ArticlePLOS digital health2026
Transparency, accountability, and scholarly integrity: A systematic narrative review of artificial intelligence governance frameworks in healthcare publication.
Article in PLOS digital health, 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
The integration of artificial intelligence (AI) in healthcare research and scholarly publication has produced a rapidly expanding but fragmented ecosystem of reporting guidelines, governance frameworks, editorial policies, and regulatory instruments, imposing significant burdens on researchers, reviewers, and editors. This systematic narrative review identified, stratified, and synthesized governance frameworks and reporting standards addressing transparent and ethical AI use in healthcare publications, and characterized the governance trajectory from predictive model standards to generative AI oversight. Fourteen Boolean search strings were executed in PubMed (MEDLINE) covering 1 January 2018-19 May 2026, supplemented by manual searching of the EQUATOR Network guideline library. Records were deduplicated and prioritized using a relevance-scoring algorithm. Of 30,276 deduplicated records, 888 progressed to human screening; 100 publications were included, comprising 69 governance instruments and policies classified across four evidence tiers and 31 contextual evidence sources. Included sources spanned six thematic groups: predictive and diagnostic AI frameworks (G1); sociotechnical and lifecycle governance (G2); generative AI and LLM-specific standards (G3); specialty-specific extensions (G4); surgical reporting updates (G5); and editorial and publication policy (G6). Analysis identified four consecutive phases of governance development from 2018 to 2026, characterized by a shift from model performance transparency toward comprehensive sociotechnical accountability. Three governance tensions emerged: the trade-off between framework specificity and universal applicability; the gap between disclosure-focused standards and lifecycle governance; and the risk of epistemic conflation, whereby heterogeneous instruments are perceived as carrying equivalent authority when treated as interchangeable. AI governance in healthcare publications remains fragmented and epistemically uneven. Advancing the field requires harmonization across evidence tiers, adaptive updating mechanisms, and lifecycle-oriented governance frameworks.
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.