Transparency, accountability, and scholarly integrity: A systematic narrative review of artificial intelligence governance frameworks in healthcare publication.

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.