Harnessing the potential of large language models in medical education: promise and pitfalls
Open accessAbstract
Objectives: To provide balanced consideration of the opportunities and challenges associated with integrating Large Language Models (LLMs) throughout the medical school continuum. Process: Narrative review of published literature contextualized by current reports of LLM application in medical education. Conclusions: LLMs like OpenAI’s ChatGPT can potentially revolutionize traditional teaching methodologies. LLMs offer several potential advantages to students, including direct access to vast information, facilitation of personalized learning experiences, and enhancement of clinical skills development. For faculty and instructors, LLMs can facilitate innovative approaches to teaching complex medical concepts and fostering student engagement. Notable challenges of LLMs integration include the risk of fostering academic misconduct, inadvertent overreliance on AI, potential dilution of critical thinking skills, concerns regarding the accuracy and reliability of LLM-generated content, and the possible implications on teaching staff.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The quality and difficulty of GPT-4 generated USMLE questions often fall below expectations.
“Lastly, the quality and difficulty of the questions often do not meet expectations (Artsi et al., 2024; Benítez et al., 2024) (Limit3).”
From MCQG-SRefine: Multiple Choice Question Generation and Evaluation with Iterative Self-Critique, Correction, and Comparison Feedback (2025) · cites this paper as Benítez et al., 2024
The Board exam preparation section says ChatGPT-generated USMLE-style questions were generally lower in complexity and not advanced enough to serve as a stand-alone question bank. That supports the concern about question difficulty and adequacy, but the paper does not identify the question-generating model as GPT-4. (LLM, conf=0.86)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
ChatGPT for good? On opportunities and challenges of large language models for education
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., et al. (2023) · Learning and Individual Differences · doi:10.1016/j.lindif.2023.102274 · Cited by 7,326 · see citing works
Kung, T. H., Cheatham, M., Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., et al. (2023) · PLOS Digital Health · doi:10.1371/journal.pdig.0000198 · Cited by 4,115 · see citing works
Large language models in medicine
Thirunavukarasu, A. J., Ting, D. S. J., Elangovan, K., Gutierrez, L., Tan, T. F., & Ting, D. S. W. (2023) · Nature Medicine · doi:10.1038/s41591-023-02448-8 · Cited by 3,944 · see citing works
Foundation models for generalist medical artificial intelligence
Moor, M., Banerjee, O., Abad, Z. S. H., Krumholz, H. M., Leskovec, J., Topol, E. J., & Rajpurkar, P. (2023) · Nature · doi:10.1038/s41586-023-05881-4 · Cited by 2,028 · see citing works
Benefits, Limits, and Risks of GPT-4 as an AI Chatbot for Medicine
Lee, P., Bubeck, S., & Petro, J. (2023) · New England Journal of Medicine · doi:10.1056/nejmsr2214184 · Cited by 1,738 · see citing works
Artificial hallucinations in ChatGPT: implications in scientific writing
Alkaissi, H., & McFarlane, S. I. (2023) · Cureus · doi:10.7759/cureus.35179 · Cited by 1,540 · see citing works
Allocation of Physician Time in Ambulatory Practice: A Time and Motion Study in 4 Specialties
Sinsky, C., Colligan, L., Li, L., Prgomet, M., Reynolds, S., Goeders, L., Westbrook, J., Tutty, M., & Blike, G. (2016) · Annals of Internal Medicine · doi:10.7326/m16-0961 · Cited by 1,494 · see citing works
Artificial Intelligence and Machine Learning in Clinical Medicine, 2023
Haug, C. J., & Drazen, J. M. (2023) · New England Journal of Medicine · doi:10.1056/nejmra2302038 · Cited by 1,215 · see citing works
Using Technology to Maintain the Education of Residents During the COVID-19 Pandemic
Chick, R. C., Clifton, G. T., Peace, K. M., Propper, B. W., Hale, D. F., Alseidi, A. A., & Vreeland, T. J. (2020) · Journal of surgical education · doi:10.1016/j.jsurg.2020.03.018 · Cited by 1,086 · see citing works
Outliers: The Story of Success
Jackson, F. R. (2009) · Multicultural Perspectives · doi:10.1080/15210960903028818 · Cited by 1,061 · see citing works
The future landscape of large language models in medicine
Clusmann, J., Kolbinger, F. R., Muti, H. S., Carrero, Z. I., Eckardt, J.-N., Laleh, N. G., Löffler, C. M. L., Schwarzkopf, S.-C., Unger, M., Veldhuizen, G. P., et al. (2023) · Communications Medicine · doi:10.1038/s43856-023-00370-1 · Cited by 1,045 · see citing works
