Framework for AI integration into the undergraduate medical curricula: a scoping review
Open accessAbstract
BACKGROUND: Artificial intelligence (AI) is rapidly transforming healthcare, driving global demand for AI applications. Undergraduate medical education (UGME) must therefore prepare future physicians for AI‑augmented practice. However, recent reviews indicate that curriculum reform lags behind the realistic clinical use of AI, raising the question of whether we are training the right “genotype” (undergraduate) and “phenotype” (postgraduate) of physicians for the AI and digital era. This scoping review synthesizes existing evidence on AI in UGME to identify key trends and offer a practical framework for designing future AI‑integrated medical curricula. METHODS: Following the PRISMA‑ScR guidelines, we searched PubMed, Web of Science, Scopus, and gray literature for studies published between January 2019 and December 2024 on AI integration into UGME. Two reviewers independently screened titles, abstracts, and full texts, with a third resolving disagreements. Data from eligible studies were extracted and thematically analyzed using seven core themes derived from the revised Bloom’s taxonomy. RESULT: Among the 767 screened articles, 26 studies met the predefined inclusion criteria, comprising 19 review articles and 7 empirical studies. The identified integrative themes predominantly focused on foundational knowledge, AI applications, and ethical/societal implications. Although themes such as human-AI collaboration, research and innovation, cross-organizational collaboration, and evaluation/assessment were less frequently addressed, they constitute critical components for establishing a sustainable and adaptable AI curriculum framework within medical education settings. CONCLUSION: Drawing on identified practices and theoretical models, this review proposes a structured, seven-element framework to support the design and implementation of future-oriented, AI-enabled UGME curricula across medical institutions worldwide.
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?
Prior work has articulated high-level domains.
“Cheng et al. and broader scoping work across the medical education continuum (Gordon et al.; Tolentino et al.; Ma et al.; Shaw et al.; Rincón et al.) have articulated high-level domains, mapped curricula and interventions, and emphasized the need for clearer competency frameworks24–27,32,33.”
From What the AI era doctor should know: a scoping review of proposed artificial intelligence competencies for medical education (2026) · cites this paper as [32]
Prior work has mapped curricula and interventions.
“Cheng et al. and broader scoping work across the medical education continuum (Gordon et al.; Tolentino et al.; Ma et al.; Shaw et al.; Rincón et al.) have articulated high-level domains, mapped curricula and interventions, and emphasized the need for clearer competency frameworks24–27,32,33.”
From What the AI era doctor should know: a scoping review of proposed artificial intelligence competencies for medical education (2026) · cites this paper as [32]
Prior work has emphasized the need for clearer competency frameworks.
“Cheng et al. and broader scoping work across the medical education continuum (Gordon et al.; Tolentino et al.; Ma et al.; Shaw et al.; Rincón et al.) have articulated high-level domains, mapped curricula and interventions, and emphasized the need for clearer competency frameworks24–27,32,33.”
From What the AI era doctor should know: a scoping review of proposed artificial intelligence competencies for medical education (2026) · cites this paper as [32]
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
PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation
Tricco, A. C., Lillie, E., Zarin, W., O'Brien, K. K., Colquhoun, H., Levac, D., Moher, D., Peters, M. D. J., Horsley, T., Weeks, L., et al. (2018) · Annals of Internal Medicine · doi:10.7326/m18-0850 · Cited by 43,565 · cited 1× · see citing works
“The methodology was conducted following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist [24].”
