Evidence map›Paper›PMID 37398823›Full record

ReviewCureus2023

The Global Use of Artificial Intelligence in the Undergraduate Medical Curriculum: A Systematic Review.

Jonny R Varma, Sherwin Fernando, Brian Y Ting, Shahrukh Aamir, Rajesh Sivaprakasam

Abstract readReview
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

20 citing papers in PubMed.

  1. Trial
  2. AI-Based Learning Experiences of Nursing Students: A Qualitative Study.Journal of evaluation in clinical practice · 2026
    Article
  3. Review
  4. Article
  5. Observational
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Review
  13. Review
  14. Artificial intelligence in respiratory care.Frontiers in digital health · 2024
    Review
  15. Article
  16. Article
  17. Review
  18. Article
  19. Article
  20. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Jonny R VarmaUndergraduate Medical Education, Barts and The London School of Medicine and Dentistry, London, GBR.
Sherwin FernandoUndergraduate Medical Education, Barts and The London School of Medicine and Dentistry, London, GBR.
Brian Y TingUndergraduate Medical Education, Barts and The London School of Medicine and Dentistry, London, GBR.
Shahrukh AamirUndergraduate Medical Education, Barts and The London School of Medicine and Dentistry, London, GBR.
Rajesh SivaprakasamRenal Transplant, Royal London Hospital, London, GBR.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

artificial intelligenceeducationmedical educationmedical studentssystematic reviewundergraduate medical education

Identifiers

PMID37398823
PMCPMC10309075

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.