Factors Influencing the Utilization of Large Language Models among Medical Students in Bangladesh: A KAP Study

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Jubayer Hossain, Md. Mahadi Hassan, Md. Fakhrul Islam Maruf, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6740202/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 Large Language Models (LLMs) have demonstrated remarkable potential in enhancing medical education. This study explored the knowledge, attitudes, and practices of undergraduate medical students in Bangladesh regarding LLM utilization. Methods This cross-sectional study, conducted from March to June 2024, assessed the knowledge, attitudes, and practices (KAP) of undergraduate medical students (MBBS) in Bangladesh. A convenience sampling method was used, with 1000 participants. A structured questionnaire, validated with acceptable Cronbach's alpha (knowledge: 0.703, attitude: 0.707, practice: 0.809), was distributed online via Google Forms. Data were analyzed using descriptive statistics, percentile-based thresholds, and multivariate ordinal logistic regression in R (version 4.3), with statistical significance set at P < 0.05. Results Most participants exhibited poor knowledge (43%), negative or uncertain attitudes (78%), and low engagement with LLMs (37%). Male students and those from private institutions showed significantly higher knowledge, more positive attitudes, and greater utilization of LLMs than their counterparts. Ordinal logistic regression confirmed these associations, highlighting gender and institutional type as the key determinants. Conclusions These findings underscore the need for targeted interventions to improve AI literacy, address disparities in access, and effectively integrate LLMs into medical curricula. Faculty training, institutional support, and careful planning are essential for harnessing the benefits of LLMs while mitigating concerns about accuracy and over-reliance. Future research should explore the longitudinal trends and evaluate the impact of AI-based interventions on learning outcomes. This study provides valuable insights into the current state of LLM adoption in medical education in Bangladesh, and emphasizes the importance of equitable access, training, and integration to maximize the potential of these transformative technologies. Artificial Intelligence Large Language Models Medical Education AI in Medical Education Bangladesh Background Medical education is one of the most fundamental and important aspects of the healthcare system worldwide. It primarily equips future practitioners with the knowledge and skills necessary to address evolving health challenges[ 1 ]. Traditional medical education usually includes didactic lectures, textbooks, and clinical rotations [ 2 ]. Although these methods remain crucial for medical education, the rapid expansion of medical knowledge, clinical complexities, and resource constraints and limitations, particularly in middle-income countries, often make it difficult for traditional professional approaches [ 3 ]. The integration of digital technologies, including artificial intelligence (AI), has transformed modern education and offers innovative solutions to enhance learning outcomes, accessibility, and scalability [ 4 ]. Among the integrated technologies, large language models (LLMs), such as GPT-3 and GPT-4, have drawn attention for their capabilities in processing and generating human-like text, supporting applications from personalized tutoring to clinical decision support [ 5 ], [ 6 ]. Therefore, the scope for integrating artificial intelligence into medical education has significant potential [ 7 ] and demands urgent evaluation of medical professionals’ and educators’ preparedness and attitude to utilize them in medical education for an efficient learning process. The foundation of AI as a scientific discipline was laid in the 20th century. British mathematician Alan Turing introduced the idea of developing machines that are indistinguishable from human behavior and intelligence. In his seminal paper, “Computing Machinery and Intelligence,” he discussed intelligent machines, a concept that later evolved significantly [ 8 ]. The term ‘Artificial Intelligence’ was coined at a workshop held on the campus of Dartmouth College in 1956 by John McCarthy [ 9 ]. Artificial intelligence is now defined as the study of algorithms, which enables machines to reason and perform different problem-solving, object-oriented, and decision-making activities [ 10 ]. Large Language Models are a sub-category of Artificial Intelligence technology that allows users to process natural language with the capability to produce outcomes in human-like conversations [ 11 ], [ 12 ]. These models are extensively trained on a large amount of data through direct input by trainers and through data generated with the help of the Internet. Their recent developments have shown an outstanding capacity to perform and comprehend situations with critical thinking and abstract reasoning, often competing with human capabilities [ 13 ]. In recent years, large language models in short LLMs have shown outstanding performance in various aspects such as writing codes, producing stories or poems, and other activities. These LLMs have demonstrated remarkable potential for assisting humans in solving challenging tasks, easing information availability, and gathering evidence [ 14 ]. A recent study conducted in Japan found that ChatGPT-4 performed better on the Japanese clinical competency test than Japanese residents who had not completed two years of training. In that study, ChatGPT-4 performed better in terms of knowledge about different diseases requiring in-depth concepts [ 15 ]. This shows that LLMs can be used for assistance in multidimensional purposes, and using these tools can provide a medical student with the scope to increase their competency and the availability of information for academic or other related purposes. It is evident that the use of LLMs in different occupational areas can increase both efficiency and performance, especially in medical education [ 12 ]. Recently, medical practitioners are planning to use LLMs in better and more efficient ways of gathering information, patient simulations, cross-checking information, and other activities related to medical education [ 16 ]. Because of different structural and resource constraints, Bangladesh faces unique challenges in medical education, including overcrowded classrooms, insufficient faculty-to-student ratios, and reliance on rote memorization over critical thinking [ 17 ]. Although digital health initiatives have gained attention, the role of LLMs in this context remains underexplored. Existing research highlights the potential of AI to mitigate resource gaps in low- and middle-income countries; however, empirical evidence on LLMs’ effectiveness of LLMs in medical education is scarce [ 18 ]. Understanding the perceptions and experiences of undergraduate medical students, the primary stakeholders, is crucial for evaluating LLMs’ applicability and ensuring contextually relevant implementation. This raises the question of how adaptive medical students in Bangladesh are in this new era of artificial intelligence tools. The growing influence of these LLMs can provide a better and more efficient way for medical students in Bangladesh to perceive and approach their education regarding the successful implementation of these technologies in their academic lives. Therefore, this study explored the current knowledge, attitudes, and practices of medical college students in Bangladesh regarding the utilization of large language models in their medical education. Methods Study design and setting This cross-sectional study was conducted between March to June 2024 among undergraduate medical students studying in undergraduate courses (MBBS) in Bangladesh. Ethical approval was obtained from the Institutional Review Board of the Center for Health Innovation, Research, Action, and Learning(Approval Number:CHIBAN1APR2024-0001). This study was conducted according to ethical guidelines, and all participants provided informed consent for participation. The research was conducted in an online setting, where participants completed a questionnaire hosted on Google Forms. Sampling and sample size A convenience non-probabilistic sampling technique was used in this study. The estimated required sample size for our study was calculated using the basic formula[ 19 ], $$\:n=\:\frac{{z}^{2}pq}{{d}^{2}}$$ where n = the number of samples, z = 1.96 (95% confidence level), p = prevalence estimate (50% or 0.5); as unknown prevalence due to a previous study in Bangladesh, q = (1 − p), d = precision limit or proportion of sampling error (5% or 0.05), and n = 1.962 × 0.5 × (1 − 0.5)/0.052–384.16. We exceeded the sample size and recruited a total of 1000 participants, ensuring an adequate number of samples to increase the robustness and credibility of the study. Survey instruments The questionnaire used in this study was previously developed and validated by Biri et al. [ 20 ] for the assessment of medical students’ knowledge, attitude, and practice regarding large language models. The original questionnaire showed good internal consistency and was deemed appropriate for the objectives. The questionnaire was organized into three domains—knowledge, attitude, and practice–with six questions. The questionnaire was adopted based on a review of relevant literature[ 20 ]. The responses were coded for quantitative analysis as follows: strongly agree = 5, agree = 4, neutral = 3, disagree = 2, and strongly disagree = 1. The Cronbach’s alphas for the knowledge, attitude, and practice domains were 0.703, 0.707, and 0.809, respectively, indicating acceptable internal consistency. For each student, the average score for a domain was calculated by summing the scores of the six responses and dividing the total by six. Test-retest reliability was assessed using Intraclass Correlation Coefficients (ICCs), with values of 0.82, 0.87, and 0.78 for the knowledge, attitude, and practice domains, respectively[ 20 ], [ 21 ], [ 22 ]. Participants Undergraduate medical students were recruited for this study, with the target participants enrolled in the Bachelor of Medicine and Bachelor of Surgery (MBBS) program. The survey link was distributed online to all eligible students and those who did not voluntarily participated were excluded from the study. Data collection Data collection was conducted using a finalized questionnaire that was distributed to undergraduate medical students via an online platform (Google Forms). Participants were contacted through Facebook, WhatsApp, and Email, with a link to the questionnaire provided, along with a brief explanation of the research objectives and the voluntary nature of participation. Ample time was allotted for participants to complete the questionnaire, and reminders were sent as necessary to improve the response rates. Data analysis The data obtained from the completed questionnaires were subjected to a structured analytical process. No missing data were observed, as all questions were mandatory in Google Forms. Descriptive statistics, including frequencies and percentages, were calculated to summarize the responses to the individual items within each domain (knowledge, attitude, and practice). The responses were coded for quantitative analysis as follows: strongly agree = 5, agree = 4, neutral = 3, disagree = 2, and strongly disagree = 1. For each student, the average score for a domain was computed by summing the scores of the six responses and dividing the total by six. To classify the respondents based on their KAP scores, percentile-based thresholds (33rd and 66th percentiles) were applied: knowledge level (poor, moderate, good), attitude level (negative, uncertain, positive), and practice level (low, moderate, high). The chi-square test was used to compare categorical variables with an expected equal distribution across all categories, and statistical significance was determined when the occurrence was unlikely to be due to chance. Multivariate ordinal logistic regression was conducted to identify the factors associated with the KAP domains. Data analysis was conducted using R (version 4.3), with a p-value of less than 0.05 considered statistically significant. Results Demographic Characteristics of the Participants Table 1 presents the demographic characteristics of the participants. In our study, an overwhelming majority of the participants (93%) were aged 25 years or older, while only 7% belonged to the < 25 years category. More than half of the participants (63%) were female, and the rest (37%) were male. Most of the participants (71%) were studying at public medical colleges. Additionally, the majority (73%) were in their senior years of college. Table 1 Demographic Characteristics of the Participants (N = 1000) Characteristic N = 1,000 1 Age =25 years 68 (7%) Gender Female 630 (63%) Male 370 (37%) Institution Type Private 292 (29%) Public 708 (71%) Year Junior 272 (27%) Senior 728 (73%) 1 n (%) (Table 1 : Demographic Characteristics of the Participants) Distribution of Knowledge, Attitude, and Practice Regarding LLMs among the Participants The distribution of participants regarding their knowledge, attitudes, and practices regarding LLMs is presented in Table 2 . The findings suggest that the majority of the participants (43%) possessed a poor level of knowledge about LLMs, followed by a good LLM knowledge level (33%) and a moderate level of knowledge (23%). Similarly, most of the participants tended to have a negative (39%) and uncertain attitude (39%) regarding the utilization of LLMs in their medical education, while only 22% of them showed a positive attitude. On the other hand, the majority of the participants (37%) tended to show poor practices while utilizing LLMs for their medical education, followed by a high practice level (33%) and moderate practice level (30%). Table 2 Distribution of Knowledge, Attitude, and Practice Regarding LLMs among the Participants (N = 1000) Characteristic N = 1,000 1 Knowledge Level Poor 434 (43%) Moderate 234 (23%) Good 332 (33%) Attitude Level Negative 391 (39%) Uncertain 393 (39%) Positive 216 (22%) Practice Level Low 371 (37%) Moderate 303 (30%) High 326 (33%) 1 n (%) (Table 2 : Distribution of Knowledge, Attitude, and Practice Regarding LLMs among the Participants) Factors Associated with Good Knowledge of LLM among the Participants To identify socio-demographics associated with knowledge, attitude, and practice regarding LLM utilization, a chi-square test was conducted (Table 3 ). Among the sociodemographic factors, participants’ gender and institution type were found to be significantly associated with knowledge regarding LLM utilization. This association was confirmed by ordinal logistic regression analysis (Table 4 ). The findings suggest that male participants (OR = 1.81, 95% CI = 1.42, 2.32, p < 0.001) were significantly more likely to have good knowledge regarding the utilization of LLMs compared to female participants. In addition, participants studying at public medical institutions (OR = 0.45, 95% CI 0.34, 0.58, p < 0.001) were significantly less likely to possess good knowledge than participants in private medical institutions. However, factors such as age and years of education were not significantly associated with the knowledge level of the participants. Table 3 Factors Associated with Knowledge, Attitude, and Practice of Utilization of LLM among the Study Participants (N = 1000) Knowledge Attitude Practices Characteristic Poor N = 434 1 Moderate N = 234 1 Good N = 332 1 p-value 2 Negative N = 391 1 Uncertain N = 393 1 Positive N = 216 1 p-value 2 Low N = 371 1 Moderate N = 303 1 High N = 326 1 p-value 2 Age 0.13 0.3 0.2 =25 34 (7.8%) 19 (8.1%) 15 (4.5%) 31 (7.9%) 27 (6.9%) 10 (4.6%) 29 (7.8%) 24 (7.9%) 15 (4.6%) Gender < 0.001 0.003 < 0.001 Female 307 (71%) 150 (64%) 173 (52%) 268 (69%) 244 (62%) 118 (55%) 259 (70%) 194 (64%) 177 (54%) Male 127 (29%) 84 (36%) 159 (48%) 123 (31%) 149 (38%) 98 (45%) 112 (30%) 109 (36%) 149 (46%) Institution Type < 0.001 < 0.001 < 0.001 Private 94 (22%) 52 (22%) 146 (44%) 87 (22%) 109 (28%) 96 (44%) 55 (15%) 80 (26%) 157 (48%) Public 340 (78%) 182 (78%) 186 (56%) 304 (78%) 284 (72%) 120 (56%) 316 (85%) 223 (74%) 169 (52%) Year > 0.9 0.4 > 0.9 Junior 116 (27%) 66 (28%) 90 (27%) 98 (25%) 116 (30%) 58 (27%) 102 (27%) 80 (26%) 90 (28%) Senior 318 (73%) 168 (72%) 242 (73%) 293 (75%) 277 (70%) 158 (73%) 269 (73%) 223 (74%) 236 (72%) 1 n (%) 2 Pearson’s Chi-squared test Table 4 Predictors of Knowledge, Attitude, and Practice Regarding Utilization of LLM among the Study Participants (N = 1000) Knowledge Attitude Practices Characteristic OR 1 95% CI 1 p-value OR 1 95% CI 1 p-value OR 1 95% CI 1 p-value Age =25 years 0.73 0.45, 1.17 0.2 0.77 0.48, 1.23 0.3 0.76 0.47, 1.22 0.3 Gender Female — — — — — — Male 1.81 1.42, 2.32 < 0.001 1.45 1.14, 1.85 