Revolutionizing healthcare: the role of artificial intelligence in clinical practice
Alowais, S. A., Alghamdi, S. S., Alsuhebany, N., Alqahtani, T., Alshaya, A. I., Almohareb, S. N., Aldairem, A., Alrashed, M., Bin Saleh, K., Badreldin, H. A., et al. (2023) · BMC Medical Education · doi:10.1186/s12909-023-04698-z · Cited by 3,146 · see citing works
The role of artificial intelligence in healthcare: a structured literature review
Secinaro, S. F., Calandra, D., Secinaro, A., Muthurangu, V., & Biancone, P. (2021) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-021-01488-9 · Cited by 1,203 · 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
A scoping review of artificial intelligence in medical education: BEME Guide No. 84
Gordon, M., Daniel, M., Ajiboye, A., Uraiby, H., Xu, N. Y., Bartlett, R., Hanson, J., Haas, M., Spadafore, M., Grafton-Clarke, C., et al. (2024) · Medical Teacher · doi:10.1080/0142159x.2024.2314198 · Cited by 543 · see citing works
Han, E.-R., Yeo, S., Kim, M.-J., Lee, Y.-H., Park, K.-H., & Roh, H. (2019) · BMC Medical Education · doi:10.1186/s12909-019-1891-5 · Cited by 393 · see citing works
Artificial intelligence in medical education: a cross-sectional needs assessment
Civaner, M. M., Uncu, Y., Bulut, F., Chalil, E. G., & Tatli, A. (2022) · BMC Medical Education · doi:10.1186/s12909-022-03852-3 · Cited by 363 · see citing works
Artificial Intelligence in Undergraduate Medical Education: A Scoping Review
Lee, J., Wu, A. S., Li, D., & Kulasegaram, K. (. (2021) · Academic Medicine · doi:10.1097/acm.0000000000004291 · Cited by 324 · see citing works
Hirani, R., Noruzi, K. A., Khuram, H., Hussaini, A. S., Aifuwa, E., Ely, K., Lewis, J. M., Gabr, A. E., Smiley, A., Tiwari, R. K., et al. (2024) · Life · doi:10.3390/life14050557 · Cited by 256 · see citing works
Younis, H. A., Eisa, T. A. E., Nasser, M., Sahib, T. M., Noor, A. A., Alyasiri, O. M., Salisu, S., Hayder, I. M., & Younis, H. A. (2024) · Diagnostics · doi:10.3390/diagnostics14010109 · Cited by 242 · see citing works
A Scoping Review of Reporting Gaps in FDA-Approved AI Medical Devices
Muralidharan, V., Adewale, B. A., Huang, C. J., Nta, M. T., Ademiju, P. O., Pathmarajah, P., Hang, M. K., Adesanya, O., Abdullateef, R. O., Babatunde, A. O., et al. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01270-x · Cited by 201 · see citing works
Needs, Challenges, and Applications of Artificial Intelligence in Medical Education Curriculum
Grunhut, J., Marques, O., & Wyatt, A. T. M. (2022) · JMIR Medical Education · doi:10.2196/35587 · Cited by 166 · see citing works
Tolentino, R., Baradaran, A., Gore, G., Pluye, P., & Abbasgholizadeh-Rahimi, S. (2024) · JMIR Medical Education · doi:10.2196/54793 · Cited by 165 · see citing works
Grunhut, J., Wyatt, A. T., & Marques, O. (2021) · Journal of Medical Education and Curricular Development · doi:10.1177/23821205211036836 · Cited by 161 · cited 5× · see citing works
“Ethical content was often delivered through case-based discussion and scenario analysis, focusing on issues such as informed consent for AI use, transparency of AI systems, data stewardship, and consequences of biased or low-quality datasets [36, 43, 49].”
InfluentialAI in Medical Education: Global situation, effects and challenges
Zhang, W., Cai, M., Lee, H. J., Evans, R., Zhu, C., & Ming, C. (2024) · Education and Information Technologies · doi:10.1007/s10639-023-12009-8 · Cited by 148 · see citing works
Mapping the use of artificial intelligence in medical education: a scoping review
Rincón, E. H. H., Jimenez, D., Aguilar, L. A. C., Flórez, J. M. P., Tapia, Á. E. R., & Peñuela, C. L. J. (2025) · BMC Medical Education · doi:10.1186/s12909-025-07089-8 · Cited by 145 · see citing works
Knopp, M. I., Warm, E. J., Weber, D. E., Kelleher, M., Kinnear, B., Schumacher, D. J., Santen, S. A., Mendonça, E. A., & Turner, L. (2023) · JMIR Medical Education · doi:10.2196/50373 · Cited by 136 · see citing works
Teaching AI Ethics in Medical Education: A Scoping Review of Current Literature and Practices
Weidener, L., & Fischer, M. (2023) · Perspectives on Medical Education · doi:10.5334/pme.954 · Cited by 133 · cited 1× · see citing works
“…four-step model has been proposed to integrate AI ethics into existing programs: define the ethical issues likely to arise, align content with current ethics modules, upskill ethics instructors with technical fun-damentals, and prepare students to address AI-specific dilemmas in practice [34].”
