Transformation and Reinvention: A Comprehensive Analysis of Frontiers and Trends in AI-Empowered Medical Education by 2025

Authors

  • Jiayi Zhang The First Affiliated Hospital (The First Clinical Medical School) of Guangdong Pharmaceutical University, P.R. China
  • Li Zhang Guangdong Pharmaceutical University, P.R. China
  • Jiaxin Bei The First Affiliated Hospital (The First Clinical Medical School) of Guangdong Pharmaceutical University, P.R. China
  • Mingzhe Li* The First Affiliated Hospital (The First Clinical Medical School) of Guangdong Pharmaceutical University, P.R. China

DOI:

https://doi.org/10.61360/BoniCETR262019730201

Keywords:

intelligent teaching, artificial intelligence, medical education, smart classroom, digitalized teaching

Abstract

In 2025, the rapid iteration of generative artificial intelligence (AI) and large language models (LLMs) has profoundly reshaped the ecosystem of medical education. This review systematically synthesizes the latest advances in AI-enabled medical education through an analysis of more than 150 core articles published in 2025 and indexed in the PubMed database. The paper first evaluates the current state of AI literacy among medical students and educators worldwide, revealing a pervasive “cognition–practice” misalignment. It then examines innovative application models of AI in curriculum integration, the development of intelligent educational tools (e.g., virtual patients and personalized tutoring systems), and AI-enhanced assessment and feedback mechanisms. Ultimately, the review examines key ethical challenges, including algorithmic bias, academic integrity, and data privacy. The findings suggest that future medical education should establish a human-centered framework of human–AI collaboration, with particular emphasis on cultivating physicians’ critical thinking and humanistic values. This review aims to provide both theoretical foundations and practical guidance for the development of a new paradigm in intelligent medical education.

References

Abdullah, S., Hasan, S. R., Asim, M. A., Khurshid, A., & Qureshi, A. W. (2025). Exploring dental faculty awareness, knowledge, and attitudes toward AI integration in education and practice: A mixed-method study. BMC Medical Education, 25(1), 691. https://doi.org/10.1186/s12909-025-07259-8

Acosta, J. A. (2025). Perspective: Advancing public health education by embedding AI literacy. Frontiers in Digital Health, 7, 1584883. https://doi.org/10.3389/fdgth.2025.1584883

Adhikary, P. K., Motiyani, I., Oke, G., et al. (2025). Menstrual health education using a specialized large language model in India: Development and evaluation study of MenstLLaMA. Journal of Medical Internet Research, 27, e71977. https://doi.org/10.2196/71977

AlGoraini, Y., Alsayyali, M., Alotaibi, O., Almeshawi, I., Alaifan, F., & Alrashed, R. (2025). Perceptions of large language models in medical education and clinical practice among pediatric emergency physicians in Saudi Arabia: A multiregional cross-sectional study. Frontiers in Public Health, 13, 1634638. https://doi.org/10.3389/fpubh.2025.1634638

Alsultan, M. M., Baraka, M. A., Alhajri, L. H., et al. (2025). Knowledge and perception of artificial intelligence education among undergraduate healthcare students. BMC Medical Education, 25(1), 1066. https://doi.org/10.1186/s12909-025-07577-x

AlZaabi, A., & Masters, K. (2025). Assessing medical students’ readiness for artificial intelligence after pre-clinical training. BMC Medical Education, 25(1), 824. https://doi.org/10.1186/s12909-025-07008-x

Azim, A. A., & Azim, K. A. (2025). Innovation in endodontic education—cliché or necessity? A perspective on post-graduate training. International Endodontic Journal. Advance online publication. https://doi.org/10.1111/iej.14259

Bai, X., Huang, R., Liu, Q., et al. (2025). A technological convergence in hepatobiliary oncology: Evolving roles of smart surgical systems. Bioscience Trends, 19(4), 410–420. https://doi.org/10.5582/bst.2025.01047

Boscardin, C. K., Abdulnour, R. E. E., & Gin, B. C. (2025). Macy Foundation Innovation Report Part I: Current landscape of artificial intelligence in medical education. Academic Medicine, 100(9 Suppl. 1), S15–S21. https://doi.org/10.1097/ACM.0000000000006107