0.002 1.56 1.22, 1.99 < 0.001 Institution Type Private — — — — — — Public 0.45 0.34, 0.58 < 0.001 0.51 0.39, 0.66 < 0.001 0.29 0.22, 0.37 < 0.001 Year Junior — — — — — — Senior 1.04 0.80, 1.36 0.8 0.92 0.71, 1.20 0.6 1.04 0.79, 1.36 0.8 1 OR = Odds Ratio, CI = Confidence Interval (Table 3 : Factors Associated with Knowledge, Attitude, and Practice of Utilization of LLM among the Study Participants) Factors Associated with Positive Attitude Towards LLM among the Study Participants The chi-square results (Table 3 ) revealed a significant association between attitude level and the gender and institution type of the participants. This association was confirmed using ordinal logistic regression analysis (Table 4 ). Male participants (OR = 1.45, 95% CI 1.14, 1.85, p = 0.002) were significantly more likely to show a positive attitude towards LLM utilization in medical education than female participants. However, participants in public institutions (OR = 0.51, 95% CI: 0.39, 0.66, p < 0.001) were less likely to have positive attitudes than participants in medical institutions. Factors such as the participants’ age and years of education revealed no significant association with their attitude level. (Table 4 : Predictors of Knowledge, Attitude, and Practice Regarding Utilization of LLM among the Study Participants) Factors Associated with the Practice Level of LLM among the Study Participants Participants’ gender and institution type were significantly associated with their LLM practice level (Table 3 ). The ordinal logistic regression in Table 4 revealed that male participants (OR = 1.56, 95% CI = 1.22, 1.99, p < 0.001) were significantly more likely to have a high level of practice regarding LLMs in their medical education. However, participants studying at public institutions (OR = 0.29, 95% CI 0.22, 0.37, p < 0.001) were less likely to have a high level of practice regarding LLMs in medical education. Factors such as the participants’ age and years of education were not significantly associated with their practice level. Discussion This study provides insights into the knowledge, attitude, and practice (KAP) of undergraduate medical students in Bangladesh regarding the utilization of Large Language Models (LLMs) in medical education. The findings highlight varying levels of familiarity and engagement with LLMs influenced by factors such as gender and institutional type. While the potential of LLMs to enhance medical education is evident, concerns regarding their integration into learning practices remain. Our results indicate that a significant proportion (43%) of students exhibited poor knowledge of LLMs, which aligns with previous studies [ 23 ], [ 24 ]. This emphasizes the limited awareness and understanding of AI-driven educational tools among medical students. The low knowledge levels observed in this study may be attributed to the lack of formal training in LLMs within the medical curriculum in Bangladesh. Notably, male students were more likely (48%) to have better knowledge of LLMs than their female counterparts (52%) were. This is consistent with prior research suggesting gender-based differences in the use of new technologies[ 25 ]. Additionally, students from private institutions demonstrated significantly higher knowledge levels (44%) than those in public institutions (56%), potentially due to differences in technological infrastructure and exposure to AI-based learning platforms, as private universities in Bangladesh have greater access to ICT than public universities [ 26 ]. The study also found that students’ attitudes toward LLMs varied, with a proportion of uncertainty (39%) or negativity (39%). Previous studies have similarly reported that students harbor doubts about the accuracy and reliability of the information generated by large language models (LLMs), leading to reluctance to adopt these tools for educational purposes [ 27 ], [ 28 ]. The negative and uncertain attitudes observed among students may arise from the doubt of misinformation, lack of guidance from faculty, or limited hands-on experience with AI-driven learning methods. Notably, gender and institutional differences were significant determinants of students' attitudes; male students (45%) and those from private institutions (44%) were more likely to have a positive perception of LLMs. This suggests that there is a need for targeted interventions such as workshops and training on AI literacy programs. This could help foster a more informed perspective of LLMs in medical education. Regarding practice, a notable proportion (37%) of students reported low engagement with LLMs for educational purposes, which aligns with the findings of similar studies [ 29 , 30 ]. The underutilization of LLMs may be attributed to barriers such as limited access, inadequate training, and lack of integration into academic curricula. Male students (46%) and those enrolled in private medical institutions (48%) were more likely to engage with LLMs in their studies, reinforcing the need for equitable access to technological resources across different demographics. Addressing disparities and promoting structured guidance regarding the effective use of LLMs may enhance their adoption in medical education. Overall, this study’s findings have significant implications for medical education. LLMs can be effective as supplementary learning tools capable of providing instant access to vast medical knowledge and supporting self-directed learning. However, their integration must be carefully planned to ensure accuracy, reliability, and complementarity with the existing teaching methods [ 31 ]. Faculty training, curriculum adaptation, and institutional support are essential to maximizing the benefits of LLMs and mitigating concerns about misinformation and over-reliance on AI-generated content. Although this study provides valuable insights, it has several limitations. The reliance on self-reported data may introduce response bias, and the study’s cross-sectional design limits causal inferences. In addition, the findings are specific to Bangladesh and may not be generalizable to other regions. Future research should explore the longitudinal trends in LLM adoption and evaluate the effectiveness of AI-based interventions in medical education. Experimental studies assessing learning outcomes among students using LLMs compared with traditional methods would further elucidate their impact on medical training. Conclusion The majority had poor knowledge (43%), negative or uncertain attitudes (78%), and low engagement with LLMs (37%). Male students and those from private institutions showed significantly higher knowledge, more positive attitudes, and greater utilization of LLMs than their counterparts. The findings highlight the need for targeted interventions to improve AI literacy, address disparities in access, and effectively integrate LLMs into medical curricula. Faculty training, institutional support, and careful planning are essential for harnessing the benefits of LLMs while mitigating concerns about accuracy and over-reliance. Declarations Ethics approval and consent to participate This study was approved by the Institutional Review Board of the Center for Health Innovation, Research, Action, and Learning, Bangladesh (Approval Number:CHIBAN1APR2024-0001). Informed consent was obtained from all the participants in accordance with the ethical guidelines of the Declaration of Helsinki. The participants were fully informed of the study objectives, procedures, and potential risks before providing written consent. Clinical trial number : not applicable. Consent for publication Not applicable. Availability of data and materials The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request. Additional materials, including software code and protocols, will be made available upon request to ensure the transparency and reproducibility of the research. Competing interests The authors declare that they have no conflicts of interest. No financial or personal relationships could influence the work reported in this manuscript. Funding This study received no specific funding. Authors' contributions Md. Jubayer Hossain: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing; Md. Mahadi Hassan: Data curation, Formal analysis, Investigation, Methodology, Validation, Writing – original draft, Writing – review & editing; Md. Fakrul Islam Maruf: Data curation, Writing – original draft, Writing – review & editing; Akash Saha: Data curation, Writing – review & editing Acknowledgements The authors express their sincere gratitude to Dr. Syeda Tasneem Towhid for her unwavering support from CHIRAL Bangladesh since its inception. Her guidance and encouragement have been invaluable for our endeavors. References J. Frenk et al. , “Health professionals for a new century: transforming education to strengthen health systems in an interdependent world,” The Lancet , vol. 376, no. 9756, pp. 1923–1958, Dec. 2010, doi: 10.1016/S0140-6736(10)61854-5. C. A. Fernández-Rodríguez, M. C. Arenas-Fenollar, I. Lacruz-Pérez, and R. Tárraga-Mínguez, “Teaching Methods in Medical Education: An Analysis of the Assessments and Preferences of Students,” Sustainability , vol. 15, no. 11, Art. no. 11, Jan. 2023, doi: 10.3390/su15119044. World Health Organization, Global strategy on human resources for health: workforce 2030 . 