Knowledge, Attitudes, and Practices of Artificial Intelligence in Dentistry: A cross-sectional survey

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Gowdar" } ], "publisher": { "@type": "Organization", "name": "F1000Research", "logo": { "@type": "ImageObject", "url": "https://f1000research.com/img/AMP/F1000Research_image.png", "height": 480, "width": 60 } }, "image": { "@type": "ImageObject", "url": "https://f1000research.com/img/AMP/F1000Research_image.png", "height": 1200, "width": 150 }, "description": " Background Artificial intelligence (AI) is rapidly reshaping various aspects of human life, including healthcare. In the Western world, AI is increasingly applied in education and clinical practice through algorithms designed to analyze health data, aid in prediction, and assist with disease diagnosis. However, developing countries like India face obstacles in adopting AI due to limited resources and socio-cultural factors. Aim This study seeks to assess and compare the knowledge, attitudes, and practices related to AI in dentistry among undergraduate and postgraduate students in South India. Methodology A descriptive cross-sectional online survey was conducted among dental students in South India. The survey included 21 validated, structured, close-ended questions addressing demographic details, self-assessment of knowledge, attitudes toward AI applications in dentistry, and self-perceived understanding of AI practice in the field. Results Of 208 respondents (81.8% response rate), 95.6% were familiar with the term AI. Postgraduates demonstrated significantly greater awareness of AI applications (90.9%) compared to undergraduates (25.8%). About 78.3% of undergraduates believed AI supports diagnosis and treatment planning, while 33.4% of undergraduates and 43.2% of postgraduates expressed concern that AI may replace dentists in the future. Most respondents acknowledged AI’s role in oral radiology (UG: 79.1%; PG: 72.2%). Interest in future learning was high (UG: 82.5%; PG: 92.2%). Level of education was a significant predictor of knowledge (p<0.01), while male students showed more positive attitudes (p<0.01). First-year postgraduates reported better AI-related practices than other groups (p<0.01). Conclusion Although most dental students lacked sufficient knowledge regarding the use of AI in dentistry, they displayed positive and encouraging attitudes toward its application. A large proportion expressed willingness to learn AI technologies to apply them in clinical practice. It is therefore recommended that universities and government bodies work together to integrate AI related topics into the dental curriculum to strengthen dental education in India. 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F1000Research 2025, 14 :1314 ( https://doi.org/10.12688/f1000research.173028.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Research Article Knowledge, Attitudes, and Practices of Artificial Intelligence in Dentistry: A cross-sectional survey [version 1; peer review: 2 approved, 1 approved with reservations] Usha GV https://orcid.org/0000-0003-3194-2626 1 , Bhuvaneshwari Nadar https://orcid.org/0000-0002-9267-9297 2 , Sultan Almalki https://orcid.org/0000-0002-3369-9925 3 , Tushar Bhagat 4 , Inderjit M. Gowdar https://orcid.org/0000-0002-3920-8082 3 Usha GV https://orcid.org/0000-0003-3194-2626 1 , Bhuvaneshwari Nadar https://orcid.org/0000-0002-9267-9297 2 , [...] Sultan Almalki https://orcid.org/0000-0002-3369-9925 3 , Tushar Bhagat 4 , Inderjit M. Gowdar https://orcid.org/0000-0002-3920-8082 3 PUBLISHED 26 Nov 2025 Author details Author details 1 Public Health Dentistry, Bapuji Dental College and Hospital, Davangere, Karnataka, 577004, India 2 Public Health Dentistry, Terna Dental College and Hospital, Nerul, Navi Mumbai, Maharashtra, 400706, India 3 Preventive Dental Sciences, Prince Sattam bin Abdulaziz University College of Dentistry, Al Kharj, Riyadh Province, 11942, Saudi Arabia 4 Prosthodontics, Prince Sattam bin Abdulaziz University College of Dentistry, Al Kharj, Riyadh Province, 11942, Saudi Arabia Usha GV Roles: Conceptualization, Formal Analysis, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing Bhuvaneshwari Nadar Roles: Software, Validation, Visualization, Writing – Review & Editing Sultan Almalki Roles: Data Curation, Funding Acquisition, Project Administration, Supervision, Writing – Review & Editing Tushar Bhagat Roles: Visualization, Writing – Review & Editing Inderjit M. Gowdar Roles: Funding Acquisition, Project Administration, Supervision, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS This article is included in the AI in Medicine and Healthcare collection. Abstract Background Artificial intelligence (AI) is rapidly reshaping various aspects of human life, including healthcare. In the Western world, AI is increasingly applied in education and clinical practice through algorithms designed to analyze health data, aid in prediction, and assist with disease diagnosis. However, developing countries like India face obstacles in adopting AI due to limited resources and socio-cultural factors. Aim This study seeks to assess and compare the knowledge, attitudes, and practices related to AI in dentistry among undergraduate and postgraduate students in South India. Methodology A descriptive cross-sectional online survey was conducted among dental students in South India. The survey included 21 validated, structured, close-ended questions addressing demographic details, self-assessment of knowledge, attitudes toward AI applications in dentistry, and self-perceived understanding of AI practice in the field. Results Of 208 respondents (81.8% response rate), 95.6% were familiar with the term AI. Postgraduates demonstrated significantly greater awareness of AI applications (90.9%) compared to undergraduates (25.8%). About 78.3% of undergraduates believed AI supports diagnosis and treatment planning, while 33.4% of undergraduates and 43.2% of postgraduates expressed concern that AI may replace dentists in the future. Most respondents acknowledged AI’s role in oral radiology (UG: 79.1%; PG: 72.2%). Interest in future learning was high (UG: 82.5%; PG: 92.2%). Level of education was a significant predictor of knowledge (p<0.01), while male students showed more positive attitudes (p<0.01). First-year postgraduates reported better AI-related practices than other groups (p<0.01). Conclusion Although most dental students lacked sufficient knowledge regarding the use of AI in dentistry, they displayed positive and encouraging attitudes toward its application. A large proportion expressed willingness to learn AI technologies to apply them in clinical practice. It is therefore recommended that universities and government bodies work together to integrate AI related topics into the dental curriculum to strengthen dental education in India. READ ALL READ LESS Keywords Dental students, Artificial Intelligence, Attitude, Knowledge, Practice Corresponding Author(s) Inderjit M. Gowdar ( [email protected] ) Close Corresponding author: Inderjit M. Gowdar Competing interests: No competing interests were disclosed. Grant information: The authors extend their appreciation to Prince Sattam bin Abdulaziz University for funding this research work through the project number (PSAU/2025/03/33409). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Copyright: © 2025 GV U et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: GV U, Nadar B, Almalki S et al. Knowledge, Attitudes, and Practices of Artificial Intelligence in Dentistry: A cross-sectional survey [version 1; peer review: 2 approved, 1 approved with reservations] . F1000Research 2025, 14 :1314 ( https://doi.org/10.12688/f1000research.173028.1 ) First published: 26 Nov 2025, 14 :1314 ( https://doi.org/10.12688/f1000research.173028.1 ) Latest published: 28 Jan 2026, 14 :1314 ( https://doi.org/10.12688/f1000research.173028.2 )  There is a newer version of this article available. Suppress this message for one day. Introduction The Fourth Industrial Revolution has reshaped how humans live, work, and interact, with technological advances rapidly influencing multiple sectors. 1 Among these, Artificial Intelligence (AI) and Augmented Intelligence (AUI) have drawn significant attention and are now increasingly integrated into healthcare and higher education. 2 AI can be broadly described as a computer-based simulation of human cognitive abilities, capable of performing tasks such as speech recognition, natural language comprehension, and complex decision-making. 3 The term artificial intelligence was first introduced by John McCarthy at Dartmouth University in 1956, and since then, AI has been widely regarded as a transformative force across diverse domains, including supply chain management, transportation, and healthcare. 4 The rapid rise of AI applications in the last decade can be attributed to breakthroughs in advanced algorithms, cost-efficient computing resources such as Graphic Processing Units (GPUs), and the availability of extensive annotated datasets. 5 Dentistry, in particular, has recently witnessed significant advances through AI-driven technologies. Following the basic computational framework of input, processing, and output, AI systems can process diverse data types in dentistry ranging from auditory data (e.g., handpiece sounds) and textual information (e.g., patient records, treatment parameters) to image-based data (e.g., radiographs and clinical photographs). 6 These inputs, when processed through neural networks, yield outputs such as diagnostic insights, prognoses, treatment planning, or disease prediction. 7 AI models, including conventional neural networks and advanced deep learning approaches, have already been applied in root canal anatomy analysis, staging of malignant lesions, detection of proximal caries, Computer Aided Design and Computer Aided Manufacturing (CAD/CAM)-based prosthesis design, and dental implant placement. 8 AI has become an essential component of dental healthcare education. In both preclinical and clinical settings, AI tools enhance learning experiences by offering adaptive, personalized, and mobile-based education. 9 AI systems can tailor learning resources to individual student needs, address knowledge gaps, and provide real-time feedback. 10 Furthermore, AI facilitates the continuous monitoring of academic progress, clinical exposure, and professional development while supporting mentorship and career guidance. These features have positioned AI as an invaluable resource in competency-based education, which is becoming increasingly relevant in global dental curricula. 11 Despite its potential, AI integration remains uneven across regions. While Western countries such as United States have incorporated AI into healthcare education and practice for decision support, 12 developing nations face infrastructural, financial, and sociocultural barriers. For example, India’s healthcare sector struggles with challenges such as limited institutional investment, resistance from healthcare providers, inadequate numbers of AI-trained professionals, and ethical concerns surrounding patient data security. 13 , 14 Additionally, issues such as medicolegal implications, public perception, and fears of physician replacement further hinder the acceptance of AI in medical and dental practice. Addressing these challenges will require targeted investments, robust data policies, faculty training programs, and collaborative research across global institutions. 15 Although AI applications in dentistry are expanding, there remains a paucity of research exploring the knowledge, attitudes, and practices (KAP) of dental students regarding AI in India. Understanding how undergraduate and postgraduate dental students perceive and engage with AI is crucial for developing effective curricula, improving clinical training, and fostering responsible adoption of these technologies. This research aims to assess the level of awareness, attitudes, and readiness for AI integration among dental students, thereby contributing evidence to guide educational policies and infrastructure development in India and other developing countries. Methods Study design This descriptive cross-sectional study was conducted between January and March 2024 and is reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. Study participants The study included all the undergraduate dental students (final-year BDS students and house surgeons) and postgraduate dental students (MDS) from Bapuji Dental College and Hospital. Students who provided informed consent and completed the full questionnaire were eligible for analysis. Survey instrument The questionnaire was adapted from a validated survey originally developed for medical students in Syria 16 and modified for dental students in India. The modified questions consisted of 21 close-ended items divided into two sections: The questionnaire