Chang, L. C., Huang, C. H., Yu, H. Y., et al. (2025). Enhancing work efficiency with generative artificial intelligence: Experience and training insights from school nurses through focus groups and surveys. International Nursing Review, 72(3), e70076. https://doi.org/10.1111/inr.70076

Chen, J., Hou, X., Lv, Y., et al. (2025). What digital competencies should medical students in China possess in the AI era? International Journal of Medical Informatics, 203, 106043. https://doi.org/10.1016/j.ijmedinf.2025.106043

Chen, J., Zhao, X., Li, J., Chen, W., Jiang, C., & Wang, X. (2025). Core competencies in pharmaceutical education: A Chinese student perspective. BMC Medical Education, 25(1), 1096. https://doi.org/10.1186/s12909-025-07686-7

Cheng, Y., & Zhu, L. (2025). A review of ChatGPT in medical education: Exploring advantages and limitations. International Journal of Surgery, 111(7), 4586–4602. https://doi.org/10.1097/JS9.0000000000002505

Cho Kwan, R. Y., Yan Tang, A. C., Ha Wong, J. Y., et al. (2025). Navigating the integration of artificial intelligence in nursing: Opportunities, challenges, and strategic actions. International Journal of Nursing Sciences, 12(3), 241–245. https://doi.org/10.1016/j.ijnss.2025.04.009

Clement David-Olawade, A., Wada, O. Z., Adeniji, Y. J., Aderupoko, I. V., & Olawade, D. B. (2025). Artificial intelligence readiness among healthcare students in Nigeria: A cross-sectional study assessing knowledge gaps, exposure, and adoption willingness. International Journal of Medical Informatics, 204, 106085. https://doi.org/10.1016/j.ijmedinf.2025.106085

Cold, K. M., Vamadevan, A., Laursen, C. B., Bjerrum, F., Singh, S., & Konge, L. (2025). Artificial intelligence in bronchoscopy: A systematic review. European Respiratory Review, 34(176), 240274. https://doi.org/10.1183/16000617.0274-2024

Daccache, N., Zako, J., Morisson, L., & Laferrière-Langlois, P. (2025). The applications of ChatGPT and other large language models in anesthesiology and critical care: A systematic review. Canadian Journal of Anesthesia, 72(6), 904–922. https://doi.org/10.1007/s12630-025-02973-9

Geng, W., Jiang, Y., Zhai, W., et al. (2026). Can artificial intelligence read between the lines: Utilizing ChatGPT to evaluate medical students’ implicit attitudes towards doctor–patient relationship. Medical Teacher, 48(1), 85–92. https://doi.org/10.1080/0142159X.2025.2515971

Gharib, A. M., Bindoff, I. K., Peterson, G. M., & Salahudeen, M. S. (2025). Educators’ and academic leaders’ insights on incorporating computer-based simulation in pharmacy education: A global qualitative study. American Journal of Pharmaceutical Education, 89(8), 101433. https://doi.org/10.1016/j.ajpe.2025.101433

Gigola, F., Amato, T., Del Riccio, M., Raffaele, A., Morabito, A., & Coletta, R. (2025). Artificial intelligence in clinical practice: A cross-sectional survey of paediatric surgery residents’ perspectives. BMJ Health & Care Informatics, 32(1), e101456. https://doi.org/10.1136/bmjhci-2025-101456

Hopson, S., Mildon, C., Hassard, K., et al. (2025). Enhancing AI literacy in undergraduate pre-medical education through student associations: An educational intervention. BMC Medical Education, 25(1), 999. https://doi.org/10.1186/s12909-025-07556-2

Howley, L. D., & Whelan, A. J. (2025). From the World Wide Web to AI: Why we must learn from our past to transform the future of medical education. Academic Medicine, 100(9 Suppl. 1), S30–S33. https://doi.org/10.1097/ACM.0000000000006103

Hu, N., Jiang, X. Q., Wang, Y. D., et al. (2025). Status and perceptions of ChatGPT utilization among medical students: A survey-based study. BMC Medical Education, 25(1), 831. https://doi.org/10.1186/s12909-025-07438-7

Huang, A. A., & Huang, S. Y. (2025). Enhancing medical education through statistics: Bridging quantitative literacy and sports supplementation research for improved clinical practice. Nutrients, 17(15), 2463. https://doi.org/10.3390/nu17152463

