AI evidence extraction

Generative artificial intelligence and editorial ethics: a roadmap for Health & New Media Research

Ghee Young Noh · Health & New Media Research · 2025

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

9/10

Relevance

2/4

Quality (LMQS)

Evidence

Citations

0.00

FWCI

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.22720/hnmr.2025.00304

Methodology & findings

Study design

Narrative review and editorial synthesis of international guidance from COPE, ICMJE, WAME, and major publishers; drawing on emerging empirical evidence and scholarly consensus to propose policy recommendations for a health and new media research journal..

Main result

The paper establishes that "AI tools cannot be credited as authors" and that "their use must be transparently disclosed," while "human authors, editors, and reviewers remain fully responsible for every aspect of scholarly content." The study also identifies that "Large language models are prone to hallucinations and overgeneralization: they produce fluent, confident statements that are not fully supported by the underlying evidence," with particular concern for health-related communication where "misrepresentations, exaggerated claims of effectiveness, or inaccurate risk communication can have direct consequences for patient behavior, health seeking decisions, and public trust."

Reports effect sizes and confidence intervals.

Research paradigm

Normative/prescriptive ethics and policy analysis

Author conclusions

The authors conclude that "By adopting principled, transparent, and context‑aware guidelines now, Health & New Media Research can help ensure that AI strengthens—rather than undermines—the reliability and social value of health and new media scholarship." The editorial also states that "For Health & New Media Research, these general principles must be adapted to a context where health information, digital platforms, and AI technologies are deeply entangled. That adaptation requires clear, field‑sensitive policies that enable legitimate, equity‑enhancing uses of AI for language editing and workflow support, that set strict expectations for transparency, documentation, and human oversight, that protect the integrity of data, images, and health‑related conclusions, and that provide coherent guidelines not only for authors but also for editors and peer reviewers."

Open questions raised

The paper identifies the need for field-sensitive AI policies that are tailored to health and new media contexts, recognizing that "explicit AI related editorial policies are now an ethical necessity" and that generic guidance must be adapted to the specific risks present in health-related communication and data-sensitive research.
The paper identifies the need for field-specific AI policies adapted to health and media contexts. It calls for policies that "enable legitimate, equity‑enhancing uses of AI for language editing and workflow support, that set strict expectations for transparency, documentation, and human oversight, that protect the integrity of data, images, and health‑related conclusions, and that provide coherent guidelines not only for authors but also for editors and peer reviewers."
The editorial identifies the need for field-sensitive policies adapted to health and new media contexts, and notes that "Such policies should be treated as living documents subject to regular review as technologies and practices evolve."

Extracted from: pdfAgreement 76%

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