Li, Q., & Qin, Y. (2023) · BMC Medical Education · doi:10.1186/s12909-023-04700-8 · Cited by 131 · see citing works
Randomized Controlled Trials of Artificial Intelligence in Clinical Practice: Systematic Review
Lam, T. Y. T., Cheung, M. F. K., Munro, Y. L., Lim, K. M., Shung, D., & Sung, J. J. Y. (2022) · Journal of Medical Internet Research · doi:10.2196/37188 · Cited by 120 · cited 1× · see citing works
“…reveals significant geographical concentration in AI medical education, with publications predominantly originating from North America and Europe, limited global consensus framework, and insufficient guidance for pedagogical implementation across diverse cultural and resource contexts [13, 14].”
Teaching, Learning and Assessing Anatomy with Artificial Intelligence: The Road to a Better Future
Abdellatif, H., Al Mushaiqri, M., Albalushi, H., Al-Zaabi, A. A., Roychoudhury, S., & Das, S. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph192114209 · Cited by 120 · see citing works
Busch, F., Hoffmann, L., Truhn, D., Ortiz-Prado, E., Makowski, M. R., Bressem, K. K., Adams, L. C., COMFORT Consortium, Abdala, N., Navarro, Á. A., et al. (2024) · BMC Medical Education · doi:10.1186/s12909-024-06035-4 · Cited by 108 · see citing works
Advancements in artificial intelligence transforming medical education: a comprehensive overview
Khakpaki, A. (2025) · Medical Education Online · doi:10.1080/10872981.2025.2542807 · Cited by 101 · see citing works
Artificial Intelligence and Surgical Education: A Systematic Scoping Review of Interventions
Kirubarajan, A., Young, D., Khan, S., Crasto, N., Sobel, M., & Sussman, D. (2022) · Journal of Surgical Education · doi:10.1016/j.jsurg.2021.09.012 · Cited by 101 · see citing works
Pupic, N., Ghaffari-zadeh, A., Hu, R., Singla, R., Darras, K., Karwowska, A., & Forster, B. B. (2023) · PLOS Digital Health · doi:10.1371/journal.pdig.0000255 · Cited by 79 · see citing works
Medical education and physician training in the era of artificial intelligence
Xu, Y., Jiang, Z., Ting, D. S. W., Kow, A. W. C., Bello, F., Car, J., Tham, Y.-C., & Wong, T. Y. (2024) · Singapore Medical Journal · doi:10.4103/singaporemedj.smj-2023-203 · Cited by 76 · see citing works
Weidener, L., & Fischer, M. (2023) · JMIR Medical Education · doi:10.2196/46428 · Cited by 70 · see citing works
Laupichler, M. C., Hadizadeh, D. R., Wintergerst, M. W. M., von der Emde, L., Paech, D., Dick, E. A., & Raupach, T. (2022) · BMC Medical Education · doi:10.1186/s12909-022-03866-x · Cited by 62 · see citing works
Insights from teaching artificial intelligence to medical students in Canada
Hu, R., Fan, K. Y., Pandey, P., Hu, Z., Yau, O., Teng, M., Wang, P., Li, T., Ashraf, M., & Singla, R. (2022) · Communications Medicine · doi:10.1038/s43856-022-00125-4 · Cited by 58 · see citing works
Developing a Canadian artificial intelligence medical curriculum using a Delphi study
Singla, R., Pupic, N., Ghaffarizadeh, S.-A., Kim, C., Hu, R., Forster, B. B., & Hacihaliloglu, I. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01307-1 · Cited by 56 · cited 8× · see citing works
“Research and innovation with AI were reported in 6 articles [26, 27, 32, 35, 38, 46].”