Huang, R., Wu, H., Yuan, Y., et al. (2025). Evaluation and bias analysis of large language models in generating synthetic electronic health records: Comparative study. Journal of Medical Internet Research, 27, e65317. https://doi.org/10.2196/65317

Jiang, J. (2025). Marathon without a finish line: A learner’s perspective on AI in medical education. Academic Medicine, 100(9 Suppl. 1), S43–S45. https://doi.org/10.1097/ACM.0000000000006100

Khakpaki, A. (2025). Advancements in artificial intelligence transforming medical education: A comprehensive overview. Medical Education Online, 30(1), 2542807. https://doi.org/10.1080/10872981.2025.2542807

Khosravi, B., Purkayastha, S., Erickson, B. J., Trivedi, H. M., & Gichoya, J. W. (2025). Exploring the potential of generative artificial intelligence in medical image synthesis: Opportunities, challenges, and future directions. The Lancet Digital Health. Advance online publication. https://doi.org/10.1016/j.landig.2025.100890

Khoury, Z. H., Sultan, M. S., Tavares, T., Jessri, M., & Sultan, A. S. (2026). AI-generated podcasts for health education. Medical Teacher, 48(1), 158–163. https://doi.org/10.1080/0142159X.2025.2513421

Lee, Y., Hunter, E., McLaughlin, A. T., et al. (2025). Cultural opportunities involving spiritual, existential, religious, or theological (SERT) themes: Three practical approaches. Psychotherapy. Advance online publication. https://doi.org/10.1037/pst0000599

Levkovich, I., Haber, Y., Levi-Belz, Y., & Elyoseph, Z. (2025). A step toward the future? Evaluating GenAI QPR simulation training for mental health gatekeepers. Frontiers in Medicine, 12, 1599900. https://doi.org/10.3389/fmed.2025.1599900

Liu, C., Zheng, J., Liu, Y., et al. (2025). Potential to perpetuate social biases in health care by Chinese large language models: A model evaluation study. International Journal for Equity in Health, 24(1), 206. https://doi.org/10.1186/s12939-025-02581-5

Liu, Y., Yu, F., Zhang, X., et al. (2025). Assessing the role of large language models between ChatGPT and DeepSeek in asthma education for bilingual individuals: Comparative study. JMIR Medical Informatics, 13, e65365. https://doi.org/10.2196/65365

Ma, X., Pan, W., & Yu, X. N. (2025). Evaluating AI-generated examination papers in periodontology: A comparative study with human-designed counterparts. BMC Medical Education, 25(1), 1099. https://doi.org/10.1186/s12909-025-07706-6

Maity, S., & Saikia, M. J. (2025). Large language models in healthcare and medical applications: A review. Bioengineering, 12(6), 631. https://doi.org/10.3390/bioengineering12060631

Mawyin-Muñoz, C. E., Salmerón-Escobar, F. J., Hidalgo-Acosta, J. A., & Calderon-León, M. F. (2025). Medical simulation: An essential tool for training, diagnosis, and treatment in the 21st century. BMC Medical Education, 25(1), 1019. https://doi.org/10.1186/s12909-025-07610-z

Miguez-Pinto, J. P., Garcia-Rosa, B., Maggitti-Bezerril, M., et al. (2025). The medical student of the future: Redefining competencies in a transformative era. Frontiers in Medicine, 12, 1593685. https://doi.org/10.3389/fmed.2025.1593685

Mizna, S., Arora, S., Saluja, P., Das, G., & Alanesi, W. A. (2025). An analytic research and review of the literature on practice of artificial intelligence in healthcare. European Journal of Medical Research, 30(1), 382. https://doi.org/10.1186/s40001-025-02603-6

Moser, M., Posel, N., Ganescu, O., & Fleiszer, D. (2025). Twelve tips: Using generative AI to create and optimize content for virtual patient simulations. Medical Teacher, 47(11), 1745–1751. https://doi.org/10.1080/0142159X.2025.2501252

Negrete, D., Lopes, S. L. P. de C., Barretto, M. D. de A., Moura, N. B. de, Nahás, A. C. R., & Costa, A. L. F. (2025). Artificial intelligence and dentomaxillofacial radiology education: Innovations and perspectives. Dentistry Journal, 13(6), 245. https://doi.org/10.3390/dj13060245

