Artificial Intelligence in Medicine: A Systematic Review of Guidelines for the Reporting and Interpretation of Studies

Abstract

Reporting guidelines, developed for medical artificial intelligence (AI) studies, are structured tools that address general and/or AI-specific methodological and reporting issues.We aimed to systematically review published medical AI reporting guidelines and checklists, and evaluate aspects that can support the choice of the tool, in a particular research context.We searched PubMed, Scopus, and Web of Science thru February 2023, as well as, Citations and Google.From 821 records, and additional sources, 24 guidelines were identified (4 narrative guidelines, 7 general reporting checklists, 4 study design specific checklists, and 9 clinical area specific checklists).13 studies reported the guideline development methods, 10 guidelines were registered in the Enhancing the QUAlity and Transparency Of health Research (EQUATOR) Network.In 224 sections, the guidelines contained 704 items in total.The number of items per checklist varied between 10 and 66.The guidelines' structure and level of detail varied significantly which makes difficult for researchers to follow how detailed and standardized a medical AI study design and report should be.The robustness of development process and support from the literature suggests that the AI extension of checklist for randomized controlled trials (CONSORT-AI guideline) as the most established tool.Such AI extensions of clinical study guidelines may not cover all the application fields of AI in medicine.In certain research contexts, an established checklist for main clinical study types, and a general AI-based checklist may be used in parallel to provide most useful guidance in designing, writing and interpreting medical AI studies.

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