Summary of ChatGPT-Related research and perspective towards the future of large language models
Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., He, H., Li, A., He, M., Liu, Z., et al. (2023) · Meta-Radiology · doi:10.1016/j.metrad.2023.100017 · Cited by 855 · see citing works
Eysenbach, G. (2023) · JMIR Medical Education · doi:10.2196/46885 · Cited by 840 · see citing works
Introducing artificial intelligence training in medical education
Paranjape, K., Schinkel, M., Nannan Panday, R., Car, J., & Nanayakkara, P. (2019) · JMIR Medical Education · doi:10.2196/16048 · Cited by 634 · see citing works
ChatGPT - Reshaping medical education and clinical management
Khan, R. A., Jawaid, M., Khan, A. R., & Sajjad, M. (2023) · Pakistan Journal of Medical Sciences · doi:10.12669/pjms.39.2.7653 · Cited by 628 · see citing works
Using ChatGPT to write patient clinic letters
Ali, S. R., Dobbs, T. D., Hutchings, H. A., & Whitaker, I. S. (2023) · The Lancet Digital Health · doi:10.1016/s2589-7500(23)00048-1 · Cited by 375 · see citing works
Using large language models in psychology
Demszky, D., Yang, D., Yeager, D. S., Bryan, C. J., Clapper, M., Chandhok, S., Eichstaedt, J. C., Hecht, C., Jamieson, J., Johnson, M., et al. (2023) · Nature Reviews Psychology · doi:10.1038/s44159-023-00241-5 · Cited by 369 · see citing works
AI-Generated Medical Advice—GPT and Beyond
Haupt, C. E., & Marks, M. (2023) · JAMA · doi:10.1001/jama.2023.5321 · Cited by 346 · see citing works
The Inevitable Reimagining of Medical Education
Emanuel, E. (2020) · JAMA · doi:10.1001/jama.2020.1227 · Cited by 324 · see citing works
Can ChatGPT draft a research article? An example of population-level vaccine effectiveness analysis
Macdonald, C., Adeloye, D., Sheikh, A., & Rudan, I. (2023) · Journal of Global Health · doi:10.7189/jogh.13.01003 · Cited by 243 · see citing works
Could AI help you to write your next paper?
Hutson, M. (2022) · Nature · doi:10.1038/d41586-022-03479-w · Cited by 236 · see citing works
The Role of Large Language Models in Medical Education: Applications and Implications
Safranek, C. W., Sidamon-Eristoff, A. E., Gilson, A., & Chartash, D. (2023) · JMIR Medical Education · doi:10.2196/50945 · Cited by 233 · see citing works
McGaghie, W. C., Cohen, E. R., & Wayne, D. B. (2010) · Academic Medicine · doi:10.1097/acm.0b013e3181ffacdb · Cited by 201 · see citing works
ChatGPT: promise and challenges for deployment in low- and middle-income countries
Wang, X., Sanders, H. M., Liu, Y., Seang, K., Tran, B. X., Atanasov, A. G., Qiu, Y., Tang, S., Car, J., Wang, Y. X., et al. (2023) · The Lancet Regional Health - Western Pacific · doi:10.1016/j.lanwpc.2023.100905 · Cited by 143 · see citing works
Self-citationTime Spent on Clinical Documentation
Oxentenko, A. S. (2010) · Archives of Internal Medicine · doi:10.1001/archinternmed.2009.534 · Cited by 139 · see citing works
The self-regulated learning of medical students in the clinical environment – a scoping review
Cho, K. K., Marjadi, B., Langendyk, V., & Hu, W. (2017) · BMC Medical Education · doi:10.1186/s12909-017-0956-6 · Cited by 133 · see citing works
Where did the day go? A time-motion study of hospitalists
Tipping, M. D., Forth, V. E., O'Leary, K. J., Malkenson, D. M., Magill, D. B., Englert, K., & Williams, M. V. (2010) · Journal of Hospital Medicine · doi:10.1002/jhm.790 · Cited by 127 · see citing works
Medical student use of digital learning resources
Scott, K. M., Morris, A., & Marais, B. J. (2017) · The Clinical Teacher · doi:10.1111/tct.12630 · Cited by 115 · see citing works
Ng, F. Y. C., Thirunavukarasu, A. J., Cheng, H., Tan, T. F., Gutierrez, L., Lan, Y., Ong, J. C. L., Chong, Y. S., Ngiam, K. Y., Ho, D., et al. (2023) · Cell Reports Medicine · doi:10.1016/j.xcrm.2023.101230 · Cited by 113 · see citing works
Self-citationRevisiting the Core Entrustable Professional Activities for Entering Residency
Amiel, J. M., Andriole, D. A., Biskobing, D. M., Brown, D. R., Cutrer, W. B., Emery, M. T., Mejicano, G. C., Ryan, M. S., Swails, J. L., & Wagner, D. P. (2021) · Academic Medicine · doi:10.1097/acm.0000000000004088 · Cited by 107 · see citing works
Ten tips for overcoming language barriers in science
Amano, T., Rojas, C. J. R., Boum, Y., Calvo, M., & Misra, B. B. (2021) · Nature Human Behaviour · doi:10.1038/s41562-021-01137-1 · Cited by 104 · see citing works
Medical student preferences for self‐directed study resources in gross anatomy
Choi-Lundberg, D. L., Low, T. F., Patman, P., Turner, P. A., & Sinha, S. N. (2015) · Anatomical Sciences Education · doi:10.1002/ase.1549 · Cited by 86 · see citing works