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Neural Netw. , vol. 2023-June, no. Ml, 2023, doi: 10.1109/IJCNN54540.2023.10191510. Additional Declarations No competing interests reported. Supplementary Files LLMsQuestionnaire.pdf 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-6740202","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":475932442,"identity":"fb18cf4c-9943-4059-babd-14e8e6f1fe8d","order_by":0,"name":"Md. Jubayer Hossain","email":"data:image/png;base64,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","orcid":"","institution":"Center for Health Innovation, Research, Action, and Learning—Bangladesh","correspondingAuthor":true,"prefix":"","firstName":"Md.","middleName":"Jubayer","lastName":"Hossain","suffix":""},{"id":475932443,"identity":"dd458cb4-6e84-4a33-b440-a2a92049ac1e","order_by":1,"name":"Md. Mahadi Hassan","email":"","orcid":"","institution":"aiHealth Lab, Center for Health Innovation, Research, Action, and Learning—Bangladesh","correspondingAuthor":false,"prefix":"","firstName":"Md.","middleName":"Mahadi","lastName":"Hassan","suffix":""},{"id":475932444,"identity":"4368ef2f-4598-4849-b7d1-36973cf1a9a3","order_by":2,"name":"Md. Fakhrul Islam Maruf","email":"","orcid":"","institution":"Population Health Studies Division, Center for Health Innovation, Research, Action, and Learning—Bangladesh, Dhaka, Bangladesh","correspondingAuthor":false,"prefix":"","firstName":"Md.","middleName":"Fakhrul Islam","lastName":"Maruf","suffix":""},{"id":475932446,"identity":"116b39fb-1285-440a-9d23-111b51850c4d","order_by":3,"name":"Akash Saha","email":"","orcid":"","institution":"Sir Salimullah Medical College","correspondingAuthor":false,"prefix":"","firstName":"Akash","middleName":"","lastName":"Saha","suffix":""}],"badges":[],"createdAt":"2025-05-24 16:53:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6740202/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6740202/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85721496,"identity":"126cd2c3-6a69-4e9d-b88f-e83442271a51","added_by":"auto","created_at":"2025-07-01 05:40:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1050636,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6740202/v1/bcf981e7-38bc-4fb0-b0e8-2a1479341da9.pdf"},{"id":85415823,"identity":"2883d5c1-4e9c-4ce6-8866-cd7703a466cf","added_by":"auto","created_at":"2025-06-25 14:44:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1688429,"visible":true,"origin":"","legend":"","description":"","filename":"LLMsQuestionnaire.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6740202/v1/3ddc34817bfd0bee32636fac.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Factors Influencing the Utilization of Large Language Models among Medical Students in Bangladesh: A KAP Study","fulltext":[{"header":"Background","content":"\u003cp\u003eMedical education is one of the most fundamental and important aspects of the healthcare system worldwide. It primarily equips future practitioners with the knowledge and skills necessary to address evolving health challenges[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Traditional medical education usually includes didactic lectures, textbooks, and clinical rotations [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Although these methods remain crucial for medical education, the rapid expansion of medical knowledge, clinical complexities, and resource constraints and limitations, particularly in middle-income countries, often make it difficult for traditional professional approaches [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The integration of digital technologies, including artificial intelligence (AI), has transformed modern education and offers innovative solutions to enhance learning outcomes, accessibility, and scalability [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Among the integrated technologies, large language models (LLMs), such as GPT-3 and GPT-4, have drawn attention for their capabilities in processing and generating human-like text, supporting applications from personalized tutoring to clinical decision support [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Therefore, the scope for integrating artificial intelligence into medical education has significant potential [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] and demands urgent evaluation of medical professionals\u0026rsquo; and educators\u0026rsquo; preparedness and attitude to utilize them in medical education for an efficient learning process.\u003c/p\u003e \u003cp\u003eThe foundation of AI as a scientific discipline was laid in the 20th century. British mathematician Alan Turing introduced the idea of developing machines that are indistinguishable from human behavior and intelligence. In his seminal paper, \u0026ldquo;Computing Machinery and Intelligence,\u0026rdquo; he discussed intelligent machines, a concept that later evolved significantly [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The term \u0026lsquo;Artificial Intelligence\u0026rsquo; was coined at a workshop held on the campus of Dartmouth College in 1956 by John McCarthy [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Artificial intelligence is now defined as the study of algorithms, which enables machines to reason and perform different problem-solving, object-oriented, and decision-making activities [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Large Language Models are a sub-category of Artificial Intelligence technology that allows users to process natural language with the capability to produce outcomes in human-like conversations [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. These models are extensively trained on a large amount of data through direct input by trainers and through data generated with the help of the Internet. Their recent developments have shown an outstanding capacity to perform and comprehend situations with critical thinking and abstract reasoning, often competing with human capabilities [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In recent years, large language models in short LLMs have shown outstanding performance in various aspects such as writing codes, producing stories or poems, and other activities. These LLMs have demonstrated remarkable potential for assisting humans in solving challenging tasks, easing information availability, and gathering evidence [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA recent study conducted in Japan found that ChatGPT-4 performed better on the Japanese clinical competency test than Japanese residents who had not completed two years of training. In that study, ChatGPT-4 performed better in terms of knowledge about different diseases requiring in-depth concepts [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. This shows that LLMs can be used for assistance in multidimensional purposes, and using these tools can provide a medical student with the scope to increase their competency and the availability of information for academic or other related purposes. It is evident that the use of LLMs in different occupational areas can increase both efficiency and performance, especially in medical education [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Recently, medical practitioners are planning to use LLMs in better and more efficient ways of gathering information, patient simulations, cross-checking information, and other activities related to medical education [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBecause of different structural and resource constraints, Bangladesh faces unique challenges in medical education, including overcrowded