ArticleBMJ open2025
Role of AI-supported case-based learning in medical education: a scoping review protocol.
Article in BMJ open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis 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
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- AI-supported case-based learning in medical education: a comprehensive scoping review.Frontiers in medicine · 2026Pooled it
- Application effect and teaching evaluation of case-based learning combined with ChatGPT in ophthalmology clinical teaching.Frontiers in medicine · 2026Article
- Application Status and Intelligent Development Prospects of Case-Based Learning in Standardized Training for Obstetric Residents: A Narrative Review.Advances in medical education and practice · 2026Review
- Generative artificial intelligence in medical education: from knowledge assessment to clinical reasoning and professional competence.Frontiers in medicine · 2026Review
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
introductionGenerative artificial intelligence (AI) tools are rapidly transforming case-based learning (CBL) within medical education. Despite growing interest, the literature is heterogeneous, fragmented and inconsistent in terminology and methodology. This scoping review aims to systematically map and synthesise evidence on the integration of generative AI in CBL, identifying key themes, educational outcomes, challenges and research gaps to guide future investigations, curricular innovation and policy development. METHOD AND ANALYSIS: A comprehensive search strategy, developed with a health sciences librarian, will be implemented across multidisciplinary databases, including PubMed/MEDLINE, ERIC, Scopus, Web of Science, EMBASE and CINAHL, covering publications from 2019 to 2025. Two independent reviewers will conduct title/abstract and full-text screening using predefined eligibility criteria. Data extraction will use standardised charting forms capturing study characteristics, AI applications, educational contexts, outcomes and user perceptions. Data synthesis will involve descriptive statistics and inductive thematic analysis to create an evidence map of generative AI-supported CBL in medical education. ETHICS AND DISSEMINATION: No ethics approval is required, as the review synthesises published literature. Findings will be disseminated through peer-reviewed journals, conferences and stakeholder networks to inform educators, researchers and policymakers.
Indexed as
Identifiers
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.