Artificial Intelligence Reporting Guidelines in Radiology: A Systematic Review
TL;DR
No single imaging-specific document comprehensively addresses model development, validation, diagnostic workflow integration, and post-deployment monitoring; complementary guidance may therefore be required.
Abstract
Background: Artificial intelligence (AI) is increasingly being used in radiology, prompting the development of reporting guidelines, checklists, position statements, and evaluation frameworks. This systematic review aimed to identify and characterize these documents, assess their methodological quality, and evaluate their applicability to AI tools supporting diagnostic imaging. Methods: PubMed, Scopus, Web of Science, Embase, and the Cochrane Library were searched in October 2025. English-language guidance documents first officially published or made available online between 2015 and 18 October 2025, including ahead-of-print articles, were eligible. Two reviewers independently screened records and assessed methodological quality using the Appraisal of Guidelines for Research and Evaluation II (AGREE II) with predefined project-specific interpretation guidance. The review was retrospectively registered in PROSPERO (CRD420261471153). Results: Sixteen guidance documents were included. Clarity of Presentation had the highest median AGREE II score (86.1%), whereas Rigour of Development had the lowest (35.4%). CLEAR, CLEAR-E3, the CLAIM 2024 Update and the European Society of Cardiovascular Radiology (ESCR) position statement were recommended for use; the remaining 12 documents were recommended with modifications. Conclusions: Radiology AI guidance is generally clear and applicable, but development methods are often insufficiently reported. No single imaging-specific document comprehensively addresses model development, validation, diagnostic workflow integration, and post-deployment monitoring; complementary guidance may therefore be required.