The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Artificial Intelligence in Undergraduate Medical Education: A Scoping Review
Abstract
PURPOSE: Artificial intelligence (AI) is a rapidly growing phenomenon poised to instigate large-scale changes in medicine. However, medical education has not kept pace with the rapid advancements of AI. Despite several calls to action, the adoption of teaching on AI in undergraduate medical education (UME) has been limited. This scoping review aims to identify gaps and key themes in the peer-reviewed literature on AI training in UME. METHOD: The scoping review was informed by Arksey and O'Malley's methodology. Seven electronic databases including MEDLINE and EMBASE were searched for articles discussing the inclusion of AI in UME between January 2000 and July 2020. A total of 4,299 articles were independently screened by 3 co-investigators and 22 full-text articles were included. Data were extracted using a standardized checklist. Themes were identified using iterative thematic analysis. RESULTS: The literature addressed: (1) a need for an AI curriculum in UME, (2) recommendations for AI curricular content including machine learning literacy and AI ethics, (3) suggestions for curriculum delivery, (4) an emphasis on cultivating "uniquely human skills" such as empathy in response to AI-driven changes, and (5) challenges with introducing an AI curriculum in UME. However, there was considerable heterogeneity and poor consensus across studies regarding AI curricular content and delivery. CONCLUSIONS: Despite the large volume of literature, there is little consensus on what and how to teach AI in UME. Further research is needed to address these discrepancies and create a standardized framework of competencies that can facilitate greater adoption and implementation of a standardized AI curriculum in UME.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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?
In the citing paper
“Current advances in AI have significantly influenced diagnostic and clinical decision-making [11, 12]”
In the citing paper
“This review summarized key themes regarding AI training in UGME and identified priorities to advance curricular development.”
From Artificial intelligence in undergraduate medical education: an updated scoping review (2025) · cites this paper as [5]
In the citing paper
“Lee et al. carried out a scoping review of the literature from 2000 to July 2020, identifying 22 studies on AI in UGME [5].”
From Artificial intelligence in undergraduate medical education: an updated scoping review (2025) · cites this paper as [5]
In the citing paper
“a lack of consensus on core competencies [5, 174, 175]”
From Artificial intelligence in undergraduate medical education: an updated scoping review (2025) · cites this paper as [5]
In the citing paper
“Challenges related to faculty members, such as the lack of AI knowledge and expertise [56, 180], resistance to adopting new technologies [5, 77, 180], and struggles with staff shortages, were also acknowledged in several studies [174].”
From Artificial intelligence in undergraduate medical education: an updated scoping review (2025) · cites this paper as [5]
In the citing paper
“Since that study, which included 22 publications, the number of publications on AI in UGME has increased dramatically, necessitating another examination of the AI landscape in UGME.”
From Artificial intelligence in undergraduate medical education: an updated scoping review (2025) · cites this paper as [5]
In the citing paper
“there were a small number that focused on AIHEd in specific disciplines or with particular groups of participants, for example Health & Welfare (n = 14), STEM (n = 4), online or blended learning (n = 5), foreign language learning (n = 2), pre-service teachers (Salas-Pilco et al., 2022), students with disabilities (Fichten et al., 2021), and undergraduate students (Lee et al., 2021).”
From A meta systematic review of artificial intelligence in higher education: a call for increased ethics, collaboration, and rigour (2024) · cites this paper as Lee et al., 2021
In the citing paper
“According to Ouyang et al. (2020), there is a disconnect between AI technology and existing educational systems, and suggest the need for more unified, standardised frameworks that incorporate ethical principles and advocate for the development of multidisciplinary teams (Charow et al., Lee et al., 2021), with a stronger focus on more robust and ethically aware AI curricula (e.g., Grunhut et al.,”
From A meta systematic review of artificial intelligence in higher education: a call for increased ethics, collaboration, and rigour (2024) · cites this paper as Lee et al., 2021
In the citing paper
“At the same time, it has been recognised that a lack of AI literacy could lead to a shift in authority moving decision-making from clinicians to AI systems (Lee et al., 2021).”
From A meta systematic review of artificial intelligence in higher education: a call for increased ethics, collaboration, and rigour (2024) · cites this paper as Lee et al., 2021
In the citing paper
“but these syntheses largely describe broad “AI literacy” themes or program-level content rather than systematically extracting and organizing competency- and learning-objective-level outcomes for undergraduate medical education (UME).”
