SynthesisFrontiers in medicine2026
Artificial intelligence for medical imaging education: bibliometric and visual analysis.
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
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: While bibliometric analyses have examined the adoption of Artificial Intelligence (AI) in general medical education contexts, the specific knowledge structures, collaborative networks, and evolutionary pathways of AI for medical imaging education remain poorly characterized quantitatively, particularly regarding the transition from technical feasibility to systematic pedagogical integration and cross-institutional partnerships. Methods: A structured literature search of English-language original articles and reviews published between 2006 and 2025 was performed in the Web of Science Core Collection (WoS) and Scopus, with the former serving as the primary database and the latter for external validation. Bibliometric analyses covering citation trends, collaboration networks, keyword bursts, and journal distributions were conducted using CiteSpace, VOSviewer, and Bibliometrix. Results: This search retrieved 577 articles from WoS, revealing exponential growth since 2019, with 226 articles published in 2025. These publications were contributed by 3,739 authors from 1,479 institutions across 80 countries/regions. Cross-database validation supported the consistency of these findings (Spearman r = 0.97, Conclusion: This study characterizes a dual-polar production structure with the United States and China as dominant contributors, and a journal distribution spanning medical education and imaging technology venues. The post-2022 emergence of generative AI keywords documented a new thematic cluster alongside foundational machine learning research, indicating an evolving methodological repertoire. These findings provide a quantitative baseline for evidence synthesis in this interdisciplinary domain, with implications for curriculum development, algorithmic performance benchmarks, and ethical governance, subject to standard bibliometric data coverage and currency constraints.
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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.