Research in Medical School: A Survey Evaluating Why Medical Students Take Research Years
Pathipati, A. S., & Taleghani, N. (2016) · Cureus · doi:10.7759/cureus.741 · Cited by 80 · see citing works
Proof of Concept: Using ChatGPT to Teach Emergency Physicians How to Break Bad News
Webb, J. J. (2023) · Cureus · doi:10.7759/cureus.38755 · Cited by 80 · see citing works
Varas, J., Coronel, B. V., Villagrán, I., Escalona, G., HERNANDEZ, R., Schuit, G., Espinoza, V. D., Lagos, A., Jarry, C., Neyem, A., et al. (2023) · Revista do Colégio Brasileiro de Cirurgiões · doi:10.1590/0100-6991e-20233605-en · Cited by 63 · see citing works
Beware of references when using ChatGPT as a source of information to write scientific articles
Sanchez-Ramos, L., Lin, L., & Romero, R. (2023) · American Journal of Obstetrics and Gynecology · doi:10.1016/j.ajog.2023.04.004 · Cited by 60 · see citing works
Ikonne, U. S., Campbell, A. M., Whelihan, K., Bay, R. C., & Lewis, J. H. (2018) · Journal of Osteopathic Medicine · doi:10.7556/jaoa.2018.174 · Cited by 50 · see citing works
Chang, C., Colón-Berlingeri, M., Mavis, B., Laird-Fick, H. S., Parker, C., & Solomon, D. (2021) · Academic Medicine · doi:10.1097/acm.0000000000003766 · Cited by 49 · see citing works
Working with Standardized Patients: A Primer
Bosek, M. S. D., Li, S., & Hicks, F. D. (2007) · International Journal of Nursing Education Scholarship · doi:10.2202/1548-923x.1437 · Cited by 46 · see citing works
Emerging Roles of Virtual Patients in the Age of AI
Combs, C. D., & Combs, P. F. (2019) · The AMA Journal of Ethic · doi:10.1001/amajethics.2019.153 · Cited by 46 · see citing works
Cost-Effectiveness of Using Standardized Patients to Assess Student-Pharmacist Communication Skills
Gillette, C., Stanton, R. B., Rockich‐Winston, N., Rudolph, M. J., & Anderson, H. G. (2017) · American Journal of Pharmaceutical Education · doi:10.5688/ajpe6120 · Cited by 41 · see citing works
Evaluation of a Theory-Based Virtual Counseling Application in Nursing Education
Shorey, S., Ang, E. N. K., Ng, E. D., Yap, J. Y. G., Lau, L. S. T., Chui, C. K., & Chan, Y. H. (2023) · CIN Computers Informatics Nursing · doi:10.1097/cin.0000000000000999 · Cited by 37 · see citing works
The Cost of Board Examination and Preparation: An Overlooked Factor in Medical Student Debt
Bhatnagar, V., Díaz, S., & Bucur, P. A. (2019) · Cureus · doi:10.7759/cureus.4168 · Cited by 34 · see citing works
Desai, A. P., Hegde, A. M., & Das, D. G. (2020) · JAMA · doi:10.1001/jama.2020.2956 · Cited by 30 · see citing works
Kelly, M., & Murphy, A. W. (2004) · Medical Teacher · doi:10.1080/01421590400005475 · Cited by 28 · see citing works
Documentation as composing: how medical students and residents use writing to think and learn
Bowker, D., Torti, J. M. I., & Goldszmidt, M. A. (2022) · Advances in Health Sciences Education · doi:10.1007/s10459-022-10167-x · Cited by 28 · see citing works
Communication training for pharmacy students with standard patients using artificial intelligence
Nakagawa, N., Odanaka, K., Ohara, H., & Kisara, S. (2022) · Currents in Pharmacy Teaching and Learning · doi:10.1016/j.cptl.2022.06.021 · Cited by 28 · see citing works
Large Language Models and Their Implications on Medical Education
Bair, H., & Norden, J. G. (2023) · Academic Medicine · doi:10.1097/acm.0000000000005265 · Cited by 25 · see citing works
Prober, C. G., & Desai, S. V. (2023) · Academic Medicine · doi:10.1097/acm.0000000000005262 · Cited by 12 · see citing works
Pivoting during a Pandemic: Reimagining Intern Orientation EPA-Based OSCE
Suneja, M., Thoma, K. D., Franklin, E., & Rowat, J. A. (2023) · Southern Medical Journal · doi:10.14423/smj.0000000000001525 · Cited by 4 · see citing works
Nelson, G., Swenson, J., Kozmenko, V., & Thompson, P. (2022) · Aesculapius · doi:10.70657/ahsm.v3.2022.01 · Cited by 3 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Large Language Models in Healthcare and Medical Applications: A Review
Maity, S., & Saikia, M. J. (2025) · Bioengineering · doi:10.3390/bioengineering12060631 · Cited by 249 · see citing works
Vrdoljak, J., Boban, Z., Vilović, M., Kumrić, M., & Božić, J. (2025) · Healthcare · doi:10.3390/healthcare13060603 · Cited by 179 · see citing works
Kıyak, Y. S., & Emekli, E. (2024) · Postgraduate Medical Journal · doi:10.1093/postmj/qgae065 · Cited by 139 · see citing works
Large language models in biomedicine and health: current research landscape and future directions