classrooms, insufficient faculty-to-student ratios, and reliance on rote memorization over critical thinking [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Although digital health initiatives have gained attention, the role of LLMs in this context remains underexplored. Existing research highlights the potential of AI to mitigate resource gaps in low- and middle-income countries; however, empirical evidence on LLMs\u0026rsquo; effectiveness of LLMs in medical education is scarce [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Understanding the perceptions and experiences of undergraduate medical students, the primary stakeholders, is crucial for evaluating LLMs\u0026rsquo; applicability and ensuring contextually relevant implementation. This raises the question of how adaptive medical students in Bangladesh are in this new era of artificial intelligence tools. The growing influence of these LLMs can provide a better and more efficient way for medical students in Bangladesh to perceive and approach their education regarding the successful implementation of these technologies in their academic lives. Therefore, this study explored the current knowledge, attitudes, and practices of medical college students in Bangladesh regarding the utilization of large language models in their medical education.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and setting\u003c/h2\u003e \u003cp\u003eThis cross-sectional study was conducted between March to June 2024 among undergraduate medical students studying in undergraduate courses (MBBS) in Bangladesh. Ethical approval was obtained from the Institutional Review Board of the Center for Health Innovation, Research, Action, and Learning(Approval Number:CHIBAN1APR2024-0001). This study was conducted according to ethical guidelines, and all participants provided informed consent for participation. The research was conducted in an online setting, where participants completed a questionnaire hosted on Google Forms.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSampling and sample size\u003c/h3\u003e\n\u003cp\u003eA convenience non-probabilistic sampling technique was used in this study. The estimated required sample size for our study was calculated using the basic formula[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e],\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:n=\\:\\frac{{z}^{2}pq}{{d}^{2}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere n\u0026thinsp;=\u0026thinsp;the number of samples, z\u0026thinsp;=\u0026thinsp;1.96 (95% confidence level), p\u0026thinsp;=\u0026thinsp;prevalence estimate (50% or 0.5); as unknown prevalence due to a previous study in Bangladesh, q = (1\u0026thinsp;\u0026minus;\u0026thinsp;p), d\u0026thinsp;=\u0026thinsp;precision limit or proportion of sampling error (5% or 0.05), and n\u0026thinsp;=\u0026thinsp;1.962 \u0026times; 0.5 \u0026times; (1\u0026thinsp;\u0026minus;\u0026thinsp;0.5)/0.052\u0026ndash;384.16. We exceeded the sample size and recruited a total of 1000 participants, ensuring an adequate number of samples to increase the robustness and credibility of the study.\u003c/p\u003e\n\u003ch3\u003eSurvey instruments\u003c/h3\u003e\n\u003cp\u003eThe questionnaire used in this study was previously developed and validated by Biri et al. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] for the assessment of medical students\u0026rsquo; knowledge, attitude, and practice regarding large language models. The original questionnaire showed good internal consistency and was deemed appropriate for the objectives. The questionnaire was organized into three domains\u0026mdash;knowledge, attitude, and practice\u0026ndash;with six questions. The questionnaire was adopted based on a review of relevant literature[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The responses were coded for quantitative analysis as follows: strongly agree\u0026thinsp;=\u0026thinsp;5, agree\u0026thinsp;=\u0026thinsp;4, neutral\u0026thinsp;=\u0026thinsp;3, disagree\u0026thinsp;=\u0026thinsp;2, and strongly disagree\u0026thinsp;=\u0026thinsp;1. The Cronbach\u0026rsquo;s alphas for the knowledge, attitude, and practice domains were 0.703, 0.707, and 0.809, respectively, indicating acceptable internal consistency. For each student, the average score for a domain was calculated by summing the scores of the six responses and dividing the total by six. Test-retest reliability was assessed using Intraclass Correlation Coefficients (ICCs), with values of 0.82, 0.87, and 0.78 for the knowledge, attitude, and practice domains, respectively[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eParticipants\u003c/h3\u003e\n\u003cp\u003eUndergraduate medical students were recruited for this study, with the target participants enrolled in the Bachelor of Medicine and Bachelor of Surgery (MBBS) program. The survey link was distributed online to all eligible students and those who did not voluntarily participated were excluded from the study.\u003c/p\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eData collection was conducted using a finalized questionnaire that was distributed to undergraduate medical students via an online platform (Google Forms). Participants were contacted through Facebook, WhatsApp, and Email, with a link to the questionnaire provided, along with a brief explanation of the research objectives and the voluntary nature of participation. Ample time was allotted for participants to complete the questionnaire, and reminders were sent as necessary to improve the response rates.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eThe data obtained from the completed questionnaires were subjected to a structured analytical process. No missing data were observed, as all questions were mandatory in Google Forms. Descriptive statistics, including frequencies and percentages, were calculated to summarize the responses to the individual items within each domain (knowledge, attitude, and practice). The responses were coded for quantitative analysis as follows: strongly agree\u0026thinsp;=\u0026thinsp;5, agree\u0026thinsp;=\u0026thinsp;4, neutral\u0026thinsp;=\u0026thinsp;3, disagree\u0026thinsp;=\u0026thinsp;2, and strongly disagree\u0026thinsp;=\u0026thinsp;1. For each student, the average score for a domain was computed by summing the scores of the six responses and dividing the total by six. To classify the respondents based on their KAP scores, percentile-based thresholds (33rd and 66th percentiles) were applied: knowledge level (poor, moderate, good), attitude level (negative, uncertain, positive), and practice level (low, moderate, high). The chi-square test was used to compare categorical variables with an expected equal distribution across all categories, and statistical significance was determined when the occurrence was unlikely to be due to chance. Multivariate ordinal logistic regression was conducted to identify the factors associated with the KAP domains. Data analysis was conducted using R (version 4.3), with a p-value of less than 0.05 considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eDemographic Characteristics of the Participants\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the demographic characteristics of the participants. In our study, an overwhelming majority of the participants (93%) were aged 25 years or older, while only 7% belonged to the \u0026lt;\u0026thinsp;25 years category. More than half of the participants (63%) were female, and the rest (37%) were male. Most of the participants (71%) were studying at public medical colleges. Additionally, the majority (73%) were in their senior years of college.