The Global Use of Artificial Intelligence in the Undergraduate Medical Curriculum: A Systematic Review
Open accessAbstract
Artificial intelligence (AI) is a rapidly advancing technology that has the potential to revolutionize medical education. AI can provide personalized learning experiences, assist with student assessment, and aid in the integration of pre-clinical and clinical curricula. Despite the potential benefits, there is a paucity of literature investigating the use of AI in undergraduate medical education. This study aims to evaluate the role of AI in undergraduate medical curricula worldwide and compare AI to current teaching and assessment methods. This systematic review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting guidelines. Texts unavailable in English were excluded alongside those not focused on medical students alone or with little mention of AI. The key search terms were "undergraduate medical education," "medical students," "medical education," and "artificial intelligence." The methodological rigor of each study was assessed using the Medical Education Research Study Quality Instrument (MERSQI). A total of 36 articles were screened from 700 initial articles, of which 11 were deemed eligible. These were categorized into the following three domains: teaching (n = 6), assessing (n = 3), and trend spotting (n = 2). AI was shown to be highly accurate in studies that directly tested its ability. The mean overall MERSQI score for all selected papers was 10.5 (standard deviation = 2.3; range = 6 to 15.5) falling below the expected score of 10.7 due to notable weaknesses in study design, sampling methods, and study outcomes. AI performance was synergized with human involvement suggesting that AI would be best employed as a supplement to undergraduate medical curricula. Studies directly comparing AI to current teaching methods demonstrated favorable performance. While shown to have a promising role, there remains a limited number of studies in the field, and further research is needed to refine and establish clear foundations to assist in its development.
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Several studies pointed to the infrastructure, financial resources, and investments necessary to implement AI-driven education methods, as the costs of servers, faculty training, tool development, and technical personnel could be substantial.
“Finally, several studies pointed to the infrastructure, financial resources, and investments necessary to implement AI-driven education methods [9, 50, 181], as the costs of servers, faculty training, tool development, and technical personnel could be substantial [182–184].”
From Artificial intelligence in undergraduate medical education: an updated scoping review (2025) · cites this paper as [181]
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Experimental — accuracy not yet measuredVery few studies have attempted to categorize the AI tools based on their uses for relevant stakeholders such as students, faculty members, and administrators.
“Although the current studies focused on analyzing the trends of AI usage in medical education, very few studies have attempted to categorize the AI tools based on their uses for relevant stakeholders (e.g., students, faculty members, and administrators) [3,6].”
From Artificial Intelligence Revolutionizing the Field of Medical Education (2023) · cites this paper as [6]
AI can influence the student learning process in three ways: direct teaching, support teaching, and empowering the learner.
“AI can influence the student learning process in three ways: direct teaching (imparting knowledge to the student while playing the role of a teacher), support teaching (playing a supporting role and collaborating with students as they learn), and empowering the learner (multiple students can collaborate to solve a complex problem upon feedback provided by the teacher) [2,3,6].”
From Artificial Intelligence Revolutionizing the Field of Medical Education (2023) · cites this paper as [6]
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- Authors
- Varma, J. R., Fernando, S., Ting, B. Y., Aamir, S., & Sivaprakasam, R.
- Affiliations
- Queen Mary University of London (GB)
- Corresponding author
- Varma, Jonny R
- Venue
- Cureus
- Published
- 2023-05-30
- Volume
- 15
- Issue
- 5
- Pages
- e39701-e39701
- ISSN
- 2168-8184 (ISSN-L)
- PMC
- PMC10309075
- OpenAlex
- W4381571322
- Cited by
- 41
- References
- 31
- Citations by year
- 2026: 12 · 2025: 13 · 2024: 12 · 2023: 4
- FWCI
- 0.48
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- Cureus (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- 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
- artificial intelligence, undergraduate medical education, medical curriculum, personalized learning, student assessment, human-AI collaboration
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