Lu, Z., Peng, Y., Cohen, T., Ghassemi, M., Weng, C., & Tian, S. (2024) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocae202 · Cited by 92 · see citing works
Mess, S. A., Mackey, A., & Yarowsky, D. (2025) · Plastic & Reconstructive Surgery Global Open · doi:10.1097/gox.0000000000006450 · Cited by 82 · see citing works
Large Language Models in Worldwide Medical Exams: Platform Development and Comprehensive Analysis
Zong, H., Wu, R., Cha, J., Wang, J., Wu, E., Li, J., Zhou, Y. K., Zhang, C., Feng, W.-Z., & Shen, B. (2024) · Journal of Medical Internet Research · doi:10.2196/66114 · Cited by 84 · see citing works
Claman, D. B., & Sezgın, E. (2024) · JMIR Medical Education · doi:10.2196/52346 · Cited by 95 · see citing works
Guo, S., Liu, D., Fang, X., Meng, Y. G., Zhou, Z.-Z., Li, J., Li, M., Luo, L. Q., Li, H.-L., Cai, X.-Y., et al. (2025) · International Journal of Surgery · doi:10.1097/js9.0000000000003668 · Cited by 73 · see citing works
Wang, H., Wang, C., Chen, Z., Liu, F., Bao, C., & Xu, X. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13487-8 · Cited by 77 · see citing works
Kooli, C., & Yusuf, N. (2024) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2024.2338330 · Cited by 63 · see citing works
Large Language Models as Tools to Generate Radiology Board-Style Multiple-Choice Questions
Mistry, N. P., Saeed, H., Rafique, S., Le, T., Obaid, H., & Adams, S. (2024) · Academic Radiology · doi:10.1016/j.acra.2024.06.046 · Cited by 59 · see citing works
de la Puente, M., Torres, J., Troncoso, A. L. B., Meza, Y. Y. H., & Carrascal, J. X. M. (2024) · Smart Learning Environments · doi:10.1186/s40561-024-00347-0 · Cited by 46 · see citing works
“Additionally, the disruptive potential of large language models, including ChatGPT, in educational processes has been highlighted, necessitating a balanced consideration of the opportunities and challenges associated with their integration in education (Benítez, 2024).”
InfluentialLarge Language Models in Healthcare: A Bibliometric Analysis and Examination of Research Trends
Gencer, G., & Gencer, K. (2025) · Journal of Multidisciplinary Healthcare · doi:10.2147/jmdh.s502351 · Cited by 43 · see citing works
Wu, X. Y., Cai, G., Guo, B., Ma, L., Shao, S., Yu, J., Zheng, Y., Wang, L., & Yang, F. (2025) · BMC Oral Health · doi:10.1186/s12903-025-06619-6 · Cited by 42 · see citing works
A large language model digital patient system enhances ophthalmology history taking skills
Luo, M.-J., Bi, S., Pang, J., Liu, L., Tsui, C.-K., Lai, Y., Chen, W., Yang, Y., Xu, K., Zhao, L., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01841-6 · Cited by 39 · see citing works
Li, X., Yan, X., & Lai, H. (2025) · PLoS ONE · doi:10.1371/journal.pone.0333411 · Cited by 34 · see citing works
Mavrych, V., Yousef, E. M., Yaqinuddin, A., & Bolgova, O. (2025) · Medical Education Online · doi:10.1080/10872981.2025.2534065 · Cited by 31 · see citing works
Generative artificial intelligence: implications for biomedical and health professions education
Hersh, W. (2025) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-103123-094756 · Cited by 28 · see citing works
Simou, E. (2025) · International Medical Education · doi:10.3390/ime4040050 · Cited by 30 · see citing works
ChatGPT in Medical Education: Bibliometric and Visual Analysis
Zhang, Y., Xie, X., & Xu, Q. (2025) · JMIR Medical Education · doi:10.2196/72356 · Cited by 29 · see citing works
Pacheco, M. A. D. L. P., Torres, J., Troncoso, A. L. B., Murillo, H. J. G., & Carrascal, J. X. M. (2025) · Journal of Political Science Education · doi:10.1080/15512169.2025.2449936 · Cited by 23 · see citing works
A review of ChatGPT in medical education: exploring advantages and limitations
Cheng, Y., & Zhu, L. (2025) · International Journal of Surgery · doi:10.1097/js9.0000000000002505 · Cited by 24 · see citing works
McCarrick, C. A., McEntee, P. D., Boland, P. A., Donnelly, S. C., O’Meara, Y., Heneghan, H., & Cahill, R. A. (2025) · Journal of surgical education · doi:10.1016/j.jsurg.2025.103629 · Cited by 19 · see citing works
Alli, S. R., Hossain, S. Q., Das, S., & Upshur, R. E. G. (2024) · JMIR Medical Education · doi:10.2196/51446 · Cited by 19 · see citing works
Constructing knowledge: the role of AI in medical learning
McLean, A. L. (2024) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocae124 · Cited by 17 · see citing works
Zhang, Y., Wan, X.-H., Kong, Q., Liu, H., Liu, J., Guo, J., Yang, X., Zuo, X., & Li, Y. (2025) · World Journal of Gastroenterology · doi:10.3748/wjg.v31.i6.102090 · Cited by 17 · see citing works
Can large language models assist with pediatric dosing accuracy?