consisted of two main sections. The first section had three questions related to demographic details of the participants. The second main section had 18 questions related to Knowledge, Attitude, and Practice (KAP) related to Artificial Intelligence (AI) in dentistry, subdivided into: Knowledge subscale – Six items assessed awareness of AI, its subtypes, and applications in dentistry (oral radiology, oral surgery, and postgraduate training). Responses were scored as Yes = 1, No = 0. A total score >3 indicated good knowledge. Attitude subscale: Seven items assessed perceptions of AI’s importance in dentistry, curriculum integration, diagnostic support, role in specialties, potential to replace dentists, and burden on clinicians. Responses were rated on a 5-point Likert scale (Strongly Agree = 5 to Strongly Disagree = 1). A score >5 indicated a positive attitude. Practice subscale: Five assessed AI use in academics/clinical practice, ease of application, clinician’s role, and willingness to learn AI. Responses were Yes = 1, No/Never applied = 0. A score >2 indicated good practice. The questionnaire was pilot tested on 20 students for validity and reliability. The test re-test score ranged from 0.8 to 0.9 (Knowledge = 0.8, Attitude = 0.82 and practice = 0.9) and the Cronbach’s alpha score ranged from 0.78 to 0.9 (Knowledge = 0.78, Attitude = 0.84 and practice = 0.9) indicating acceptable internal consistency. Administration of the survey The questionnaire was distributed electronically via Google Forms to the official WhatsApp numbers registered with the college administration. The form included the survey title, purpose, consent statement, questionnaire items, and a thank-you note. Participants were given two days to respond. Non-respondents received up to two reminders at one-week intervals. Students who did not respond after the reminders were excluded from the study. Statistical analysis Data were analyzed using IBM SPSS Statistics version 25.0 (IBM Corp., Armonk, NY, USA). Descriptive statistics (mean, standard deviation, frequencies, and percentages) summarized responses. Pearson’s chi-square test and Fisher’s exact test compared categorical variables. Mann–Whitney U was used to compared non-parametric data. A binary logistic regression was used to identify demographic predictors of KAP scores. Results were reported as unadjusted odds ratios (ORs) with 95% confidence intervals (CIs). A p-value <0.05 was considered statistically significant. Results Out of 254 dental students invited to participate, 208 completed the survey, resulting in a response rate of 81.8%. The average age of participants was 24.29 ± 2.48 years, ranging from 21 to 45 years. The group consisted of 146 females (70.2%) and 62 males (29.8%). Among them, 120 were undergraduate (UG) students and 88 were postgraduate (PG) students. Specifically, the sample included 44 (36.5%) final-year BDS students, 76 (21.2%) house surgeons, 34 (16.3%) first-year MDS students, 30 (14.4%) second-year MDS students, and 24 (11.5%) third-year MDS students [ Table 1 ]. Table 1. Demographic characteristics of study participants. Variables N Percentages Mean ± SD Age 208 24.29 ± 2.48 Gender Female 146 70.2 Male 62 29.8 Class Final year BDS 44 21.2 Interns 76 36.5 1 st year MDS 34 16.3 2 nd year MDS 30 14.4 3 rd year MDS 24 11.5 Knowledge of AI When asked about their basic understanding of artificial intelligence (AI), 114 UG students (95%) and 85 PG students (96.6%) reported familiarity with the term. Awareness of AI subfields, such as machine learning and deep learning, was much higher among PG students (73; 83%) compared to UG students (32; 26.7%) (p = 0.001). Similarly, 80 PG students (90.9%) were familiar with the applications of AI in dentistry, compared to only 31 UG students (25.8%) (p = 0.004). However, when asked about specific uses of AI within dentistry, 68 PG students (77.3%) identified applications in oral radiology, while 58 (65.9%) recognized its role in oral surgery (p = 0.002). A majority of PG respondents (71; 80.6%) indicated that AI is not currently included in their curriculum [ Table 2 ]. Table 2. Comparison of knowledge on artificial intelligence between undergraduate and post-graduate students. Questions Response Undergraduate students Postgraduate students p-value Do you know what artificial intelligence is? Yes 114 (95%) 85 (96.6%) 0.577 No 6 (5%) 3 (3.4%) Do you know about machine learning and deep learning (subtypes of AI)? Yes 32 (26.7%) 73 (83%) 0.001 No 88 (73.3%) 15 (17%) Do you know about any application of AI in the dental field? Yes 31 (25.8%) 80 (90.9%) 0.004 No 89 (74.2%) 8 (9.1%) Do you know about the application of AI in oral radiology field? Yes 28 (23.4%) 68 (77.3%) 0.002 No 92 (76.6%) 20 (22.7%) Do you know about the application of AI in the oral surgery field? Yes 12 (10%) 58 (65.9%) 0.001 No 108 (90%) 30 (34.1%) If you are a PG student, does your training include a curriculum regarding AI? Yes - 7 (19.4%) No - 71 (80.6%) The overall mean knowledge score was 4.32 ± 1.79. No significant difference was found between genders (p = 0.177). PG students scored significantly higher (5.12 ± 1.67) than UG students (3.66 ± 1.60) (p = 0.000) [ Table 5 ]. Regardless of age and gender, AI knowledge levels were generally good. Final-year MDS students demonstrated particularly strong knowledge (p = 0.003) [ Table 6 ]. The regression analysis confirmed that the level of dental education is a significant predictor of AI knowledge, with final-year MDS students showing the highest level of understanding compared to other groups [ Table 7 ]. Table 3. Comparison of attitude on artificial intelligence between undergraduate and post-graduate students. Questions Response Undergraduate students Postgraduate students p-value Do you believe AI is essential in the dental field? Strongly agree 21 (17.5%) 37 (42%) 0.001 Agree 76 (63.3%) 37 (42%) Neutral 23 (19.2%) 12 (13.6%) Disagree 0 1 (1.1%) Strongly disagree 0 1 (1.1%) Do you think AI should be included in the curriculum in dental school as well as specialist training? Strongly agree 22 (18.3%) 28 (31.8%) 0.05 Agree 74 (61.7%) 47 (53.4%) Neutral 19 (15.8%) 10 (11.4%) Disagree 5 (4.2%) 1 (1.1%) Strongly disagree 0 2 (2.3%) Do you think that AI aids practitioners in early diagnosis and assessment of the severity of disease? Strongly agree 22 (18.3%) 19 (21.6%) 0.525 Agree 72 (60%) 51 (58%) Neutral 20 (16.7%) 17 (19.3%) Disagree 3 (2.5%) 1 (1.1%) Strongly