From creation to dissemination: a lifecycle-based evaluation of AI guidance policies in health-related journals

Abstract

The rapid integration of generative artificial intelligence into health sciences research has prompted major advisory bodies to establish ethical guidelines governing AI use in scholarly publishing. However, practical implementation at the journal level remains inconsistent, with considerable incongruence observed between individual journal policies and overarching publisher mandates. This study conducts a comprehensive analysis of AI guidance policies in international health-related journals to evaluate the current regulatory landscape and provide a robust framework for future guidance. A mixed-methods approach was employed, integrating qualitative subject analysis, inferential statistics, and Multiple Correspondence Analysis (MCA). Through qualitative subject analysis, an attribute-based regulatory schema spanning the research lifecycle was developed. Policy texts were independently evaluated using a dual-coder protocol and transformed into a standardized categorical dataset. Bivariate inferential statistics and MCA were subsequently applied to evaluate structural interrelationships between these regulatory stances and institutional characteristics. Descriptive analysis revealed profound regulatory disparities. While a robust consensus exists on prohibiting AI authorship (72.6%), severe regulatory vacuums persist in upstream research phases, with 54.2% of journals omitting guidance on methodological design. Bivariate analyses demonstrated that policy rigor is significantly determined by policy presentation mode (the presence and origin of a journal's policy) and journal prestige. Furthermore, MCA extracted three distinct regulatory typologies: Silent Vacuum prevalent among lower-ranked journals; Restrictive Hardliners enforcing strict bans on visual and methodological AI applications; and Autonomous Pragmatics, where journals with independent policies champion disclosure-based integration. This study reveals a polarized AI regulatory landscape across health-related journals, wherein institutions either maintain a complete policy vacuum or implement comprehensive frameworks. Policy presentation mode and journal prestige emerged as the primary structural determinants of regulatory content, with publisher-derived policies and high-impact journals consistently associated with more rigorous guidance. This study advances the theoretical understanding of scientific guidance by conceptualizing AI policy adoption as a complex interplay between administrative standardization and journal prestige. Practically, the proposed lifecycle-based framework serves as a vital diagnostic tool for editorial boards to address critical regulatory gaps, while simultaneously providing authors with a comprehensive guide to navigate the ethical application of generative technologies across the research process.