ArticleJMIR medical education2024
Curriculum Frameworks and Educational Programs in AI for Medical Students, Residents, and Practicing Physicians: Scoping Review.
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.
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
81 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Development of AI competencies within the medical curriculum.Frontiers in medicine · 2026Pooled it
- Content and structural needs assessment for an artificial intelligence education mobile app in healthcare: a mixed methods study.BMC medical education · 2025Pooled it
- Integrating artificial intelligence into medical education: a narrative systematic review of current applications, challenges, and future directions.BMC medical education · 2025Pooled it
- Artificial Intelligence Readiness in Emergency Medicine: Expert Consensus Opinion for Preparing the Workforce.Journal of the American College of Emergency Physicians open · 2026Article
- When the Algorithm Speaks First: Trust, Reliance and the Reconstruction of Clinical Metacognition.Journal of evaluation in clinical practice · 2026Article
- AI In Leukemia Diagnostics: Complementing the Pathologist's Role.International journal of laboratory hematology · 2026Review
- Generative Language Models in Medical Education: From Advent to Entrustment.JMIR medical education · 2026Article
- Self-Reported Knowledge, Attitudes, Perceptions, and Readiness Regarding AI Among Obstetrics and Gynecology Trainees: Cross-Sectional Study.JMIR formative research · 2026Article
- Role-Differentiated AI Competencies and Curriculum Implications for Health Professions Education: Qualitative Study.JMIR medical education · 2026Article
- AI-Empowered Nuclear Medicine Education, Part 3: Practical AI Applications for Learners.Journal of nuclear medicine technology · 2026Article
- AI Integration in Undergraduate Medical Education: Qualitative Study of Faculty Perspectives in the United Arab Emirates.JMIR medical education · 2026Article
- Use, Concerns, and Perspectives on AI in Health Care Among French Health Professionals and Students: Web-Based Cross-Sectional Survey.JMIR medical education · 2026Article
- AI literacy among healthcare professionals and students in the Americas.Lancet regional health. Americas · 2026Review
- Teaching AI ethics in medical schools: a scoping review protocol on the ethical-technical balance in curricular frameworks.BMJ open · 2026Article
- Artificial Intelligence Integration in U.S. Healthcare Professional Degree Programs: A Rapid Scoping Review.Research square · 2026Article
- AI Integration in Spanish Undergraduate Medical Education: National Cross-Sectional Study.JMIR medical education · 2026Article
- Ethical and Legal Implications of Implementing AI in Gastrointestinal Endoscopy.Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society · 2026Review
- How to Teach Generative Artificial Intelligence in Undergraduate Medical Education.The clinical teacher · 2026Article
- Towards an accessible, centralised, searchable database for AI courses in Europe: the Artificial Intelligence in Medical Imaging and Radiation Oncology Education (AIMIROE) project.European radiology experimental · 2026Article
- Defining the artificial intelligence knowledge gap in surgery: experience and perspectives from surgical resident and postgraduate.Updates in surgery · 2026Article
21 more citing papers are in PubMed but not listed here.
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
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.
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What OpenQuestion holds
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.