Levin, C., Orkaby, B., Kerner, E., & Saban, M. (2025) · Pediatric Research · doi:10.1038/s41390-025-03980-8 · Cited by 17 · see citing works
GPT-4 versus human authors in clinically complex MCQ creation: A blinded analysis of item quality
Wu, H., Zerner, T., Lee, D., Court‐Kowalski, S., Devitt, P. G., & Palmer, E. (2025) · Medical Teacher · doi:10.1080/0142159x.2025.2505122 · Cited by 16 · see citing works
Hsieh, C., Hsieh, H.-Y., & Lin, H. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e34851 · Cited by 16 · see citing works
Blockchain and Artificial Intelligence Non-Formal Education System (BANFES)
Nazari, Z., Vahidi, A. R., & Musı́lek, P. (2024) · Education Sciences · doi:10.3390/educsci14080881 · Cited by 28 · see citing works
Lakshmaiah, A., Korrapati, C., Suresh, C., & Raghavan, R. (2024) · Advances in medical diagnosis, treatment, and care (AMDTC) book series · doi:10.4018/979-8-3693-7728-4.ch010 · Cited by 16 · see citing works
Liu, R., Liu, J., Yang, J. N., Sun, Z., & Yan, H. (2025) · BMC Musculoskeletal Disorders · doi:10.1186/s12891-025-08601-3 · Cited by 16 · see citing works
Yao, Z., Parashar, A., Zhou, H., Jang, W. S., Ouyang, F., Yang, Z., & Yu, H. (2025) · Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) · doi:10.18653/v1/2025.naacl-long.538 · Cited by 12 · see citing works
Challenging the curve: can ChatGPT-generated MCQs reduce grade inflation in pharmacy education
Almaghaslah, D. (2025) · Frontiers in Pharmacology · doi:10.3389/fphar.2025.1516381 · Cited by 12 · see citing works
Cabitza, F., Famiglini, L., Fregosi, C., Pe, S., Parimbelli, E., La Maida, G. A., & Gallazzi, E. (2025) · Proceedings of the 30th International Conference on Intelligent User Interfaces · doi:10.1145/3708359.3712157 · Cited by 11 · see citing works
Fąferek, J., Kononowicz, A. A., Bogutska, N., Da Silva Domingues, V., Davydova, N., Frankowska, A., Iguacel, I., Mayer, A., Morin, L., Pavlyukovich, N., et al. (2025) · BMC Medical Education · doi:10.1186/s12909-025-08006-9 · Cited by 10 · see citing works
The application of ChatGPT in medical education: prospects and challenges
Wu, Z., Li, S., & Zhao, X. (2024) · International Journal of Surgery · doi:10.1097/js9.0000000000001887 · Cited by 8 · see citing works
Artificial intelligence and informatics in neonatal resuscitation
Fortunov, R. M., Cabacungan, E., Barry, J. S., & Jagarapu, J. (2024) · Seminars in Perinatology · doi:10.1016/j.semperi.2024.151992 · Cited by 8 · see citing works
Safadi, S., Amirahmadi, R., Tlimat, A., Rovinski, R., Sun, J., Lee, B. W., Seam, N., Tlimat, A., Staitieh, B., Clark, B. J., et al. (2025) · CHEST Journal · doi:10.1016/j.chest.2025.07.005 · Cited by 8 · see citing works
Wang, Y., Yang, S., Zeng, C., Xie, Y. C., Shen, Y., Li, J., Huang, X., Wei, R.-L., & Chen, Y. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1546706 · Cited by 8 · see citing works
Self-citationQiu, Y., & Liu, C. (2025) · Global Medical Education · doi:10.1515/gme-2024-0021 · Cited by 7 · see citing works
What patients want from healthcare chatbots: insights from a mixed-methods study
Dellavalle, N. S., Ellis, J. R., Moore, A., Akerson, M., Andazola, M., Campbell, E. G., & DeCamp, M. W. (2025) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaf164 · Cited by 9 · see citing works
Maitín, A. M., Nogales, A., Fernández-Rincón, S., Aranguren, E., Barba, E. C., Arranz, S. D., Rodríguez, A. A. M., & García-Tejedor, Á. J. (2024) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocae302 · Cited by 7 · see citing works
Wang, A., Ruparel, R., Iurchenko, A., Jhun, P., Séguin, J. A., Strachan, P., Wong, R., Karthikesalingam, A., Matias, Y., Hassidim, A., et al. (2025) · Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706599.3721208 · Cited by 6 · see citing works
Chow, M., Li, A., Quek, D. Y. J., Fan, E. B., & Sadasiv, M. S. (2026) · Postgraduate Medical Journal · doi:10.1093/postmj/qgag068 · Cited by 6 · see citing works
Kondo, T., Okamoto, M., & Kondo, Y. (2025) · Medical Science Educator · doi:10.1007/s40670-024-02251-1 · Cited by 6 · see citing works