From What the AI era doctor should know: a scoping review of proposed artificial intelligence competencies for medical education (2026) · cites this paper as [23]
In the citing paper
“Several scoping reviews have examined AI within medical education23–28”
From What the AI era doctor should know: a scoping review of proposed artificial intelligence competencies for medical education (2026) · cites this paper as [23]
In the citing paper
“Within undergraduate medical education, Lee et al. and Simoni et al. found that most publications describe general content areas rather than detailed competency-based outcomes23,31.”
From What the AI era doctor should know: a scoping review of proposed artificial intelligence competencies for medical education (2026) · cites this paper as [23]
In the citing paper
“Despite the support for including AI education, financial barriers, resistance of educators to change curricula, lack of staff with teaching experience in AI, and limited digitalization are some of the main barriers for implementing AI into the medical curriculum [3,10,12,13].”
From An evidence-based approach to artificial intelligence education for medical students: A systematic review (2023) · cites this paper as [12]
In the citing paper
“Seven studies met the ROBIS criteria for low risk [12,13,38,39,40,41,42], one study was of unclear risk [43], and three studies were of high risk [44–46].”
From An evidence-based approach to artificial intelligence education for medical students: A systematic review (2023) · cites this paper as [12]
In the citing paper
“Overall, this includes learning the AI terminology [13,37,45], strengths and limitations of AI [12,13,16,37,42], risks of AI [12,13,17,37,39,42], controlling for bias [42], critical appraisal AI literature and tools [12,13,34,37,39,45,46], and shared decision-making [12,13,37,41,45].”
From An evidence-based approach to artificial intelligence education for medical students: A systematic review (2023) · cites this paper as [12]
In the citing paper
“In particular, knowledge related to data stewardship [12,13,37,41], data preprocessing and acquisition [13], standardization [13], data analytics [13,17,39], health data infrastructure [45], programming [39,41], and big data [13,45], may help physicians appreciate the obstacles faced by data scientists and improve the multidisciplinary collaboration [13,39].”
From An evidence-based approach to artificial intelligence education for medical students: A systematic review (2023) · cites this paper as [12]
In the citing paper
“Eleven studies suggest that physicians should understand the role of data science in AI [12,13,16,37,39,41,42,45].”
From An evidence-based approach to artificial intelligence education for medical students: A systematic review (2023) · cites this paper as [12]
In the citing paper
“Quality improvement was also reflected by the need for evaluating and iterating on any AI curriculum designed for medical schools on a regular basis to further improve the learning experience [12].”
From An evidence-based approach to artificial intelligence education for medical students: A systematic review (2023) · cites this paper as [12]
In the citing paper
“Quality improvement was reflected through the need for critical appraisal of AI literature and tools [12,13,34,37,39], physician involvement in AI tool design/development [13,39], the ability to identify and control for bias [45], communication with data scientists and engineers [13], and communication with patients [12,13,37,41,42].”
From An evidence-based approach to artificial intelligence education for medical students: A systematic review (2023) · cites this paper as [12]
In the citing paper
“Furthermore, four key relationships for communication were identified: data scientist to physician [13], physician to physician/allied healthcare professional [12,13,42], physician to patient [12,13,37,41,42], and physician to payer [13,37].”
From An evidence-based approach to artificial intelligence education for medical students: A systematic review (2023) · cites this paper as [12]
In the citing paper
“Other important skills include knowledge related to machine learning (ML) [12,13], deep learning (DL) [13], natural language processing (NLP) [13], model development [13], and AI tool design and development [13,39].”
From An evidence-based approach to artificial intelligence education for medical students: A systematic review (2023) · cites this paper as [12]
In the citing paper
“The goal of this communication is to equip physicians with the tools needed to successfully use AI in a clinical setting [12,13,16,40,44,45].”
From An evidence-based approach to artificial intelligence education for medical students: A systematic review (2023) · cites this paper as [12]
In the citing paper
“Three key types of communication were identified: communication for clinical decision-making [13], communication for implementation [13,37], and communication for knowledge dissemination [12,13,37,41,42].”
From An evidence-based approach to artificial intelligence education for medical students: A systematic review (2023) · cites this paper as [12]
In the citing paper
“Communication for knowledge dissemination focuses on sharing knowledge to improve the understanding of AI tools with respect to their role in care, as well as the strengths, limitations, and risks of AI [12,13,17,37,39,42].”
From An evidence-based approach to artificial intelligence education for medical students: A systematic review (2023) · cites this paper as [12]
In the citing paper
“This would equip them with the skills needed to critically appraise AI literature and tools before implementing them [12,13,34,37,39], interpret model output [13,37,42], and communicate AI findings to others [12,13,42].”
