Reporting checklists: from a tool after publication to a tool before submission

A rtificial intelligence (AI) methods have attracted widespread interest in the field of medical imaging, and the increasing number of AI publications in radiological journals reflects this growing attention from researchers and journals alike.As the old saying goes, "interest is the best teacher, " yet interest in AI does not automatically translate into proper use and adequate reporting of AI methods.Promising results in published articles do not necessarily ensure high methodological quality.More often than not, incomplete reporting of methodology and the lack of data and code sharing hinder study evaluation and model replication.To address this issue, the Checklist for AI in Medical Imaging (CLAIM) was developed as a guide for the complete reporting of AI studies in medical imaging. 1 CLAIM has been widely adopted, with more than 800 articles citing the guideline as of March 14, 2025, and has also been used as a tool for quality assessment in systematic reviews of AI studies. 2 However, these systematic reviews, which typically focus on medical imaging studies using AI, highlight the limited quality of current studies.This raises the question of whether CLAIM has been used appropriately and how the reporting and methodological quality of AI studies in medical imaging can be improved.