Intelligent Development of College Physical Education Teaching Mode Based on “Internet+”
Zhang, X., & Ma, C. (2024) · International Journal of Web-Based Learning and Teaching Technologies · doi:10.4018/ijwltt.340724 · Cited by 6 · see citing works
Sridharan, K., & Sivaramakrishnan, G. (2025) · BMC Medical Education · doi:10.1186/s12909-025-08134-2 · Cited by 4 · see citing works
UsmleGPT: An AI application for developing MCQs via multi-agent system
Jiang, Z., & Feng, S. (2025) · Software Impacts · doi:10.1016/j.simpa.2025.100742 · Cited by 4 · see citing works
Roy, A. D., Jaiswal, I. B., Tiu, D. N., Das, D., Mondal, S., Behera, J. K., & Mondal, H. (2024) · Cureus · doi:10.7759/cureus.73484 · Cited by 4 · see citing works
Evaluating Large Language Models in Ptosis-Related inquiries: A Cross-Lingual Study
Niu, L.-H., Li, W., Qin, B., Chen, T., Dong, L., He, Y., Jiang, X., Wang, M., Ma, L., Geng, J., et al. (2025) · Translational Vision Science & Technology · doi:10.1167/tvst.14.7.9 · Cited by 3 · see citing works
Su, J., Yang, X., Li, X., Chen, J., Jiang, C., Wang, Y., Zhuang, L., & Li, H. (2025) · Clinical Cosmetic and Investigational Dermatology · doi:10.2147/ccid.s552979 · Cited by 3 · see citing works
Self-citationAssessing Personalized AI Mentoring with Large Language Models in the Computing Field
Luo, X., O'Connell, S., & Mithun, S. (2025) · IEEE Symposium on Computational Intelligence in Natural Language Processing and Social Media (CI-NLPSoMe) · doi:10.1109/ci-nlpsomecompanion65206.2025.10977933 · Cited by 3 · see citing works
Areas of research focus and trends in the research on the application of AIGC in healthcare
Wang, C., Zhu, Y., Zhang, X., Chen, X., Li, Y., Tan, Y., & Qi, H. (2025) · Journal of Health Population and Nutrition · doi:10.1186/s41043-025-00947-7 · Cited by 3 · see citing works
Chen, G., Sha, Y., Wang, Z., Wang, K., & Chen, L. (2025) · Cureus · doi:10.7759/cureus.85979 · Cited by 3 · see citing works
CaseMaster: Designing a Probe for Oral Case Presentation Training with LLM Assistance
Ouyang, Y., Xu, Y., Jiang, C., & Li, Q. (2025) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3706599.3720206 · Cited by 2 · see citing works
Self-citationAn LLM-Based System for Accessible and Personalized Scientific Communication
Rehman, M., Petrillo, A., & Awasare, K. (2025) · Procedia Computer Science · doi:10.1016/j.procs.2025.12.092 · Cited by 2 · see citing works
Guiding Novice Programmers With CPS-GPT: A Prompt-Driven AI Assistant for Creative Problem Solving
Wang, S. T., Li, P.-H., & Huang, Y. (2026) · Journal of Educational Computing Research · doi:10.1177/07356331261431145 · Cited by 2 · see citing works
Designing Generative AI-Based Agents to Empower Collaborative Self-Learning in Higher Education
Banh, L., Holldack, F., & Strobel, G. (2026) · Business & Information Systems Engineering · doi:10.1007/s12599-026-01012-1 · Cited by 2 · see citing works
Sawamura, S., Kohiyama, K., Takenaka, T., Sera, T., Inoue, T., & Nagai, T. (2025) · Cureus · doi:10.7759/cureus.79183 · Cited by 2 · see citing works
Commentary: Leveraging Large Language Models for Radiology Education and Training
Singh, S. M., Chaurasia, A., Raichandani, S., Grewal, H., Udare, A. S., & Jawahar, A. (2025) · Journal of Computer Assisted Tomography · doi:10.1097/rct.0000000000001736 · Cited by 2 · see citing works
Saban, M., Alon, Y., Luxenburg, O., Singer, C., Hierath, M., Schreiner, A. K., Brkljačić, B., & Sosna, J. (2025) · European Radiology · doi:10.1007/s00330-025-11608-y · Cited by 1 · see citing works
Okuno, S., Kawamitsu, K., & Yamaguchi, T. (2025) · Cureus · doi:10.7759/cureus.94390 · Cited by 1 · see citing works
Abumelha, M., ALGhamdi, A. S. A.-M., Fayoumi, A. G., & Ragab, M. (2025) · JMIR Medical Informatics · doi:10.2196/78432 · Cited by 1 · see citing works
A Prompt Engineering Methodology for Evaluating Large Language Models in Academic Processes