disagree 3 (2.5%) 0 Do you believe that AI will replace physicians in the future? Strongly agree 11 (9.2%) 5 (5.7%) 0.184 Agree 29 (24.2%) 33 (37.5%) Neutral 36 (30%) 18 (20.5%) Disagree 35 (29.2%) 23 (26.1%) Strongly disagree 9 (7.5%) 9 (10.2%) Do you believe AI is very essential in the field of radiology? Strongly agree 22 (18.3%) 19 (21.6%) 0.271 Agree 73 (60.8%) 45 (51.1%) Neutral 19 (15.8%) 21 (23.9%) Disagree 3 (2.5%) 3 (3.4%) Strongly disagree 3 (2.5%) 0 Do You believe AI is essential in the field of oral surgery? Strongly agree 16 (13.3%) 14 (15.9%) 0.866 Agree 71 (59.2%) 49 (55.7%) Neutral 26 (21.7%) 22 (25%) Disagree 4 (3.3%) 2 (2.3%) Strongly disagree 3 (2.5%) 1 (1.1%) Do you believe AI would be a burden for practitioners? Strongly agree 10 (8.3%) 7 (8%) 0.081 Agree 27 (22.5%) 31 (35.2%) Neutral 40 (33.3%) 18 (20.5%) Disagree 39 (32.5%) 25 (28.4%) Strongly disagree 4 (3.3%) 7 (8%) Table 4. Comparison of practice of artificial intelligence between undergraduate and post-graduate students. Questions Response Undergraduate students Postgraduate students p-value Have you ever applied AI technology in any field? Yes 8 (6.7%) 54 (61.4%) 0.04 No 112 (93.3%) 34 (36.6%) Was it easy for you to apply AI? Yes 4 (3.3%) 18 (20.4%) 0.50 No 4 (3.3%) 36 (40.9%) Never applied 112 (93.3%) 34 (36.6%) Did AI make your task easy? Yes 7 (87.5%) 50 (92.6%) 0.014 No 1 (12.5%) 4 (7.4%) Clinician role is important in application and evaluation of AI in the dental field Yes 69 (57.5%) 65 (73.9%) 0.047 No 16 (13.3%) 6 (6.8%) Don’t know 35 (29.2%) 17 (19.3%) Would you like to work on AI in future? Yes 86 (71.7%) 74 (84.1%) 0.060 No 13 (10.8%) 8 (9.1%) Don’t know 21 (17.5%) 6 (6.8%) Table 5. Mean knowledge, attitude and practice scores and demographic details of study participants. Variables N Demographic details Mean ± SD p-value Mean knowledge of AI 62 Male 4.58 ± 1.82 0.177 146 Female 4.21 ± 1.78 120 Undergraduate students 3.66 ± 1.60 0.000 88 Postgraduate students 5.12 ± 1.67 Mean attitude of AI 62 Male 5.16 ± 1.73 0.014 146 Female 4.43 ± 2.00 120 Undergraduate students 4.55 ± 1.88 0.372 88 Postgraduate students 4.79 ± 2.05 Mean practice of AI 62 Male 3.51 ± 1.66 0.087 146 Female 3.06 ± 1.73 120 Undergraduate students 2.88 ± 1.78 0.002 88 Postgraduate students 3.63 ± 1.54 Table 6. Knowledge, attitude and practice scores and demographic details of study participants. Demographic details Variables p-value Knowledge of AI Age group Good N(%) Poor N(%) 0.269 21-25 years 92 (61.7%) 57 (38.3%) 26-30 years 42 (72.4%) 16 (27.6%) >31 years 1 (100%) 0 Gender 0.809 Male 41 (66.1%) 21 (33.9%) Female 94 (64.4%) 52 (35.6%) Course 0.003 Final year BDS 24 (54.5%) 20 (45.5%) Internship 41 (53.9%) 35 (46.1%) 1 st MDS 28 (82.4%) 6 (17.6%) 2 nd MDS 21 (70%) 9 (30%) 3 rd MDS 21 (87.5%) 3 (12.5%) Attitude of AI Age group Positive Negative 0.733 21-25 years 101 (67.8%) 48 (32.2%) 26-30 years 41 (70.7%) 17 (29.3%) >31 years 1 (100%) 0 Gender 0.037 Male 49 (79%) 13 (21%) Female 94 (64.4%) 52 (35.6%) Course 0.641 Final year BDS 28 (63.6%) 16 (36.4%) Internship 52 (68.4%) 24 (31.6%) 1 st MDS 25 (73.5%) 9 (26.5%) 2 nd MDS 19 (63.3%) 11 (36.7%) 3 rd MDS 19 (79.2%) 5 (20.8%) Practice of AI Age group Good Poor 0.516 21-25 years 71 (47.7%) 78 (52.3%) 26-30 years 30 (51.7%) 28 (48.3%) >31 years 1 (100%) 0 Gender 0.163 Male 35 (56.5%) 27 (43.5%) Female 67 (45.9%) 79 (54.1%) Course 0.001 Final year BDS 9 (20.5%) 35 (79.5%) Internship 40 (52.6%) 36 (47.4%) 1 st MDS 21 (61.8%) 13 (38.2%) 2 nd MDS 18 (60%) 12 (40%) 3 rd MDS 14 (58.3%) 10 (41.7%) Table 7. Binary logistic regression between knowledge, attitude, practice and demographic details of study participants. Demographic details Categories Odds ratio p-value Lower Upper Knowledge of AI Gender Female Reference Male 0.889 0.729 0.459 1.722 Class Final year BDS Reference Internship 0.154 0.001 0.010 0.296 1 st MDS 0.152 0.001 0.009 0.308 2 nd MDS 0.471 0.338 0.101 2.194 3 rd MDS 0.798 0.038 0.043 0.197 Attitude of AI Gender Female Reference Male 0.461 0.032 0.228 0.935 Class Final year BDS Reference Internship 0.570 0.320 0.188 1.725 1 st MDS 0.444 0.175 0.137 1.435 2 nd MDS 0.741 0.640 0.211 2.602 3 rd MDS 0.422 0.176 0.121 1.473 Practice of AI Gender Female Reference Male 0.606 0.123 0.321 1.145 Class Final year BDS Reference Internship 0.797 0.635 0.313 2.029 1 st MDS 0.177 0.002 0.059 0.534 2 nd MDS 1.171 0.773 0.401 3.423 3 rd MDS 1.033 0.953 0.344 3.103 Attitude toward AI Only a small proportion of students strongly agreed that AI is vital in dentistry 21 undergraduates (17.5%) and 37 postgraduates (42%) (p = 0.001). About 33.4% of UG students and 43.2 % of PG students agreed that AI would replace dentists in future. Similarly, 22 UG students (18.3%) and 28 PG students (31.8%) strongly agreed that AI should be incorporated into both dental school curricula and specialist training. A majority of participants 72 UG (60%) and 51 PG (58%) students agreed that AI supports practitioners in the early detection of disease and in assessing its severity. Very few, 11 UG (9.2%) and 5 PG (5.7%) students, strongly agreed that AI would eventually replace physicians. Most respondents recognized AI’s importance in specific specialties: 73 UG students (60.8%) and 45 PG students (51.1%) agreed that it is highly valuable in radiology, while 71 UG (59.2%) and 49 PG (55.7%) students agreed that it plays an essential role in oral surgery. Conversely, only 10 UG (8.3%) and 7 PG (8%) students strongly agreed that AI could be burdensome for practitioners [ Table 3 ]. The mean attitude score toward AI was 4.65 ± 1.95. A significant gender difference was observed, with male students showing a more favourable attitude (5.16 ± 1.73; p = 0.014). Regardless of age or academic level, the majority of participants expressed positive views about AI. Notably, 49 male respondents (79%) demonstrated a positive attitude, significantly higher compared to females (p = 0.037) [ Table 6 ]. Further analysis indicated that male gender is a significant predictor of positive attitudes toward AI [ Table 7 ]. Practice of AI More than half of the postgraduate (PG) respondents, 54 (61.4%), reported using AI technology compared to only 8 undergraduate (UG) students (6.7%) (p = 0.04). Among PG students, 36 (40.9%) reported difficulties in applying AI. Nevertheless, most of those who had used AI noted that it made tasks easier. A majority of PG students, 65 (73.9%), emphasized the critical role of clinicians in