From An evidence-based approach to artificial intelligence education for medical students: A systematic review (2023) · cites this paper as [12]
In the citing paper
“Studies suggest that ML, DL, and NLP should be included in the early years of training in undergraduate medical education in order to increase student exposure to AI [12,13].”
From An evidence-based approach to artificial intelligence education for medical students: A systematic review (2023) · cites this paper as [12]
In the citing paper
“Lee et al. support this sentiment, suggesting the need for the development of a standardized list of core AI competencies and the implementation of an AI curriculum to medical schools [12].”
From An evidence-based approach to artificial intelligence education for medical students: A systematic review (2023) · cites this paper as [12]
In the citing paper
“Nine studies discuss the need for a fundamental understanding of AI ethics to improve the adoption of AI in medicine and ensure its safe use [12,13,16,37,39,45].”
From An evidence-based approach to artificial intelligence education for medical students: A systematic review (2023) · cites this paper as [12]
In the citing paper
“Furthermore, by providing medical students with an opportunity to learn about AI ethics before they enter their clinical experiences, it could not only better prepare them to work with AI, but also better communicate to patients about AI [12,40].”
From An evidence-based approach to artificial intelligence education for medical students: A systematic review (2023) · cites this paper as [12]
In the citing paper
“At least 11 medical schools across the United States of America, Canada and the Republic of Korea have pilot AI curricula (3).”
From Integrating AI in medical education: embracing ethical usage and critical understanding (2023) · cites this paper as [3]
In the citing paper
“Although other publications acknowledge the importance of ethics in AI education, they do not provide specific content or guidance [36-39].”
From Proposing a Principle-Based Approach for Teaching AI Ethics in Medical Education (2024) · cites this paper as [37]
Citations
Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Qualitative Data Analysis: An Expanded Sourcebook
(1994) · Journal of Environmental Psychology · doi:10.1016/s0272-4944(05)80231-2 · Cited by 48,420
Scoping studies: towards a methodological framework
Arksey, H., & O'Malley, L. (2005) · International Journal of Social Research Methodology · doi:10.1080/1364557032000119616 · Cited by 36,742
Scoping studies: advancing the methodology
Levac, D., Colquhoun, H., & O'Brien, K. K. (2010) · Implementation Science · doi:10.1186/1748-5908-5-69 · Cited by 15,268
High-performance medicine: the convergence of human and artificial intelligence
Topol, E. J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0300-7 · Cited by 10,450
Predicting the future - big data, machine learning, and clinical medicine
Obermeyer, Z., & Emanuel, E. J. (2016) · New England Journal of Medicine · doi:10.1056/nejmp1606181 · Cited by 3,652
International evaluation of an AI system for breast cancer screening
McKinney, S. M., Sieniek, M., Godbole, V., Godwin, J., Antropova, N., Ashrafian, H., Back, T., Chesus, M., Corrado, G. S., Darzi, A., et al. (2020) · Nature · doi:10.1038/s41586-019-1799-6 · Cited by 3,287
Steinert, Y., Mann, K., Centeno, A., Dolmans, D., Spencer, J., Gelula, M., & Prideaux, D. (2006) · Medical Teacher · doi:10.1080/01421590600902976 · Cited by 1,417
A deep learning system for differential diagnosis of skin diseases
Liu, Y., Jain, A., Eng, C., Way, D. H., Lee, K., Bui, P., Kanada, K., de Oliveira Marinho, G., Gallegos, J., Gabriele, S., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0842-3 · Cited by 853
The Use of the Delphi and Other Consensus Group Methods in Medical Education Research: A Review
Humphrey-Murto, S. M., Varpio, L., Wood, T. J., Gonsalves, C., Ufholz, L., Mascioli, K., Wang, C., & Foth, T. (2017) · Academic Medicine · doi:10.1097/acm.0000000000001812 · Cited by 638
Cited by
Sorted by citations
Items 301–326 of 326
Zhu-ge, Y., Yao, X.-T., Mei, H., Yao, H.-X., & Chen, Q. (2026) · BMC Medical Education · doi:10.1186/s12909-026-09734-2
