Artificial intelligence (AI) is transforming healthcare and medical education, offering opportunities to enhance learning, diagnostic reasoning, and personalized instruction. However, its integration into undergraduate medical education (UME) remains a challenge, necessitating a balance between technological advancements and ethical considerations. This study investigates best practices for incorporating AI into UME curriculum through a qualitative thematic analysis of recent literature. Key themes identified include AI’s potential to improve medical student performance, ethical concerns related to privacy and bias, and the need for expanded AI education to allow for curricular integration. While AI offers significant benefits in medical training, challenges such as academic integrity, patient confidentiality, and the risk of over-reliance on AI highlight the necessity for structured, ethical, and evidence-based AI education. The findings underscore the importance of developing comprehensive curricula that equip future physicians with the knowledge and critical thinking skills required to navigate AI-assisted healthcare responsibly.
References
[1]
Alzayed, A.A. (2023) Application of Artificial Intelligence in Pediatric Pulmonology: Current Scenario and Future Prospective. SVU-International Journal of Medical Sciences, 6, 501-510. https://doi.org/10.21608/svuijm.2023.195963.1544
[2]
Lee, J., Wu, A.S., Li, D. and Kulasegaram, K. (2021) Artificial Intelligence in Undergraduate Medical Education: A Scoping Review. Academic Medicine, 96, S62-S70. https://doi.org/10.1097/acm.0000000000004291
[3]
Civaner, M.M., Uncu, Y., Bulut, F., Chalil, E.G. and Tatli, A. (2022) Artificial Intelligence in Medical Education: A Cross-Sectional Needs Assessment. BMC Medical Education, 22, Article No. 772. https://doi.org/10.1186/s12909-022-03852-3
[4]
Rampton, V., Mittelman, M. and Goldhahn, J. (2020) Implications of Artificial Intelligence for Medical Education. The Lancet Digital Health, 2, e111-e112. https://doi.org/10.1016/s2589-7500(20)30023-6
[5]
Sami, A., Tanveer, F., Sajwani, K., Kiran, N., Javed, M.A., Ozsahin, D.U., et al. (2025) Medical Students’ Attitudes toward AI in Education: Perception, Effectiveness, and Its Credibility. BMC Medical Education, 25, Article No. 82. https://doi.org/10.1186/s12909-025-06704-y
[6]
Daungsupawong, H. and Wiwanitkit, V. (2025) Impact of AI-Generated Individual Feedback on Written Online Assignments for Medical Students: Correspondence. Medical Teacher, 47, 1404-1405. https://doi.org/10.1080/0142159x.2025.2461542
[7]
Holderried, F., Stegemann-Philipps, C., Herrmann-Werner, A., Festl-Wietek, T., Holderried, M., Eickhoff, C., et al. (2024) A Language Model-Powered Simulated Patient with Automated Feedback for History Taking: Prospective Study. JMIR Medical Education, 10, e59213. https://doi.org/10.2196/59213
[8]
Misra, S.M. and Suresh, S. (2024) Artificial Intelligence and Objective Structured Clinical Examinations: Using ChatGPT to Revolutionize Clinical Skills Assessment in Medical Education. Journal of Medical Education and Curricular Development, 11, 1-6. https://doi.org/10.1177/23821205241263475
[9]
Feigerlova, E., Hani, H. and Hothersall-Davies, E. (2025) A Systematic Review of the Impact of Artificial Intelligence on Educational Outcomes in Health Professions Education. BMC Medical Education, 25, Article No. 129. https://doi.org/10.1186/s12909-025-06719-5
[10]
Jamieson, A.R., Holcomb, M.J., Dalton, T.O., Campbell, K.K., Vedovato, S., Shakur, A.H., et al. (2024) Rubrics to Prompts: Assessing Medical Student Post-Encounter Notes with AI. NEJM AI, 1. https://doi.org/10.1056/aics2400631
[11]
Wang, Z., Hassan, N., LeBaron, V., Flickinger, T., Ling, D., Edwards, J., et al. (2024) CommSense: A Wearable Sensing Computational Framework for Evaluating Patient-Clinician Interactions. Proceedings of the ACM on Human-Computer Interaction, 8, 1-31. https://doi.org/10.1145/3686952
[12]
Park, C.J., Yi, P.H. and Siegel, E.L. (2021) Medical Student Perspectives on the Impact of Artificial Intelligence on the Practice of Medicine. Current Problems in Diagnostic Radiology, 50, 614-619. https://doi.org/10.1067/j.cpradiol.2020.06.011
[13]
Ejaz, H., McGrath, H., Wong, B.L., Guise, A., Vercauteren, T. and Shapey, J. (2022) Artificial Intelligence and Medical Education: A Global Mixed-Methods Study of Medical Students’ Perspectives. Digital Health, 8, 1-11. https://doi.org/10.1177/20552076221089099
[14]
Preiksaitis, C., Nash, C., Gottlieb, M., Chan, T.M., Alvarez, A. and Landry, A. (2024) Brain versus BOT: Distinguishing Letters of Recommendation Authored by Humans Compared with Artificial Intelligence.
[15]
Moritz, S., Romeike, B., Stosch, C. and Tolks, D. (2023) Generative AI (gAI) in Medical Education: ChatGPT and Co. GMS Journal for Medical Education, 40, Doc54.
[16]
Snedegar, R., Pilkerton, C., Xiang, J. and Allison, R. (2025) Implementation of Artificial Intelligence in Writing Letters of Recommendation. Cureus, 17, e94627. https://doi.org/10.7759/cureus.94627
[17]
Fried, J.C., Johnson, N.R., Pelletier, A., Landman, A. and Bartz, D. (2025) Using Generative Artificial Intelligence When Writing Letters of Recommendation. Academic Medicine, 100, 769-775. https://doi.org/10.1097/acm.0000000000006047
[18]
Gin, B.C., LaForge, K., Burk-Rafel, J. and Boscardin, C.K. (2025) Macy Foundation Innovation Report Part II: From Hype to Reality: Innovators’ Visions for Navigating AI Integration Challenges in Medical Education. Academic Medicine, 100, S22-S29. https://doi.org/10.1097/acm.0000000000006117