diagnosis and treatment, compared to 69 UG students (57.5%) (p = 0.047). Interest in learning more about AI was high among both groups, with 86 UG (71.7%) and 74 PG (84.1%) students expressing interest [ Table 4 ]. The overall mean practice score for AI was 3.20 ± 1.72. Male students had higher scores (3.51 ± 1.66) than females (3.06 ± 1.73), although the difference was not statistically significant (p = 0.087). PG students, however, achieved significantly higher practice scores (3.63 ± 1.54) compared to UG students (2.88 ± 1.78) (p = 0.002) [ Table 5 ]. Among participants aged 21–25 years, 78 (52.3%) had low practice scores, but the difference across age groups was not significant (p = 0.516). While 35 male students (56.5%) demonstrated good practice scores compared to female students, the difference remained statistically insignificant (p = 0.163). Importantly, PG students particularly those in the first year of MDS were identified as having significantly better practice of AI compared to other groups (p = 0.001) [ Table 6 , Table 7 ]. Discussion The key findings of this study indicate that most students were familiar with the term AI, postgraduate students had greater awareness of AI applications in medical and dental fields, a considerable proportion of undergraduate students found AI easier to apply compared to postgraduates, and the majority of respondents emphasized the indispensable role of clinicians in AI-assisted dentistry. These outcomes suggest that dental students remain in an exploratory phase, with a degree of uncertainty about adopting AI applications in their field. The gender distribution of participants in this survey (70.2% female, 29.8% male) aligns with previous reports by Murali et al. 17 (73% female, 27% male) and Elhijazi et al. 18 (71.4% female, 28.9% male). The highest participation was from interns (36.5%), while final-year MDS students were least represented (11.5%). By contrast, Murali et al. 17 reported lower participation from interns (19.3%) and higher involvement of postgraduates (20.7%), whereas Elchaghaby et al. found most respondents were fifth-year students (53%) and fewest were in their third year (21%). 19 In the present study, 95.6% of participants had basic knowledge of AI, closely matching Murali et al.’s findings (94%). Awareness of machine learning and deep learning was higher among PG students (82.9%), likely due to the integration of AI into clinical practice and increased exploration of new technologies through social media and smartphones. Supporting this, Yüzbaşıoğlu 20 reported that 76.1% of students learned about AI from social media, while Aldowah et al. 21 found similar results (78%). Compared with medical students in Syria 16 (34.7%) and Pakistan 22 (35.3%), our respondents demonstrated higher knowledge of AI subtypes. However, only 25.8% of UG students knew about AI’s applications in dentistry, versus 90.9% of PG students who were aware of its role in healthcare. This disparity reflects the limited academic and clinical exposure of undergraduates to AI, underscoring the need for curriculum integration. Medical studies from Syria 16 (87.4%), Pakistan 20 (74.4%), and the UK 23 (78%) further reinforce AI’s perceived importance in healthcare. Similarly, 77.3% of our PG students acknowledged its role in oral radiology, consistent with reports from Pakistan 22 (74%) and Syria 16 (73%). Regarding future implications, 33.4% of UG and 43.2% of PG students believed AI might replace dentists. These findings contrast with Jeong’s study 24 where 72% disagreed with this notion, but align with Elhijazi 18 where 53.7% considered replacement possible. The higher proportion in our study may stem from misconceptions about AI’s scope and limitations in dentistry. Nonetheless, most participants (77.8%) agreed that AI supports early diagnosis and disease severity assessment, echoing global trends. AI systems have already demonstrated their value in enhancing diagnostic accuracy and reducing human error. Additionally, more than half of respondents (57.7%) acknowledged AI’s importance in oral surgery, a view shared by students in Turkey, 20 Saudi Arabia, 25 and India. 26 Interestingly, 36% of students felt AI could become a burden for practitioners. While AI is revolutionizing decision-making and personalized treatment, its integration also raises concerns regarding job displacement, information overload, and potential erosion of clinical skills if overreliance occurs. To prevent disruption, AI should be positioned as a supportive tool rather than a substitute in oral healthcare. Overall, the findings show moderate baseline knowledge of AI, broad acceptance of its inclusion in the curriculum, and a generally positive attitude toward its use in dentistry. Students also demonstrated enthusiasm for future learning. Therefore, we recommend hands on workshops, seminars, and training programs on machine learning and deep learning in clinical dentistry. This would enable future dentists to harness AI effectively, reducing errors and enhancing treatment quality. Addressing identified knowledge gaps will require expanding AI training within dental education to eliminate misconceptions and prepare these students for the future of digital dentistry. Limitations This study has several limitations. First, its cross-sectional design captures responses at a single point in time and therefore does not allow causal inferences. Second, data were collected from a single institution, which may limit the generalizability of the findings to other regions of India. Nonetheless, internal validity was strengthened through the use of a validated questionnaire, a high response rate, and inclusion of both undergraduate and postgraduate students across all academic levels. Future research should include multi-institutional studies with larger and more diverse samples. Longitudinal designs are also recommended to assess how exposure to AI training influences the development of students’ knowledge, attitudes, and competencies over time. Conclusion A significant proportion of dental students in this study demonstrated basic knowledge of AI and a positive attitude toward incorporating it into the dental curriculum. Students recognized its value in dental applications and expressed interest in future learning. Universities and policymakers should collaborate to integrate AI training into dental education, equipping future dentists to utilize AI in routine practice and