Toward a Regional Standard: Minimum Competencies in AI for MERCOSUR. (Preprint)Preprint
González, R., Godoy, J. A. R., & Cubas, J. (2026) · doi:10.2196/preprints.105035
Ismatovna, А. Y. (2026) · International Journal of Educational Research · doi:10.1016/j.ijer.2026.103044
Stefanova, V. P., & Zhekov, K. (2026) · doi:10.2196/preprints.104475
Yap, C. H. J., & Lim, Z. V. (2026) · Annals of the Academy of Medicine Singapore · doi:10.47102/annals-acadmedsg.2026346
Ahmad, A., O’Malley, A., & Gardner, A. (2026) · doi:10.2196/preprints.107119
Yang, Y., Li, S., Huang, L., Guo, Q., Hu, Y., & Shi, J. (2026) · BMC Medical Education · doi:10.1186/s12909-026-09993-z
Teachers’ adoption of GenAI: a model integrating ethics and creativity
Onbaşılı, Ü. İ., Batı, K., Yetişir, M. İ., & Doğan, C. D. (2026) · Educational Studies · doi:10.1080/03055698.2026.2710334
Nalbant, A. (2026) · Medical Science and Discovery · doi:10.36472/msd.v12i7.1380
Tang, N., Duan, Z., & Wang, Y. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1896446
Wu, Z., Tan, Y., Chen, Q., Ye, X., Zhang, Q., Bi, J., & Li, H. (2026) · Anesthesiology and Perioperative Science · doi:10.1007/s44254-026-00176-z
A pioneering faculty training program for artificial intelligence in Ukrainian medical education
Bulbuk, O., Гаврищук, Л., Lisovsky, R., & Bulbuk, O. (2026) · BMC Medical Education · doi:10.1186/s12909-026-10097-x
Fan, T., Gao, T., Tang, W., Xu, Q., Wang, X., Su, Q., & Lyu, Q. (2026) · Frontiers in Public Health · doi:10.3389/fpubh.2026.1899528
Artificial intelligence in medical education: a narrative review across four functional domains
Zarei, M., Mozaffari, M., & Hajiani, Y. (2026) · Currents in Pharmacy Teaching and Learning · doi:10.1016/j.cptl.2026.102752
Duque, D. F., & Gomez‐Samper, A. F. (2026) · Plastic & Reconstructive Surgery Global Open · doi:10.1097/gox.0000000000008022
Kim, E.-H., & Kim, H. (2026) · CardioMetabolic Syndrome Journal · doi:10.51789/cmsj.2026.6.e27
Development of AI competencies within the medical curriculum
Cevallos, M. B. M., López, D. F. V., Avalos, C. H. M., Morocho, B. C. H., Ameza, J. D. S., & León, M. L. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1899639
Alamro, A. (2026) · Saudi Journal for Health Sciences · doi:10.4103/sjhs.sjhs_63_26
Canbay, F. Ç., Çitil, E. T., & Amanak, K. (2026) · Research Square · doi:10.21203/rs.3.rs-10411366/v1
Chandra, A., Singh, S., Maroof, K., & Khaliq, F. (2026) · Archives of Medicine and Health Sciences · doi:10.4103/amhs.amhs_324_25
Tan, M., Aziz, F., & de Vries, J. (2024) · Annals of Pharmacy Practice and Pharmacotherapy · doi:10.51847/jqaforib1a
Comparing Human and Generative AI Ethical Decision-Making in Computer Ethics Education
Tonbuloğlu, B., Kert, S. B., & Başal, A. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2734059
Özel, M. A., Naldemir, İ. F., Ozen, A. U., Zenginbal, Y. S., & Dagli, M. (2026) · Abdominal Radiology · doi:10.1007/s00261-026-05807-4 · Cited by 1
Kathirvel, A., Vellaichamy, P., Pillai, N. D., & Gobinath, V. M. (2026) · Medical Science Educator · doi:10.1007/s40670-026-02906-1
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
Aldalou, M., Brooks, W. S., & Wilson, A. B. (2026) · Anatomical Sciences Education · doi:10.1002/ase.70372
Metadata
- Authors
- Lee, J., Wu, A. S., Li, D., & Kulasegaram, K. (.
- Affiliations
- University of Toronto (CA)
- University of Ottawa (CA)
- University Health Network (CA)
- Corresponding author
- Lee, Juehea
- Venue
- Academic Medicine
- Publisher
- Oxford University Press (OUP)
- Published
- 2021-10-27
- Volume
- 96
- Issue
- 11S
- Pages
- S62-S70
- ISSN
- 1040-2446 (ISSN-L)
- OpenAlex
- W3189751383
- Cited by
- 324
- References
- 38
- Citations by year
- 2026: 80 · 2025: 103 · 2024: 69 · 2023: 54 · 2022: 16 · 2021: 1
- FWCI
- 2.88
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Academic Medicine (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Education › Innovations in Medical Education
- Social Sciences › Education › Artificial Intelligence in Education
- Keywords
- artificial intelligence, undergraduate medical education, AI curriculum, AI ethics, curriculum delivery, soft skills, empathy, medical competencies
- MeSH terms
- Artificial Intelligence, 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
