Evidence map›Paper›PMID 39023999›Full record

ArticleJMIR medical education2024

Curriculum Frameworks and Educational Programs in AI for Medical Students, Residents, and Practicing Physicians: Scoping Review.

Raymond Tolentino, Ashkan Baradaran, Genevieve Gore, Pierre Pluye, Samira Abbasgholizadeh-Rahimi

Abstract readScoping Review
In one paragraph

Article in JMIR medical education, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 81 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
81citing papers in PubMed, 3 pooled it
–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

81 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Article
  5. Article
  6. AI In Leukemia Diagnostics: Complementing the Pathologist's Role.International journal of laboratory hematology · 2026
    Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Review
  14. Article
  15. Article
  16. Article
  17. Ethical and Legal Implications of Implementing AI in Gastrointestinal Endoscopy.Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society · 2026
    Review
  18. Article
  19. Article
  20. Article

21 more citing papers are in PubMed but not listed here.

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.

Raymond TolentinoDepartment of Family Medicine, McGill University, Montreal, QC, Canada.ORCID 0000-0002-7758-314X
Ashkan BaradaranDepartment of Family Medicine, McGill University, Montreal, QC, Canada.ORCID 0000-0002-7982-8032
Genevieve GoreSchulich Library of Physical Sciences, Life Sciences, and Engineering, McGill University, Montreal, QC, Canada.ORCID 0000-0003-1072-2683
Pierre PluyeDepartment of Family Medicine, McGill University, Montreal, QC, Canada.ORCID 0000-0002-9274-7720
Samira Abbasgholizadeh-RahimiDepartment of Family Medicine, McGill University, Montreal, QC, Canada.ORCID 0000-0003-3781-1360

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe successful integration of artificial intelligence (AI) into clinical practice is contingent upon physicians' comprehension of AI principles and its applications. Therefore, it is essential for medical education curricula to incorporate AI topics and concepts, providing future physicians with the foundational knowledge and skills needed. However, there is a knowledge gap in the current understanding and availability of structured AI curriculum frameworks tailored for medical education, which serve as vital guides for instructing and facilitating the learning process.

objectiveThe overall aim of this study is to synthesize knowledge from the literature on curriculum frameworks and current educational programs that focus on the teaching and learning of AI for medical students, residents, and practicing physicians.

methodsWe followed a validated framework and the Joanna Briggs Institute methodological guidance for scoping reviews. An information specialist performed a comprehensive search from 2000 to May 2023 in the following bibliographic databases: MEDLINE (Ovid), Embase (Ovid), CENTRAL (Cochrane Library), CINAHL (EBSCOhost), and Scopus as well as the gray literature. Papers were limited to English and French languages. This review included papers that describe curriculum frameworks for teaching and learning AI in medicine, irrespective of country. All types of papers and study designs were included, except conference abstracts and protocols. Two reviewers independently screened the titles and abstracts, read the full texts, and extracted data using a validated data extraction form. Disagreements were resolved by consensus, and if this was not possible, the opinion of a third reviewer was sought. We adhered to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist for reporting the results.

resultsOf the 5104 papers screened, 21 papers relevant to our eligibility criteria were identified. In total, 90% (19/21) of the papers altogether described 30 current or previously offered educational programs, and 10% (2/21) of the papers described elements of a curriculum framework. One framework describes a general approach to integrating AI curricula throughout the medical learning continuum and another describes a core curriculum for AI in ophthalmology. No papers described a theory, pedagogy, or framework that guided the educational programs.

conclusionsThis review synthesizes recent advancements in AI curriculum frameworks and educational programs within the domain of medical education. To build on this foundation, future researchers are encouraged to engage in a multidisciplinary approach to curriculum redesign. In addition, it is encouraged to initiate dialogues on the integration of AI into medical curriculum planning and to investigate the development, deployment, and appraisal of these innovative educational programs. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.11124/JBIES-22-00374.

Indexed as

Artificial IntelligenceCurriculumStudents, MedicalEducation, MedicalHumansInternship and ResidencyPhysiciansartificial intelligencecurriculumframeworkmachine learningmedical educationreview

Identifiers

PMID39023999
PMCPMC11294785

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.