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TRUVACE RECORD VERSION
record: TRV-2026-2026
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-20T18:12:37.014734Z
status: published
lens: p_space
sector: science
headline: Transparency, accountability, and scholarly integrity: A systematic narrative review of artificial intelligence governance frameworks in healthcare publication
dek: Author summary Artificial intelligence is increasingly used in healthcare research, from developing clinical tools to helping prepare scientific papers. At the same time, many guidelines and policies have been created to tell researchers, journals, and reviewers how this work should be reported and managed. We reviewed this rapidly growing landscape to understand what guidance exists, how it has changed over time, and where important gaps remain. We identified 100 relevant publications, including 69 guidelines,…
gain_title: (none)
problem_title: Guidance for AI in healthcare research and publishing remains fragmented, varies in authority and development, and often covers disclosure at publication rather than responsibilities across an AI system's life.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: Guidance for AI in healthcare research and publishing remains fragmented, varies in authority and development, and often covers disclosure at publication rather than responsibilities across an AI system's life.
problem_evidence: the current landscape remains fragmented | many focus mainly on disclosure when a paper is published rather than responsibilities throughout the life of an artificial intelligence system
quick_read: Researchers reviewed the growing body of guidance on how AI work in healthcare research should be reported and managed. They identified 100 relevant publications, 69 of which were guidelines, frameworks, or institutional or editorial policies, and traced how the focus changed over time.

The finding matters because researchers, journals, and reviewers receive uneven direction on AI use in clinical tools and in paper preparation. The authors call for better coordination, clearer choice of standards, and attention to post-publication responsibilities. The excerpt does not give detail on the review methods or on how any specific guidance performed in practice.
limitation: The source states that the guidance landscape is fragmented, with documents varying in development and authority and often focusing on disclosure at publication rather than ongoing responsibilities.
tag: Evidence-backed problem
key_points: The review identified 100 relevant publications, including 69 guidelines, frameworks, and institutional or editorial policies. | Guidance shifted from model performance toward fairness, transparency, human oversight, and generative AI use in research and publishing. | Documents differ greatly in how they were developed and how much authority they carry. | Many documents emphasize disclosure at publication rather than responsibilities across an AI system's life.
rundown: The authors describe AI as increasingly used in healthcare research, from developing clinical tools to helping prepare scientific papers. Many guidelines and policies have been written to tell researchers, journals, and reviewers how such work should be reported and managed.

The review examined what guidance exists, how it has changed over time, and where important gaps remain. Early emphasis on how models perform has broadened to include fairness, transparency, human oversight, and generative AI in scholarly publishing.

The authors conclude that the field needs better coordination of existing guidance, clearer direction on which standards apply in different situations, and stronger attention to responsibilities beyond publication.
sources:
- journalism | PLOS (Public Library of Science) | https://journals.plos.org/digitalhealth/article?id=10.1371%2Fjournal.pdig.0001783 | 2026-10-20
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