InfluentialThe cases for and against artificial intelligence in the medical school curriculum
Ngo, B., Nguyen, D., & vanSonnenberg, E. (2022) · Radiology: Artificial Intelligence · doi:10.1148/ryai.220074 · Cited by 53 · see citing works
Houssaini, M. S., Aboutajeddine, A., Toughrai, İ., & Ibrahimi, A. (2024) · Thinking Skills and Creativity · doi:10.1016/j.tsc.2024.101491 · Cited by 45 · see citing works
Tsopra, R., Peiffer-Smadja, N., Charlier, C., Campeotto, F., Lemogne, C., Ruszniewski, P., Vivien, B., & Burgun, A. (2023) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2022.104980 · Cited by 42 · cited 10× · see citing works
“Research and innovation with AI were reported in 6 articles [26, 27, 32, 35, 38, 46].”
InfluentialRoveta, A., Castello, L. M., Massarino, C., Francese, A., Ugo, F., & Maconi, A. (2025) · AI · doi:10.3390/ai6090227 · Cited by 42 · see citing works
Abid, A., Murugan, A., Banerjee, I., Purkayastha, S., Trivedi, H., & Gichoya, J. W. (2023) · JMIR Medical Education · doi:10.2196/46500 · Cited by 41 · see citing works
Varma, J. R., Fernando, S., Ting, B. Y., Aamir, S., & Sivaprakasam, R. (2023) · Cureus · doi:10.7759/cureus.39701 · Cited by 41 · see citing works
Zidoun, Y., & El-Mardi, A. (2024) · BMC Medical Education · doi:10.1186/s12909-024-06236-x · Cited by 38 · see citing works
Javaeed, A. (2018) · MedEdPublish · doi:10.15694/mep.2018.0000128.1 · Cited by 38 · see citing works
Jacobs, S. M., Lundy, N. N., Issenberg, S. B., & Chandran, L. (2023) · JMIR Medical Education · doi:10.2196/50903 · Cited by 33 · see citing works
UNESCO's AI Competency Framework
Mutawa, A. M., & Sruthi, S. (2024) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-0884-4.ch004 · Cited by 24 · see citing works
Sanchez‐Gonzalez, M. A., & Terrell, M. (2023) · Cureus · doi:10.7759/cureus.48354 · Cited by 23 · see citing works
Salem, M. A., Zakaria, O. M., Aldoughan, E. A., Khalil, Z. A., & Zakaria, H. M. (2025) · Computers · doi:10.3390/computers14060238 · Cited by 21 · 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
Pizzolla, I., Aro, R., Duez, P., Lièvre, B. D., & Briganti, G. (2023) · The Journal of Interactive Learning Research · doi:10.70725/265312mqoebd · Cited by 17 · see citing works
Building the AI-Enabled Medical School of the Future
Succi, M. D., Chang, B. S., & Rao, A. S. (2025) · JAMA · doi:10.1001/jama.2025.2789 · Cited by 16 · see citing works
Navigating the AI Revolution in Medicine—Adopting Strategies for Medical Education
Greengrass, C. J. (2024) · Medical Science Educator · doi:10.1007/s40670-024-02257-9 · Cited by 15 · see citing works
Redmond, C., Farrell, R. M., Cunningham, C., Dineen, A., Foley, S. J., O’Donnell, D., O'Reilly, S. L., Stokes, D., & O’Neill, E. J. (2024) · BMC Medical Education · doi:10.1186/s12909-023-04972-0 · Cited by 14 · see citing works
Agarwal, G. G., Ramamoorthi, L. S., Yuen, T., Merced, E., Brenner, J., Wu, W. Y., & Sabina, R. L. (2025) · MedEdPORTAL · doi:10.15766/mep_2374-8265.11524 · Cited by 13 · see citing works
Impact of Artificial Intelligence on Global Healthcare Sector Performance
Maran, K., Priyadarshini, P., Senthilnanthan, C. R., Manikandan, M., Kumar, R. G., & Ramu, M. (2024) · International Conference on Power, Energy, Control and Transmission Systems (ICPECTS) · doi:10.1109/icpects62210.2024.10780008 · Cited by 9 · see citing works
Assessment of the global artificial intelligence market in healthcare
Малышев, В. В., Lipskyi, Y., Коваленко, В., Габ, А., Шахнін, Д. Б., & Орел, О. (2024) · Technology audit and production reserves · doi:10.15587/2706-5448.2024.316451 · Cited by 9 · see citing works