\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\u003eDemographic Characteristics of the Participants (N\u0026thinsp;=\u0026thinsp;1000)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \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\u003eN\u0026thinsp;=\u0026thinsp;1,000\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;25 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e932 (93%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;=25 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68 (7%)\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e630 (63%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e370 (37%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInstitution Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrivate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e292 (29%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePublic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e708 (71%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJunior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e272 (27%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSenior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e728 (73%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003en (%)\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(Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e: Demographic Characteristics of the Participants)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDistribution of Knowledge, Attitude, and Practice Regarding LLMs among the Participants\u003c/h2\u003e \u003cp\u003eThe distribution of participants regarding their knowledge, attitudes, and practices regarding LLMs is presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The findings suggest that the majority of the participants (43%) possessed a poor level of knowledge about LLMs, followed by a good LLM knowledge level (33%) and a moderate level of knowledge (23%). Similarly, most of the participants tended to have a negative (39%) and uncertain attitude (39%) regarding the utilization of LLMs in their medical education, while only 22% of them showed a positive attitude. On the other hand, the majority of the participants (37%) tended to show poor practices while utilizing LLMs for their medical education, followed by a high practice level (33%) and moderate practice level (30%).\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\u003eDistribution of Knowledge, Attitude, and Practice Regarding LLMs among the Participants (N\u0026thinsp;=\u0026thinsp;1000)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \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\u003eN\u0026thinsp;=\u0026thinsp;1,000\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKnowledge Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e434 (43%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e234 (23%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e332 (33%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAttitude Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e391 (39%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUncertain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e393 (39%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e216 (22%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePractice Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e371 (37%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e303 (30%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e326 (33%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003en (%)\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(Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e: Distribution of Knowledge, Attitude, and Practice Regarding LLMs among the Participants)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eFactors Associated with Good Knowledge of LLM among the Participants\u003c/h2\u003e \u003cp\u003eTo identify socio-demographics associated with knowledge, attitude, and practice regarding LLM utilization, a chi-square test was conducted (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Among the sociodemographic factors, participants\u0026rsquo; gender and institution type were found to be significantly associated with knowledge regarding LLM utilization. This association was confirmed by ordinal logistic regression analysis (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The findings suggest that male participants (OR\u0026thinsp;=\u0026thinsp;1.81, 95% CI\u0026thinsp;=\u0026thinsp;1.42, 2.32, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were significantly more likely to have good knowledge regarding the utilization of LLMs compared to female participants. In addition, participants studying at public medical institutions (OR\u0026thinsp;=\u0026thinsp;0.45, 95% CI 0.34, 0.58, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were significantly less likely to possess good knowledge than participants in private medical institutions. However, factors such as age and years of education were not significantly associated with the knowledge level of the participants.\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\u003eFactors Associated with Knowledge, Attitude, and Practice of Utilization of LLM among the Study Participants (N\u0026thinsp;=\u0026thinsp;1000)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eKnowledge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eAttitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c13\" namest=\"c10\"\u003e \u003cp\u003ePractices\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCharacteristic\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePoor\u003c/b\u003e N\u0026thinsp;=\u0026thinsp;434\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eModerate\u003c/b\u003e N\u0026thinsp;=\u0026thinsp;234\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eGood\u003c/b\u003e N\u0026thinsp;=\u0026thinsp;332\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eNegative\u003c/b\u003e N\u0026thinsp;=\u0026thinsp;391\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eUncertain\u003c/b\u003e N\u0026thinsp;=\u0026thinsp;393\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003ePositive\u003c/b\u003e N\u0026thinsp;=\u0026thinsp;216\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eLow\u003c/b\u003e N\u0026thinsp;=\u0026thinsp;371\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003eModerate\u003c/b\u003e N\u0026thinsp;=\u0026thinsp;303\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003eHigh\u003c/b\u003e N\u0026thinsp;=\u0026thinsp;326\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e400 (92%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e215 (92%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e317 (95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e360 (92%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e366 (93%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e206 (95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e342 (92%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e279 (92%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e311 (95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;=25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34 (7.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (8.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (4.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31 (7.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27 (6.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10 (4.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e29 (7.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e24 (7.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e15 (4.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e307 (71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e150 (64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e173 (52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e268 (69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e244 (62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e118 (55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e259 (70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e194 (64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e177 (54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e127 (29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84 (36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e159 (48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e123 (31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e149 (38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e98 (45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e112 (30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e109 (36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e149 (46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInstitution Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrivate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e146 (44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e87 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e109 (28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e96 (44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e55 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e80 (26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e157 (48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePublic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e340 (78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e182 (78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e186 (56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e304 (78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e284 (72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e120 (56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e316 (85%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e223 (74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e169 (52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJunior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e116 (27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66 (28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90 (27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e98 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e116 (30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e58 (27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e102 (27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e80 (26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e90 (28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSenior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e318 (73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e168 (72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e242 (73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e293 (75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e277 (70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e158 (73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e269 (73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e223 (74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e236 (72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003en (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003ePearson\u0026rsquo;s Chi-squared test\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 \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\u003ePredictors of Knowledge, Attitude, and Practice Regarding Utilization of LLM among the Study Participants (N\u0026thinsp;=\u0026thinsp;1000)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eKnowledge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eAttitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003ePractices\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCharacteristic\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eOR\u003c/b\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eOR\u003c/b\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eOR\u003c/b\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;25 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;=25 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.45, 1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.48, 1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.47, 1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.3\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.42, 2.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.14, 1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.22, 1.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInstitution Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrivate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePublic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.34, 0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.39, 0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.22, 0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJunior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSenior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.80, 1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.71, 1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.79, 1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003eOR = Odds Ratio, CI\u0026thinsp;=\u0026thinsp;Confidence Interval\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(Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e: Factors Associated with Knowledge, Attitude, and Practice of Utilization of LLM among the Study Participants)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eFactors Associated with Positive Attitude Towards LLM among the Study Participants\u003c/h2\u003e \u003cp\u003eThe chi-square results (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) revealed a significant association between attitude level and the gender and institution type of the participants. This association was confirmed using ordinal logistic regression analysis (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Male participants (OR\u0026thinsp;=\u0026thinsp;1.45, 95% CI 1.14, 1.85, p\u0026thinsp;=\u0026thinsp;0.002) were significantly more likely to show a positive attitude towards LLM utilization in medical education than female participants. However, participants in public institutions (OR\u0026thinsp;=\u0026thinsp;0.51, 95% CI: 0.39, 0.66, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were less likely to have positive attitudes than participants in medical institutions. Factors such as the participants\u0026rsquo; age and years of education revealed no significant association with their attitude level.\u003c/p\u003e \u003cp\u003e(Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e: Predictors of Knowledge, Attitude, and Practice Regarding Utilization of LLM among the Study Participants)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eFactors Associated with the Practice Level of LLM among the Study Participants\u003c/h2\u003e \u003cp\u003eParticipants\u0026rsquo; gender and institution type were significantly associated with their LLM practice level (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The ordinal logistic regression in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e revealed that male participants (OR\u0026thinsp;=\u0026thinsp;1.56, 95% CI\u0026thinsp;=\u0026thinsp;1.22, 1.99, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were significantly more likely to have a high level of practice regarding LLMs in their medical education. However, participants studying at public institutions (OR\u0026thinsp;=\u0026thinsp;0.29, 95% CI 0.22, 0.37, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were less likely to have a high level of practice regarding LLMs in medical education. Factors such as the participants\u0026rsquo; age and years of education were not significantly associated with their practice level.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study provides insights into the knowledge, attitude, and practice (KAP) of undergraduate medical students in Bangladesh regarding the utilization of Large Language Models (LLMs) in medical education. The findings highlight varying levels of familiarity and engagement with LLMs influenced by factors such as gender and institutional type. While the potential of LLMs to enhance medical education is evident, concerns regarding their integration into learning practices remain.\u003c/p\u003e \u003cp\u003eOur results indicate that a significant proportion (43%) of students exhibited poor knowledge of LLMs, which aligns with previous studies [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. This emphasizes the limited awareness and understanding of AI-driven educational tools among medical students. The low knowledge levels observed in this study may be attributed to the lack of formal training in LLMs within the medical curriculum in Bangladesh. Notably, male students were more likely (48%) to have better knowledge of LLMs than their female counterparts (52%) were. This is consistent with prior research suggesting gender-based differences in the use of new technologies[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Additionally, students from private institutions demonstrated significantly higher knowledge levels (44%) than those in public institutions (56%), potentially due to differences in technological infrastructure and exposure to AI-based learning platforms, as private universities in Bangladesh have greater access to ICT than public universities [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe study also found that students\u0026rsquo; attitudes toward LLMs varied, with a proportion of uncertainty (39%) or negativity (39%). Previous studies have similarly reported that students harbor doubts about the accuracy and reliability of the information generated by large language models (LLMs), leading to reluctance to adopt these tools for educational purposes [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The negative and uncertain attitudes observed among students may arise from the doubt of misinformation, lack of guidance from faculty, or limited hands-on experience with AI-driven learning methods. Notably, gender and institutional differences were significant determinants of students' attitudes; male students (45%) and those from private institutions (44%) were more likely to have a positive perception of LLMs. This suggests that there is a need for targeted interventions such as workshops and training on AI literacy programs. This could help foster a more informed perspective of LLMs in medical education.