Checklist for Artificial Intelligence in Medical Imaging (CLAIM): 2024 Update

S ince its introduction, the Checklist for Artificial Intelli- gence in Medical Imaging (CLAIM) has sought to promote complete and consistent reporting of artificial intelligence (AI) science in medical imaging (1).CLAIM has been adopted widely in several medical specialties that involve imaging and AI; as of February 2024, PubMed identified 275 articles that cite the original guideline, and Google Scholar identified 608 citations.CLAIM is one of several reporting guidelines developed to address AI and medical imaging (2).Although not designed as a scoring system, some authors have applied it as such and have found variable adherence among published articles (3-6).Some authors have identified opportunities to improve the guideline, such as separating complex items in the original guideline and accommodating rapidly evolving techniques (7).The CLAIM Steering Committee sought to revise, improve, and formalize the guideline (8).The authors renewed CLAIM's registration with the Enhancing the Quality and Transparency of Health Research (EQUA-TOR) Network, an organization that promotes the use of reporting guidelines to improve health research (https://www.equator-network.org)(9,10).The CLAIM Steering Committee developed and conducted a formal Delphi consensus survey process to review the appropriateness and importance of existing checklist items and to identify new content to reflect current science in AI.The authors recruited 73 volunteers, including physicians from a variety of medical imaging-related specialties, AI scientists, journal editors, and statisticians to form the CLAIM 2024 Update Panel; 72 members completed the two survey rounds and are listed as contributors to this work.To address AI's rapid scientific evolution, this article presents the CLAIM 2024 Update (see Table and see Appendix for downloadable Word document).Based on an expert-panel Delphi process, the guideline's recommendations promote consistent reporting of scientific advances of AI in medical imaging to build trust in published results and enable clinical translation.This guideline serves as an educational tool for both authors and reviewers; it offers a best practice checklist to promote transparency and reproducibility of medical imaging AI research.