SynthesisFrontiers in medicine2026
Mapping artificial intelligence in problem-based and case-based medical education: a bibliometric analysis (2019-2026).
Synthesis in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Problem-based learning (PBL) has been a cornerstone of medical education since its introduction at McMaster University in the 1960s. Since the public release of ChatGPT in November 2022, artificial intelligence (AI) tools have increasingly been applied to PBL and case-based learning (CBL) contexts, yet the research landscape at this intersection remains poorly characterized. This study aimed to map the growth trajectory, thematic structure, and collaboration networks of AI-PBL/CBL research from 2019 to 2026. Methods: A comprehensive search of Scopus and Web of Science was conducted on June 2, 2026, combining AI-related terms with PBL/CBL frameworks and medical education contexts. Using a PRISMA-guided bibliometric review workflow, 1,616 records were identified; after deduplication and eligibility screening, 735 unique publications (2019-2026, original articles, reviews, conference papers, and other eligible indexed document types) were included. Bibliometric analyses employed VOSviewer for network visualization (keyword co-occurrence, co-authorship, co-citation), CiteSpace for citation burst detection, and Bibliometrix for thematic mapping, three-field plot, and factorial analysis. Results: Publication output grew from 25 papers in 2022 to 254 in 2025, with 206 papers indexed by June 2, 2026. The United States ( Conclusion: To our knowledge, this is the first bibliometric study specifically focused on the intersection of AI technologies with PBL/CBL in health professions education. The findings reveal rapid growth after 2023, four distinct but interconnected research clusters, and a collaboration network led by the United States and China, with uneven regional participation. These results may inform curriculum design and research priorities in AI-enhanced medical education.
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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.