Panzuto, F., Barbi, S., Trama, A., & Fazio, N. (2025). The importance of education and training in neuroendocrine neoplasms: Challenges and opportunities for multidisciplinary management. Cancer Treatment Reviews, 139, 102998. https://doi.org/10.1016/j.ctrv.2025.102998

Perera, I. R., Daniels, T., Looney, J., Gittings, K., & Rawlins, F. A. (2025). Predicting New York Heart Association (NYHA) heart failure classification from medical student notes following simulated patient encounters. Scientific Reports, 15(1), 25491. https://doi.org/10.1038/s41598-025-10179-8

Pham, T. D., Karunaratne, N., Exintaris, B., et al. (2025). The impact of generative AI on health professional education: A systematic review in the context of student learning. Medical Education, 59(12), 1280–1289. https://doi.org/10.1111/medu.15746

Qiang, S., Zhang, H., Liao, Y., et al. (2025). Application of large language models in stroke rehabilitation health education: 2-phase study. Journal of Medical Internet Research, 27, e73226. https://doi.org/10.2196/73226

Qiu, Y., Chen, X., Wu, X., et al. (2025). Embodied artificial intelligence in ophthalmology. NPJ Digital Medicine, 8(1), 351. https://doi.org/10.1038/s41746-025-01754-4

Seneviratne, H. M. T. W., & Manathunga, S. S. (2025). Artificial intelligence assisted automated short answer question scoring tool shows high correlation with human examiner markings. BMC Medical Education, 25(1), 1146. https://doi.org/10.1186/s12909-025-07718-2

Shah, N., Wawrzynski, J., Hussain, R., et al. (2025). Application of real-time artificial intelligence to cataract surgery. British Journal of Ophthalmology, 109(12), 1338–1344. https://doi.org/10.1136/bjo-2024-326111

Shishehgar, S., Murray-Parahi, P., Alsharaydeh, E., Mills, S., & Liu, X. (2025). Artificial intelligence in health education and practice: A systematic review of health students’ and academics’ knowledge, perceptions and experiences. International Nursing Review, 72(2), e70045. https://doi.org/10.1111/inr.70045

Sockolow, P., Michalowski, M., Peltonen, L. M., Pruinelli, L., & Topaz, M. (2025). Advancing AI initiatives in nursing academics: Case studies and insights from thought leaders. Nursing Outlook, 73(5), 102527. https://doi.org/10.1016/j.outlook.2025.102527

Stephan, D., Bertsch, A. S., Schumacher, S., et al. (2025). Improving patient communication by simplifying AI-generated dental radiology reports with ChatGPT: Comparative study. Journal of Medical Internet Research, 27, e73337. https://doi.org/10.2196/73337

Stern, J. M., Fernandez-Perez, A. M., Cruz-Ossa, N., Hernandez, V. H., McNamara, C. A., & D’Apuzzo, M. R. (2025). Detecting artificial intelligence-generated text in personal statements of adult reconstruction fellowship applicants. The Journal of Arthroplasty. Advance online publication. https://doi.org/10.1016/j.arth.2025.07.072

Sun, Q. W., Miller, J., & Hull, S. C. (2025). Charting the ethical landscape of generative AI-augmented clinical documentation. Journal of Medical Ethics. Advance online publication. https://doi.org/10.1136/jme-2024-110656

Syeda, L. H., Batool, Z., Hayder, Z., & Ali, S. (2025). Medical undergraduate students’ awareness and perspectives on artificial intelligence: A developing nation’s context. BMC Medical Education, 25(1), 1060. https://doi.org/10.1186/s12909-025-07223-6

Tong, X., Hu, Y., Long, Y., et al. (2025). The application of problem-based learning (PBL) guided by ChatGPT in clinical education in the department of nephrology. BMC Medical Education, 25(1), 1048. https://doi.org/10.1186/s12909-025-07427-w

Tucker, F. (2025). Doing philosophy and the future of the “good doctor” paradigm. Medicine, Health Care and Philosophy, 28(4), 669–677. https://doi.org/10.1007/s11019-025-10294-3