Feasibility and acceptability of incorporating artificial intelligence into simulation education
Swan, B. A., Febres-Cordero, S., Steiger, L., Lisenby, A., Getz, T., Hudson, J., Cole, K., Branch, R., McDermott, C., Fugate, K., et al. (2025) · Clinical Simulation in Nursing · doi:10.1016/j.ecns.2025.101739 · Cited by 5 · see citing works
Applying Bloom's Taxonomy in Primary Care Behavioural Health Training
Ogbeide, S. A., Johnson‐Esparza, Y., & George, D. (2025) · The Clinical Teacher · doi:10.1111/tct.70099 · Cited by 3 · see citing works
Flagged first, then by citations
Research works printed but not linked: 5
- [4]Patel N, Grewal H, Buddhavarapu V, Dhillon G. Openevidence: enhancing medical student clinical rotations with AI but with limitations. Cureus. 2025;17(1):e76867. 39897335 PMC11787857
- [19]Tuma F, Nassar AK. Applying Bloom’s taxonomy in clinical surgery: practical examples. Ann Med Surg (Lond). 2021;69:102656. 34429945 PMC8371225
- [20]Ray ME, Rudolph MJ, Daugherty KK. Bloom’s taxonomy in health professions education: associations with exam scores, clinical reasoning, and instructional effectiveness. Curr Pharm Teach Learn. 2025;17(11):102444. 40695205 · DOI
- [25]Wilson LO. Anderson and Krathwohl bloom’s taxonomy revised Understanding the new version of bloom’s taxonomy. Second Principle. 2016;1(1):1–8.
- [38]Mir MM, Mir GM, Raina NT, Mir SM, Mir SM, Miskeen E, et al. Application of artificial intelligence in medical education: current scenario and future perspectives. J Adv Med Educ Prof. 2023;11(3):133–40. 37469385 PMC10352669
Cited by
Sorted by citations
Hunt, V. M., Sprehe, L. K., de Lomba, W. C., Gameiro, R. R., Woite, N. L., Wade, C. G., Chen, J. H., Rougas, S. C., Fraser, H. S. F., Sullivan, A. M., et al. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02761-9 · Cited by 10 · see citing works
Artificial intelligence perceptions and experiences among first and second year medical students
Knopf, E., Corrada, G. M. H., Luck, G. R., & Perumareddi, P. (2026) · Journal of Investigative Medicine · doi:10.1177/10815589261462547 · Cited by 1 · see citing works
Global research trends of digital applications in medical education
Chen, B., & Zhang, X. (2026) · Medicine · doi:10.1097/md.0000000000050357
Niño, D. F., Masters, K., Patel, R. M., Bahous, S. A., Cheng, D., & Rossi, A. (2026) · Medical Teacher · doi:10.1080/0142159x.2026.2718319
Self-citationAI and the double competence gap in dental education: towards symbiotic intelligence
Krumsvik, R. J., Klock, K. S., & Bratteberg, M. (2026) · Frontiers in Dental Medicine · doi:10.3389/fdmed.2026.1965756
- Authors
- Cheng, D., Chan, E. Y. S., Song, Y., Cui, A., Chang, A., & Wang, W.
- Affiliations
- Chinese University of Hong Kong (HK)
- Peking University (CN)
- Corresponding author
- Cheng, D.
- Venue
- BMC Medical Education
- Published
- 2026-01-23
- Volume
- 26
- Issue
- 1
- ISSN
- 1472-6920 (ISSN-L)
- PMC
- PMC13011678
- OpenAlex
- W7125504003
- Cited by
- 4
- References
- 52
- Citations by year
- 2026: 4
- FWCI
- 7.87
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMC Medical Education (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Keywords
- artificial intelligence in healthcare, undergraduate medical education, curriculum design framework, human-AI collaboration, Bloom's taxonomy, medical curriculum reform, digital health workforce
- MeSH terms
- Artificial Intelligence, Curriculum, Education, Medical, Undergraduate, Humans
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