Georgieva, M., Patias, I., & Totev, P. (2025) · International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) · doi:10.1109/ismsit67332.2025.11267889 · Cited by 1 · see citing works
Karakoyun, Z. N., Yörük, M. D., Özdemir, M. E., & Koşar, M. İ. (2026) · BMC Medical Education · doi:10.1186/s12909-026-09173-z · Cited by 1 · see citing works
Xie, Y. U. Q. I. A. O., Ou, Z., Huang, Y., Huang, H., Ran, X., Dai, H., Huang, B., & Shu, L. (2026) · BMC Medical Education · doi:10.1186/s12909-026-09518-8 · Cited by 1 · see citing works
Qin, X., Song, P., Yan, Z., Qian, H., Jiang, L., & Wang, C. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1874243 · Cited by 1 · see citing works
Open‐source large language models in medical education: Balancing promise and challenges
Ray, P. P. (2024) · Anatomical Sciences Education · doi:10.1002/ase.2484 · Cited by 1 · see citing works
Valanci, S., Hlede, V., MacNeill, H., Margolis, Á., & Masters, K. (2026) · Journal of Continuing Education in the Health Professions · doi:10.1097/ceh.0000000000000648 · Cited by 1 · see citing works
Transformations in medical education: A prudential perspective
Boudreau, J. D., & Fuks, A. (2024) · Health care science · doi:10.1002/hcs2.86 · Cited by 1 · see citing works
Self-citationGuo, Z., Mao, H., & Zhang, F. (2026) · Publications · doi:10.3390/publications14020027
PhD: Distribution of heavy metals in environmental samples in the Allchar mine and its environ
Andonovska, K. B. (2013)
Yang, B. X., Zhou, F., Bai, N., Zhou, S., Luo, C., Wáng, Q., Wong, A. K. C., & Lin, F. (2025) · Frontiers of digital education. · doi:10.1007/s44366-025-0046-y · Cited by 2 · see citing works
Aljamaan, F., Mubarak, M., Altamimi, I., Alanteet, A. A., Alsalman, M., Dasuqi, S. A., Alballaa, R., Alarifi, M. I., Saadon, A. A., Alhaqbani, A. O., et al. (2025) · Research Square · doi:10.21203/rs.3.rs-6222830/v1
Xu, W., Lin, K.-L., Ailixiati, G., Yorke, J., Pan, J.-D., Liu, T., Chen, H., Zhang, S.-F., Zhang, Z.-J., Ho, K. Y., et al. (2025) · Research Square · doi:10.21203/rs.3.rs-6803964/v1
Xiong, Y., Wu, N., Zhang, Z., Wu, W., Zhang, X., Wang, X., Peng, G., Li, W., Huang, Y., Wang, J., et al. (2025) · Radiography · doi:10.1016/j.radi.2025.103115
Self-citationAttitude Towards Artificial Intelligence: Perspectives From Students in Economic Studies
Іванівна, В. Н., Balytska, M., & Honchar, I. (2025) · Emerald Group Publishing Limited eBooks · doi:10.1108/978-1-83608-904-920251005
Murakami, S., Oumi, K., Hirata, K., & Ogasawara, K. (2025) · doi:10.2196/preprints.83247
Shen, P., Yuan, Y., He, X., & Wang, F. (2025) · Global Medical Education · doi:10.1515/gme-2025-0009
Multimorbidity measurement strategies for predicting hospital visits
Neves, B. D., Moreira, J. M., Gonçalves, S., Cerejo, J., Mota, I., Silva, N. A., Leite, F., & Silva, M. J. (2025) · BMC Health Services Research · doi:10.1186/s12913-025-13817-z
Ethical and Societal Implications of Large Language Models
Shafik, W. (2025) · IGI Global eBooks · doi:10.4018/979-8-3693-8387-2.ch007
Technological Evolution in the Perioperative Environment
Giarrizzo‐Wilson, S. (2025) · AORN Journal · doi:10.1002/aorn.70016
Saban, M., Ben-Haim, G., Livne, A., Eden, H., Kreiss, Y., & Dankner, R. S. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-025-00667-1
Revolutionizing Interdisciplinary Education and Research
Shafik, W. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-9225-7.ch001
Clustering Techniques for Survey Response in Education Using Large Language Models
Villaceran, E. C., & Cabije, A. T. A. (2025) · International Conference on Artificial Intelligence for Sustainable Innovation (AI-SI) · doi:10.1109/ai-si66213.2025.11341525
Zhong, W., Hu, Y., Su, R., Xu, L., Chen, Y., He, N., Liu, C., Xu, K., Zhao, M., Liao, W., et al. (2026) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2026.100974
Sun, S. Z., Beswick, B., Zhang, Z., & Song, H. (2026) · Studies in Higher Education · doi:10.1080/03075079.2026.2653674
CaseMaster: Designing and Evaluating a Probe for Oral Case Presentation Training with LLM Assistance