\u003c/p\u003e \u003cp\u003eRegarding practice, a notable proportion (37%) of students reported low engagement with LLMs for educational purposes, which aligns with the findings of similar studies [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The underutilization of LLMs may be attributed to barriers such as limited access, inadequate training, and lack of integration into academic curricula. Male students (46%) and those enrolled in private medical institutions (48%) were more likely to engage with LLMs in their studies, reinforcing the need for equitable access to technological resources across different demographics. Addressing disparities and promoting structured guidance regarding the effective use of LLMs may enhance their adoption in medical education.\u003c/p\u003e \u003cp\u003eOverall, this study\u0026rsquo;s findings have significant implications for medical education. LLMs can be effective as supplementary learning tools capable of providing instant access to vast medical knowledge and supporting self-directed learning. However, their integration must be carefully planned to ensure accuracy, reliability, and complementarity with the existing teaching methods [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Faculty training, curriculum adaptation, and institutional support are essential to maximizing the benefits of LLMs and mitigating concerns about misinformation and over-reliance on AI-generated content.\u003c/p\u003e \u003cp\u003eAlthough this study provides valuable insights, it has several limitations. The reliance on self-reported data may introduce response bias, and the study\u0026rsquo;s cross-sectional design limits causal inferences. In addition, the findings are specific to Bangladesh and may not be generalizable to other regions. Future research should explore the longitudinal trends in LLM adoption and evaluate the effectiveness of AI-based interventions in medical education. Experimental studies assessing learning outcomes among students using LLMs compared with traditional methods would further elucidate their impact on medical training.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe majority had poor knowledge (43%), negative or uncertain attitudes (78%), and low engagement with LLMs (37%). Male students and those from private institutions showed significantly higher knowledge, more positive attitudes, and greater utilization of LLMs than their counterparts. The findings highlight the need for targeted interventions to improve AI literacy, address disparities in access, and effectively integrate LLMs into medical curricula. Faculty training, institutional support, and careful planning are essential for harnessing the benefits of LLMs while mitigating concerns about accuracy and over-reliance.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Institutional Review Board of the Center for Health Innovation, Research, Action, and Learning, Bangladesh (Approval Number:CHIBAN1APR2024-0001). Informed consent was obtained from all the participants in accordance with the ethical guidelines of the Declaration of Helsinki. The participants were fully informed of the study objectives, procedures, and potential risks before providing written consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e: not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request. Additional materials, including software code and protocols, will be made available upon request to ensure the transparency and reproducibility of the research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest. No financial or personal relationships could influence the work reported in this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received no specific funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMd. Jubayer Hossain:\u003c/strong\u003e Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing; \u003cstrong\u003eMd. Mahadi Hassan:\u003c/strong\u003e Data curation, Formal analysis, Investigation, Methodology, Validation, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing; \u003cstrong\u003eMd. Fakrul Islam Maruf:\u003c/strong\u003e Data curation, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing; \u003cstrong\u003eAkash Saha:\u003c/strong\u003e Data curation, Writing \u0026ndash; review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors express their sincere gratitude to Dr. Syeda Tasneem Towhid for her unwavering support from CHIRAL Bangladesh since its inception. Her guidance and encouragement have been invaluable for our endeavors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eJ. 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Ml, 2023, doi: 10.1109/IJCNN54540.2023.10191510.\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, Large Language Models, Medical Education, AI in Medical Education, Bangladesh","lastPublishedDoi":"10.21203/rs.3.rs-6740202/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6740202/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eLarge Language Models (LLMs) have demonstrated remarkable potential in enhancing medical education. This study explored the knowledge, attitudes, and practices of undergraduate medical students in Bangladesh regarding LLM utilization.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis cross-sectional study, conducted from March to June 2024, assessed the knowledge, attitudes, and practices (KAP) of undergraduate medical students (MBBS) in Bangladesh. A convenience sampling method was used, with 1000 participants. A structured questionnaire, validated with acceptable Cronbach's alpha (knowledge: 0.703, attitude: 0.707, practice: 0.809), was distributed online via Google Forms. Data were analyzed using descriptive statistics, percentile-based thresholds, and multivariate ordinal logistic regression in R (version 4.3), with statistical significance set at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eMost participants exhibited poor knowledge (43%), negative or uncertain attitudes (78%), and low engagement with LLMs (37%). Male students and those from private institutions showed significantly higher knowledge, more positive attitudes, and greater utilization of LLMs than their counterparts. Ordinal logistic regression confirmed these associations, highlighting gender and institutional type as the key determinants.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThese findings underscore the need for targeted interventions to improve AI literacy, address disparities in access, and effectively integrate LLMs into medical curricula. Faculty training, institutional support, and careful planning are essential for harnessing the benefits of LLMs while mitigating concerns about accuracy and over-reliance. Future research should explore the longitudinal trends and evaluate the impact of AI-based interventions on learning outcomes. This study provides valuable insights into the current state of LLM adoption in medical education in Bangladesh, and emphasizes the importance of equitable access, training, and integration to maximize the potential of these transformative technologies.\u003c/p\u003e","manuscriptTitle":"Factors Influencing the Utilization of Large Language Models among Medical Students in Bangladesh: A KAP Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-25 14:36:42","doi":"10.21203/rs.3.rs-6740202/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":"16864653-8ee4-4278-8c88-d9e40610be7b","owner":[],"postedDate":"June 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-01T05:23:58+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-25 14:36:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6740202","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6740202","identity":"rs-6740202","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}