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From policing authors to stewarding science: Governing artificial intelligence across the scientific publishing pipeline in medical journals

Aug 2026 · MIT Science Policy Review · Vol 7, pp. 5-20 · 1 citation · 25 references

TL;DR

It is argued that effective AI governance in scientific publishing cannot be achieved through static rules or journal-centric control alone, and requires a shift toward shared, adaptive oversight of the scientific publishing system.

Abstract

Generative artificial intelligence (AI) is rapidly transforming how scientific knowledge is produced, reviewed, and disseminated. In response, journals and publishing organizations have begun issuing policies to govern AI use in scholarly publishing. However, it remains unclear whether existing governance frameworks meaningfully address the risks AI introduces across the full publication pipeline. We conducted a narrative review of journal policies, publisher guidance, and recent analyses of AI governance in scientific publishing, complemented by direct examination of submission guidelines from high-impact, open-access, and regional medical journals. Our findings show that while most journals have converged on a narrow legal consensus (prohibiting AI authorship and requiring disclosure), yet governance remains fragmented and incomplete. Policies disproportionately target text generation by authors, while leaving critical domains under-regulated, including AI-assisted data analysis, peer review practices, enforcement mechanisms, and equity implications for researchers and reviewers globally. To synthesize these findings, we introduce the AI governance readiness levels, a five-level framework for assessing how well-equipped journals are to govern AI across the research and publication process. We further describe PRAIDE (Preparation, Representation, Attribution, Integrity checks, Dissemination, and Evaluation) as an illustrative architecture that integrates existing policies, integrity safeguards, and post-publication oversight into a coherent governance model. We argue that effective AI governance in scientific publishing cannot be achieved through static rules or journal-centric control alone. Instead, it requires a shift toward shared, adaptive oversight of the scientific publishing system, recognizing that responsibility for governing AI in science is collective, continuous, and inseparable from the public trust in research.

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