Turner, L., Kelleher, M., Overla, S., et al. (2025). Harnessing the generative power of AI to move closer to personalized medical education. Academic Medicine, 100(12), 1447–1451. https://doi.org/10.1097/ACM.0000000000006185

Ugoala, O., Ebubechukwu, U., Mares, A. C., et al. (2025). Visual art and representation in cardiology: Past, present, and future. American Heart Journal, 290, 201–215. https://doi.org/10.1016/j.ahj.2025.06.016

Urda-Cîmpean, A. E., Leucuța, D. C., Drugan, C., Duțu, A. G., Călinici, T., & Drugan, T. (2025). Assessing the accuracy of diagnostic capabilities of large language models. Diagnostics, 15(13), 1657. https://doi.org/10.3390/diagnostics15131657

Verghese, B. G., Iyer, C., Borse, T., Cooper, S., White, J., & Sheehy, R. (2025). Modern artificial intelligence and large language models in graduate medical education: A scoping review of attitudes, applications & practice. BMC Medical Education, 25(1), 730. https://doi.org/10.1186/s12909-025-07321-5

Wang, Z., Fan, T. T., Li, M. L., Zhu, N. J., & Wang, X. C. (2025). Feasibility study of using GPT for history-taking training in medical education: A randomized clinical trial. BMC Medical Education, 25(1), 1030. https://doi.org/10.1186/s12909-025-07614-9

Wong, A., Sussman, J., Price, N., et al. (2025). The data-augmented, technology-assisted medical decision making (DATA-MD) curriculum: A machine learning and artificial intelligence curriculum for clinical trainees. Academic Medicine, 100(9), 1035–1039. https://doi.org/10.1097/ACM.0000000000006089

Wu, X., Huang, Y., & He, Q. (2025). A large language model improves clinicians’ diagnostic performance in complex critical illness cases. Critical Care, 29(1), 230. https://doi.org/10.1186/s13054-025-05468-7

Xu, F., Zhang, J., Zhou, Q., & Mou, S. (2025). MOOC construction for life education in Chinese universities: An analytical study. Frontiers in Public Health, 13, 1569881. https://doi.org/10.3389/fpubh.2025.1569881

Yazdi, N. A., Zamaniahari, U., KhadiviMaleki, H., & Hasanabadi, P. (2025). Readiness to use artificial intelligence: A comparative study among dental faculty members and students. BMC Medical Education, 25(1), 1006. https://doi.org/10.1186/s12909-025-07621-w

Yousef, M., Deeb, S., & Alhashlamon, K. (2025). AI usage among medical students in Palestine: A cross-sectional study and demonstration of AI-assisted research workflows. BMC Medical Education, 25(1), 693. https://doi.org/10.1186/s12909-025-07272-x

Yu, E., Chu, X., Zhang, W., et al. (2025). Large language models in medicine: Applications, challenges, and future directions. International Journal of Medical Sciences, 22(11), 2792–2801. https://doi.org/10.7150/ijms.111780

Zheng, A., Barker, C. J., Ferrante, S. S., Squires, J. H., Branstetter, B. F., & Hughes, M. A. (2025). Leveraging ChatGPT for enhancing learning in radiology resident education. Academic Radiology, 32(9), 5635–5642. https://doi.org/10.1016/j.acra.2025.06.019

Zheng, L., & Xiao, Y. (2025). Refining AI perspectives: Assessing the impact of AI curricula on medical students’ attitudes towards artificial intelligence. BMC Medical Education, 25(1), 1115. https://doi.org/10.1186/s12909-025-07669-8

Zheng, X., Zou, H., Wu, L., Dong, P., Yuan, W., & Chen, Y. (2025). Generative artificial intelligence in cardiovascular specialty care: A scoping review. BMC Nursing, 24(1), 947. https://doi.org/10.1186/s12912-025-03594-9

Zhu, X., & Juanatas, R. A. (2025). Auxiliary teaching and student evaluation methods based on facial expression recognition in medical education. JMIR Human Factors, 12, e72838. https://doi.org/10.2196/72838

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2026-02-26

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How to Cite

Transformation and Reinvention: A Comprehensive Analysis of Frontiers and Trends in AI-Empowered Medical Education by 2025. (2026). Contemporary Education and Teaching Research, 7(2), 41-51. https://doi.org/10.61360/BoniCETR262019730201

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