on AI and medical imaging. It assesses AI awareness in trainees and professionals in radiology and discusses possibilities for integrating AI into the teaching curricula. In addition to the key highlight, the study aimed at finding regional variations in perceptions about the role of AI in diagnostic radiology.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMaterials and Methods\u003c/b\u003e\u003c/p\u003e\u003cp\u003e A retrospective methods chart review and survey was performed over the course of five months (June-September 2014), to survey 500 different radiologists and radiology residents. Data were gathered through surveys sent out through radiological societies and social media. The statistical analysis was conducted using version 26 of the Statistical Package for the Social Sciences (SPSS) with a p-value less than 0.05. Ethical approval was granted by the University of Hail.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFor example, out of 433 respondents, 80.6% were aware of the existence of AI, and 82.2% were aware of the potentially positive effects of AI for improved diagnosis. However, concerns about job displacement and machine errors were noticed. Moreover, there was a greater proportion of interest in AI education, with a total of 81.5%, radiologists in the central areas showing a higher level of knowledge in the northern region (P\u0026thinsp;=\u0026thinsp;0.023). Those with more education had greater awareness of AI (P\u0026thinsp;=\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAI has great potential for benefits to the field of diagnostic radiology in Saudi Arabia, with challenges regarding regulation, training, and the issue of privacy that need to be addressed. The study highlights the significance of integrating AI educational content into the medical curriculum, aligning with Saudi Vision 2030.\u003c/p\u003e","manuscriptTitle":"The Role of Artificial Intelligence in Enhancing Diagnostic Radiology: Applications and Advancements in Medical Imaging in Saudi Arabia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-26 01:02:23","doi":"10.21203/rs.3.rs-7724943/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"22589268-d25f-4053-810c-aae6262aa156","owner":[],"postedDate":"October 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-25T08:54:10+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-26 01:02:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7724943","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7724943","identity":"rs-7724943","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}