Ouyang, Y., Xu, Y., Jiang, C., Jin, Y., Jiang, H., & Li, Q. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790304
Self-citationLombardi, R., Destère, A., Dellamonica, J., Gérard, A. O., & Jozwiak, M. (2026) · JMIR Medical Education · doi:10.2196/82702
Foundations of LLMs in Healthcare
Taheri, A., Zamanifar, A., & Farhadi, A. (2026) · Springer eBooks · doi:10.1007/978-3-032-15088-2_1
Xu, C., Zhou, M., Wang, D., Zhang, Q. J., Liu, J.-Q., Fung, G. P. G., Wong, M., Xue, Y., Schmölzer, G. M., & Cheung, P.-Y. (2026) · Scandinavian Journal of Trauma Resuscitation and Emergency Medicine · doi:10.1186/s13049-026-01632-2
Pe, S., Angi, A., Izquierdo, C., Parimbelli, E., Boonstra, M., Asselbergs, F. W., & Lekadir, K. (2026) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-026-03697-6
Aljamaan, F., Mubarak, M. F., Altamimi, I., Alanteet, A. A., Alsalman, M. A., Dasuqi, S. A., Alballaa, R., Alarifi, M. I., Saadon, A. A., Alhaqbani, A. O., et al. (2026) · Health Science Reports · doi:10.1002/hsr2.72904
Khoshgoftar, Z., Ghaiyoomi, S., & Alaee, A. (2026) · Journal of Education and Health Promotion · doi:10.4103/jehp.jehp_719_25
Zhao, W., Li, X., Gao, Z., Chen, Z., & Sang, L. (2026) · Research Square · doi:10.21203/rs.3.rs-10590073/v1
Cheng, H. Y., & Schoen, S. (2026) · Journal of Primary Care & Community Health · doi:10.1177/21501319261488481
Simoni, J., Pereira, J. L., Sánchez, S. C., Egaña-Yin, D., García-Gargallo, C., Hertog, O. L. A., Idoate, A., López-Ortiz, L., Morales-Gutiérrez, I., Romero, A., et al. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2026.1918847
Mohammadipour, F., Beiranvand, M. S., & Piri-Nargeseh, R. (2026) · Research Square · doi:10.21203/rs.3.rs-10641134/v1
Agrawal, A., Sutar, R. F., & Singh, V. (2026) · Cureus · doi:10.7759/cureus.118334
- Authors
- Benítez, T. M., Xu, Y., Boudreau, J. D., Kow, A. W. C., Bello, F., Van Phuoc, L., Wang, X., Sun, X., Leung, G. K.-K., Lan, Y., Wang, Y., Cheng, D., Tham, Y.-C., Wong, T. Y., & Chung, K. C.
- Affiliations
- University of Michigan (US)
- Tsinghua University (CN)
- McGill University (CA)
- National University of Singapore (SG)
- College of Health Sciences, VinUniversity , Hanoi, 100000, Vietnam
- Beihang University (CN)
- Shanghai Jiao Tong University (CN)
- Department of Surgery, School of Clinical Medicine, LKS Faculty of Medicine, The University of Hong Kong, Queen Mary Hospital , Hong Kong, 999077, China
- Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Beijing Ophthalmology and Visual Sciences Key Laboratory, Capital University of Medical Science , Beijing, 100730, China
- Chinese University of Hong Kong, Shenzhen (CN)
- Singapore National Eye Center (SG)
- Corresponding author
- Tham, Yih-Chung
- Chung, Kevin C
- ORCID
- Venue
- Journal of the American Medical Informatics Association
- Publisher
- Oxford University Press (OUP)
- Published
- 2024-01-24
- Volume
- 31
- Issue
- 3
- Pages
- 776-783
- ISSN
- 1067-5027 (ISSN-L)
- 1527-974X
- PMC
- PMC10873781
- OpenAlex
- W4391225179
- Cited by
- 100
- References
- 51
- Citations by year
- 2026: 25 · 2025: 55 · 2024: 19 · 2013: 1
- FWCI
- 2.26
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of the American Medical Informatics Association (publishedVersion)
- PubMed Central (publishedVersion, other-oa)
- PubMed (publishedVersion)
- The HKU Scholars Hub (University of Hong Kong) (submittedVersion, cc-by-nc-nd)
- Funders
- National Key Research and Development Program of China (2022YFC2502802)
- National Natural Science Foundation of China (8238810007)
- National Institutes of Health
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
- Keywords
- large language models, ChatGPT, medical education, AI in teaching, personalized learning, clinical skills development, academic misconduct, overreliance on AI, critical thinking, medical curriculum
- MeSH terms
- Clinical Competence, Education, Medical, Humans, Language, Learning, Reproducibility of Results
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
