ReviewCureus2023
The Global Use of Artificial Intelligence in the Undergraduate Medical Curriculum: A Systematic Review.
Review in Cureus, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed.
- Integrating large language models into prostate cancer training: evidence from comparative benchmarking and a pilot randomized trial.BMC medical education · 2026Trial
- AI-Based Learning Experiences of Nursing Students: A Qualitative Study.Journal of evaluation in clinical practice · 2026Article
- Medical students' and faculty members' perceptions and experiences of AI integration in health care practice and in medical curricula: A meta-ethnographic review.Medical education · 2026Review
- Transformers in surgical artificial intelligence: A domain-stratified, study-level narrative review.JTCVS open · 2026Article
- Artificial intelligence-assisted training for rib fracture interpretation: a prospective study in undergraduate medical students.World journal of emergency surgery : WJES · 2026Observational
- Framework for AI integration into the undergraduate medical curricula: a scoping review.BMC medical education · 2026Article
- Competency-based curricula in the Eastern Mediterranean Region schools of pharmacy: a framework-informed mixed method study.Journal of pharmaceutical policy and practice · 2026Article
- Use and perception of generative artificial intelligence in traditional Korean medicine education: A cross-sectional survey of undergraduate students in Korea.Integrative medicine research · 2025Article
- Artificial intelligence in undergraduate medical education: an updated scoping review.BMC medical education · 2025Article
- Exploring Artificial Intelligence Readiness in Medical Students: Analysis of a Global Survey.Medical science educator · 2025Article
- Impact of Artificial Intelligence on the Knowledge, Attitude, and Performance of Ophthalmology Residents: A Systematic Review.Journal of ophthalmic & vision research · 2025Article
- The integration of AI in nursing: addressing current applications, challenges, and future directions.Frontiers in medicine · 2025Review
- Large Language Models in Oncology: Revolution or Cause for Concern?Current oncology (Toronto, Ont.) · 2024Review
- Artificial intelligence in respiratory care.Frontiers in digital health · 2024Review
- Comparing the Performance of ChatGPT-4 and Medical Students on MCQs at Varied Levels of Bloom's Taxonomy.Advances in medical education and practice · 2024Article
- Article
- Review
- Article
- Medical Education: Considerations for a Successful Integration of Learning with and Learning about AI.Journal of medical education and curricular developmentArticle
- Artificial Intelligence Generated Videos as Supportive Tools in Medical Education: A Scoping Review.Journal of medical education and curricular developmentReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.