Evidence map›Paper›PMID 40034416›Full record

ReviewCureus2025

Applications of Artificial Intelligence in Medical Education: A Systematic Review.

Eric Hallquist, Ishank Gupta, Michael Montalbano, Marios Loukas

Abstract readReview
In one paragraph

Review in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.

0numbers the graph read from it
0cells of the map it votes in
28citing 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

28 citing papers in PubMed.

  1. Trial
  2. Article
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  4. Review
  5. Article
  6. Article
  7. Article
  8. Blending Artificial Intelligence with Expertise: A Comparative Study of Concept Maps in Anatomy Education.Medical principles and practice : international journal of the Kuwait University, Health Science Centre · 2026
    Article
  9. Article
  10. Article
  11. Article
  12. Review
  13. Article
  14. Article
  15. Review
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article
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

4 authors.

Eric HallquistDepartment of Family Medicine, Prevea Shawano Avenue Health Center, Green Bay, USA.
Ishank GuptaDepartment of Anatomical Sciences, St. George's University School of Medicine, St. George, GRD.
Michael MontalbanoDepartment of Anatomical Sciences, St. George's University School of Medicine, St. George, GRD.
Marios LoukasDepartment of Anatomical Sciences, St. George's University School of Medicine, St. George, GRD.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) models, like Chat Generative Pre-Trained Transformer (OpenAI, San Francisco, CA), have recently gained significant popularity due to their ability to make autonomous decisions and engage in complex interactions. To fully harness the potential of these learning machines, users must understand their strengths and limitations. As AI tools become increasingly prevalent in our daily lives, it is essential to explore how this technology has been used so far in healthcare and medical education, as well as the areas of medicine where it can be applied. This paper systematically reviews the published literature on the PubMed database from its inception up to June 6, 2024, focusing on studies that used AI at some level in medical education, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Several papers identified where AI was used to generate medical exam questions, produce clinical scripts for diseases, improve the diagnostic and clinical skills of students and clinicians, serve as a learning aid, and automate analysis tasks such as screening residency applications. AI shows promise at various levels and in different areas of medical education, and our paper highlights some of these areas. This review also emphasizes the importance of educators and students understanding AI's principles, capabilities, and limitations before integration. In conclusion, AI has potential in medical education, but more research needs to be done to fully explore additional areas of applications, address the current gaps in knowledge, and its future potential in training healthcare professionals.

Indexed as

machine learning algorithmsmedical education assessmentmedical examinationnatural language processing modelsteaching undergraduate and postgraduate

Identifiers

PMID40034416
PMCPMC11872247

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.