Full title: evaluating AI guidelines in leading family medicine journals: a cross-sectional study.

BACKGROUND: Artificial intelligence (AI) is increasingly integrated into family medicine research and practice, enhancing diagnostics, data analysis, and care delivery. Yet, its rapid adoption has outpaced the development of standardized editorial policies, raising concerns about transparency, ethics, and reproducibility. Clear guidance from journals is urgently needed to ensure responsible use of AI in research and publishing. OBJECTIVE: To evaluate editorial policies and reporting guideline endorsements related to AI across leading FM journals. METHODS: Using the SCImago Journal Rank database, we conducted a cross-sectional analysis of FM journals. From November 2024 to January 2025, we reviewed publicly available Instructions for Authors for AI-related policies, including authorship, manuscript writing, content/image generation, and disclosure. We also assessed whether journals endorsed AI-specific RGs (e.g., CONSORT-AI, SPIRIT-AI). Data were extracted in duplicate using a standardized form. Reproducibility was supported through protocol registration on Open Science Framework. RESULTS: Of 57 FM journals identified, 40 met inclusion criteria. Among these, 82.5% (33/40) referenced AI in their policies. Most (77.5%) prohibited AI authorship and required disclosure of AI use, while 72.5% permitted AI-assisted manuscript writing. Policies on AI-generated content and images varied, with 47.5% and 50.0% of journals allowing their use, respectively. Only 5.0% (2/40) endorsed AI-specific RGs. No correlation was observed between journal characteristics and AI policy adoption. CONCLUSIONS: Most family medicine journals now address AI use, but notable gaps remain, particularly in endorsing AI-specific reporting guidelines. Without broader adoption of structured guidance, AI-integrated research risks inconsistency, limited reproducibility, and ethical challenges. Strengthening journal policies and endorsing standardized reporting frameworks is essential to ensure high-quality, trustworthy AI research in family medicine.