ultimately improve the quality of dental healthcare. Ethics and consent The protocol was approved by the Institutional Review Board of Bapuji Dental College and Hospital, India (Approval number: ECR/1652/Inst/KA/2022/24-04/08-007). The Committee on Research Ethics followed internationally recognized guidelines for the protection of human subjects, including the Declaration of Helsinki, the Belmont Report, and CIOMS principles. Prior to participation, all individuals were provided with a clear explanation of the study and gave informed consent electronically. The consent form described the study’s purpose, estimated completion time, and was written in simple, accessible language. Participation was entirely voluntary, and responses were collected anonymously. Participants were informed of their right to withdraw at any point, and assured that their personal information would remain confidential. Access to the dataset was restricted to the principal investigator. Data availability statement Underlying data Figshare – Knowledge, Attitudes, and Practices of Artificial Intelligence in Dentistry: a cross-sectional survey . https://doi.org/10.6084/m9.figshare.30513776.v1 . 27 This project contains following underlying data: • study-data.xlsx Data are available under the terms of the Creative Commons Zero “No rights reserved” data waiver (CC BY 4.0 Public domain dedication). Extended data Figshare - Knowledge, Attitudes, and Practices of Artificial Intelligence in Dentistry: a cross-sectional survey (Questionnaire), https://doi.org/10.6084/m9.figshare.30513803.v1 . 28 This project contains following extended data • Survey questionnaire, participant information and consent form.docx Data are available under the terms of the Creative Commons Zero “No rights reserved” data waiver (CC BY 4.0 Public domain dedication). References 1. 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Publisher Full Text Comments on this article Comments (0) Version 2 VERSION 2 PUBLISHED 26 Nov 2025 ADD YOUR COMMENT Comment Author details Author details 1 Public Health Dentistry, Bapuji Dental College and Hospital, Davangere, Karnataka, 577004, India 2 Public Health Dentistry, Terna Dental College and Hospital, Nerul, Navi Mumbai, Maharashtra, 400706, India 3 Preventive Dental Sciences, Prince Sattam bin Abdulaziz University College of Dentistry, Al Kharj, Riyadh Province, 11942, Saudi Arabia 4 Prosthodontics, Prince Sattam bin Abdulaziz University College of Dentistry, Al Kharj, Riyadh Province, 11942, Saudi Arabia Usha GV Roles: Conceptualization, Formal Analysis, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing Bhuvaneshwari Nadar Roles: Software, Validation, Visualization, Writing – Review & Editing Sultan Almalki Roles: Data Curation, Funding Acquisition, Project Administration, Supervision, Writing – Review & Editing Tushar Bhagat Roles: Visualization, Writing – Review & Editing Inderjit M. Gowdar Roles: Funding Acquisition, Project Administration, Supervision, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information The authors extend their appreciation to Prince Sattam bin Abdulaziz University for funding this research work through the project number (PSAU/2025/03/33409). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Article Versions (2) version 2 Revised Published: 28 Jan 2026, 14:1314 https://doi.org/10.12688/f1000research.173028.2 version 1 Published: 26 Nov 2025, 14:1314 https://doi.org/10.12688/f1000research.173028.1 Copyright © 2025 GV U et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article GV U, Nadar B, Almalki S et al. Knowledge, Attitudes, and Practices of Artificial Intelligence in Dentistry: A cross-sectional survey [version 1; peer review: 2 approved, 1 approved with reservations] . F1000Research 2025, 14 :1314 ( https://doi.org/10.12688/f1000research.173028.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 1 VERSION 1 PUBLISHED 26 Nov 2025 Views 0 Cite How to cite this report: Gangadharappa DP. Reviewer Report For: Knowledge, Attitudes, and Practices of Artificial Intelligence in Dentistry: A cross-sectional survey [version 1; peer review: 2 approved, 1 approved with reservations] . F1000Research 2025, 14 :1314 ( https://doi.org/10.5256/f1000research.190802.r436907 ) The direct URL for this report is: https://f1000research.com/articles/14-1314/v1#referee-response-436907 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 07 Jan 2026 Dr Praveen Gangadharappa , Jazan University, Jazan, Saudi Arabia Approved VIEWS 0 https://doi.org/10.5256/f1000research.190802.r436907 1. Incorrect interpretation of logistic regression (Table 7) In Table 7 , odds ratios (ORs) < 1 indicate lower odds compared to reference , but the manuscript interprets some of these incorrectly. Examples: Knowledge of AI ... Continue reading READ ALL 1. Incorrect interpretation of logistic regression (Table 7) In Table 7 , odds ratios (ORs) < 1 indicate lower odds compared to reference , but the manuscript interprets some of these incorrectly. Examples: Knowledge of AI Internship OR = 0.154 , p = 0.001 1st MDS OR = 0.152 , p = 0.001 Yet the text states: “Final-year MDS students demonstrated particularly strong knowledge…” 2. Mismatch between mean scores and categorical KAP classification You use: Mean scores (Table 5) Dichotomized “Good / Poor” KAP scores (Tables 6 & 7) But cut-offs are not statistically justified . Example: Knowledge score range = 0–6 “Good knowledge” defined as >3 Yet: Mean knowledge = 4.32 ± 1.79 Still, ~35% are classified as poor 3. Overstatement in conclusion The conclusion states: “A significant proportion of dental students demonstrated basic knowledge of AI…” But: UG students had low application awareness (25.8%) Practice scores were poor in more than half 4. Single-institution sample limits generalizability You mention this in limitations, but the Abstract and Conclusion still generalize to “dental students in India.” 5. Practice results wording Statement: “A considerable proportion of undergraduate students found AI easier to apply compared to postgraduates” But: Only 4 UG students reported ease Most UG never applied AI 6. Terminology consistency “Physicians” is used instead of dentists in some attitude questions and discussion. This creates conceptual ambiguity. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? I cannot comment. A qualified statistician is required. Are all the source data underlying the results available to ensure full reproducibility? No source data required Are the conclusions drawn adequately supported by the results? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: clinical research, I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Gangadharappa DP. Reviewer Report For: Knowledge, Attitudes, and Practices of Artificial Intelligence in Dentistry: A cross-sectional survey [version 1; peer review: 2 approved, 1 approved with reservations] . F1000Research 2025, 14 :1314 ( https://doi.org/10.5256/f1000research.190802.r436907 ) The direct URL for this report is: https://f1000research.com/articles/14-1314/v1#referee-response-436907 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Kukreja P. Reviewer Report For: Knowledge, Attitudes, and Practices of Artificial Intelligence in Dentistry: A cross-sectional survey [version 1; peer review: 2 approved, 1 approved with reservations] . F1000Research 2025, 14 :1314 ( https://doi.org/10.5256/f1000research.190802.r436900 ) The direct URL for this report is: https://f1000research.com/articles/14-1314/v1#referee-response-436900 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 11 Dec 2025 Pankaj Kukreja , Faculty of dentistry, Department of Biomedical Dental Sciences, Al Baha University (Ringgold ID: 158203), Al Bahah, Al Bahah Province, Saudi Arabia Approved VIEWS 0 https://doi.org/10.5256/f1000research.190802.r436900 The article is well written. Appropriate methodology has been followed. The article effectively spans radiology and pathology domains of dentistry related to understanding of both postgraduate and undergraduate students and ensures that readers gain an understanding of AI’s impact on ... Continue reading READ ALL The article is well written. Appropriate methodology has been followed. The article effectively spans radiology and pathology domains of dentistry related to understanding of both postgraduate and undergraduate students and ensures that readers gain an understanding of AI’s impact on the students. The authors not only summarize existing studies but also highlight limitations which adds depth and balance to the discussion section. The discussion provides valuable guidance for future research. While the article is already strong, future iterations could explore patient perspectives on AI integration in dentistry to enrich the human-centered dimension. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: Oral and Maxillofacial surgery, Dental implantology I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Kukreja P. Reviewer Report For: Knowledge, Attitudes, and Practices of Artificial Intelligence in Dentistry: A cross-sectional survey [version 1; peer review: 2 approved, 1 approved with reservations] . F1000Research 2025, 14 :1314 ( https://doi.org/10.5256/f1000research.190802.r436900 ) The direct URL for this report is: https://f1000research.com/articles/14-1314/v1#referee-response-436900 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Kumar Bijai L. Reviewer Report For: Knowledge, Attitudes, and Practices of Artificial Intelligence in Dentistry: A cross-sectional survey [version 1; peer review: 2 approved, 1 approved with reservations] . F1000Research 2025, 14 :1314 ( https://doi.org/10.5256/f1000research.190802.r436903 ) The direct URL for this report is: https://f1000research.com/articles/14-1314/v1#referee-response-436903 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 11 Dec 2025 Laliytha Kumar Bijai , King Saud bin Abdulaziz University for Health Sciences, Riyadh, Riyadh Province, Saudi Arabia Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.190802.r436903 The manuscript is well written. However, it would be more plausible to readers if the following were addressed. 1. One part of the result does not coincide with the first line of the discussion. In the results section, ... Continue reading READ ALL The manuscript is well written. However, it would be more plausible to readers if the following were addressed. 1. One part of the result does not coincide with the first line of the discussion. In the results section, only 6.7% of UGs had ever used AI (vs. 61.4% of PGs). Can you justify why, in the discussion section, it is mentioned that the study reports that UGs found AI “easier to apply” than PGs? This could be either a misunderstanding of the question item or ambiguity in how “ease of application” was measured. 2. The limitation clearly states that the KAP study cannot be generalized as it was done only in one institution. So it is fair to modify the sentence of recommendation to Universities and policymakers. Instead, you can conclude that only if we have multi-institutional studies can we make a substantial recommendation to the policymakers. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly Competing Interests: No competing interests were disclosed. Reviewer Expertise: Oral Cancer, Oral potentially malignant disorders, prediction of malignant transformation, I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Kumar Bijai L. Reviewer Report For: Knowledge, Attitudes, and Practices of Artificial Intelligence in Dentistry: A cross-sectional survey [version 1; peer review: 2 approved, 1 approved with reservations] . F1000Research 2025, 14 :1314 ( https://doi.org/10.5256/f1000research.190802.r436903 ) The direct URL for this report is: https://f1000research.com/articles/14-1314/v1#referee-response-436903 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Comments on this article Comments (0) Version 2 VERSION 2 PUBLISHED 26 Nov 2025 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 3 Version 2 (revision) 28 Jan 26 read Version 1 26 Nov 25 read read read Laliytha Kumar Bijai , King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia Pankaj Kukreja , Al Baha University (Ringgold ID: 158203), Al Bahah, Saudi Arabia Dr Praveen Gangadharappa , Jazan University, Jazan, Saudi Arabia Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Gangadharappa D. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 06 Feb 2026 | for Version 2 Dr Praveen Gangadharappa , Jazan University, Jazan, Saudi Arabia 0 Views copyright © 2026 Gangadharappa D. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions no changes required Competing Interests No competing interests were disclosed. Reviewer Expertise clinical research, I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Gangadharappa DP. Peer Review Report For: Knowledge, Attitudes, and Practices of Artificial Intelligence in Dentistry: A cross-sectional survey [version 1; peer review: 2 approved, 1 approved with reservations] . F1000Research 2025, 14 :1314 ( https://doi.org/10.5256/f1000research.195508.r453481) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-1314/v2#referee-response-453481 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Gangadharappa D. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 07 Jan 2026 | for Version 1 Dr Praveen Gangadharappa , Jazan University, Jazan, Saudi Arabia 0 Views copyright © 2026 Gangadharappa D. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions 1. Incorrect interpretation of logistic regression (Table 7) In Table 7 , odds ratios (ORs) < 1 indicate lower odds compared to reference , but the manuscript interprets some of these incorrectly. Examples: Knowledge of AI Internship OR = 0.154 , p = 0.001 1st MDS OR = 0.152 , p = 0.001 Yet the text states: “Final-year MDS students demonstrated particularly strong knowledge…” 2. Mismatch between mean scores and categorical KAP classification You use: Mean scores (Table 5) Dichotomized “Good / Poor” KAP scores (Tables 6 & 7) But cut-offs are not statistically justified . Example: Knowledge score range = 0–6 “Good knowledge” defined as >3 Yet: Mean knowledge = 4.32 ± 1.79 Still, ~35% are classified as poor 3. Overstatement in conclusion The conclusion states: “A significant proportion of dental students demonstrated basic knowledge of AI…” But: UG students had low application awareness (25.8%) Practice scores were poor in more than half 4. Single-institution sample limits generalizability You mention this in limitations, but the Abstract and Conclusion still generalize to “dental students in India.” 5. Practice results wording Statement: “A considerable proportion of undergraduate students found AI easier to apply compared to postgraduates” But: Only 4 UG students reported ease Most UG never applied AI 6. Terminology consistency “Physicians” is used instead of dentists in some attitude questions and discussion. This creates conceptual ambiguity. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? I cannot comment. A qualified statistician is required. Are all the source data underlying the results available to ensure full reproducibility? No source data required Are the conclusions drawn adequately supported by the results? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise clinical research, I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Gangadharappa DP. Peer Review Report For: Knowledge, Attitudes, and Practices of Artificial Intelligence in Dentistry: A cross-sectional survey [version 1; peer review: 2 approved, 1 approved with reservations] . F1000Research 2025, 14 :1314 ( https://doi.org/10.5256/f1000research.190802.r436907) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-1314/v1#referee-response-436907 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Kukreja P. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 11 Dec 2025 | for Version 1 Pankaj Kukreja , Faculty of dentistry, Department of Biomedical Dental Sciences, Al Baha University (Ringgold ID: 158203), Al Bahah, Al Bahah Province, Saudi Arabia 0 Views copyright © 2025 Kukreja P. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The article is well written. Appropriate methodology has been followed. The article effectively spans radiology and pathology domains of dentistry related to understanding of both postgraduate and undergraduate students and ensures that readers gain an understanding of AI’s impact on the students. The authors not only summarize existing studies but also highlight limitations which adds depth and balance to the discussion section. The discussion provides valuable guidance for future research. While the article is already strong, future iterations could explore patient perspectives on AI integration in dentistry to enrich the human-centered dimension. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise Oral and Maxillofacial surgery, Dental implantology I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Kukreja P. Peer Review Report For: Knowledge, Attitudes, and Practices of Artificial Intelligence in Dentistry: A cross-sectional survey [version 1; peer review: 2 approved, 1 approved with reservations] . F1000Research 2025, 14 :1314 ( https://doi.org/10.5256/f1000research.190802.r436900) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-1314/v1#referee-response-436900 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Kumar Bijai L. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 11 Dec 2025 | for Version 1 Laliytha Kumar Bijai , King Saud bin Abdulaziz University for Health Sciences, Riyadh, Riyadh Province, Saudi Arabia 0 Views copyright © 2025 Kumar Bijai L. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The manuscript is well written. However, it would be more plausible to readers if the following were addressed. 1. One part of the result does not coincide with the first line of the discussion. In the results section, only 6.7% of UGs had ever used AI (vs. 61.4% of PGs). Can you justify why, in the discussion section, it is mentioned that the study reports that UGs found AI “easier to apply” than PGs? This could be either a misunderstanding of the question item or ambiguity in how “ease of application” was measured. 2. The limitation clearly states that the KAP study cannot be generalized as it was done only in one institution. So it is fair to modify the sentence of recommendation to Universities and policymakers. Instead, you can conclude that only if we have multi-institutional studies can we make a substantial recommendation to the policymakers. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly Competing Interests No competing interests were disclosed. Reviewer Expertise Oral Cancer, Oral potentially malignant disorders, prediction of malignant transformation, I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (0) Kumar Bijai L. Peer Review Report For: Knowledge, Attitudes, and Practices of Artificial Intelligence in Dentistry: A cross-sectional survey [version 1; peer review: 2 approved, 1 approved with reservations] . F1000Research 2025, 14 :1314 ( https://doi.org/10.5256/f1000research.190802.r436903) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-1314/v1#referee-response-436903 Alongside their report, reviewers assign a status to the article: Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. 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