Checklist for Artificial Intelligence in Medical Imaging (CLAIM): Explanation, Elaboration, and Examples.
Authors
Affiliations (17)
Affiliations (17)
- Division of Diagnostic and Interventional Neuroradiology, Department of Radiology, University Hospital Basel, Petersgraben 4, 4031 Basel, Switzerland.
- Department of Pediatric Radiology, University Children's Hospital Basel, Basel, Switzerland.
- Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technical University of Munich, School of Medicine and Health, TUM University Hospital, Munich, Germany.
- Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Mass.
- Department of Radiology, Stanford University, Stanford, Calif.
- Department of Radiology, University of Washington, Seattle, Wa.
- Department of Surgical, Medical, Molecular and Critical Area Pathology, University of Pisa, Pisa, Italy.
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, Mass.
- Faculty of Computing, University of Engineering and Technology-UTEC, Lima, Peru.
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, the Netherlands.
- Artificial Intelligence and Translational Imaging Lab, Department of Radiology, School of Medicine, University of Crete, Crete, Greece.
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, Conn.
- Department of Radiology, Mayo Clinic, Rochester, Minn.
- Icahn School of Medicine at Mount Sinai, New York, NY.
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif.
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa.
- Department of Medicine, Division of Clinical Informatics and Digital Transformation Received August 4, 2026; revision requested August 28; revision received September 4.
Abstract
The Checklist for Artificial Intelligence in Medical Imaging (CLAIM) provides a structured framework for transparent and reproducible reporting of AI studies in medical imaging. Since its introduction in 2020, CLAIM has been widely adopted by researchers, reviewers, and journal editors, but variability in its interpretation has limited consistent application. In 2024, the CLAIM Steering Committee published an updated checklist developed through a structured Delphi consensus process involving 72 experts across imaging-related medical specialties, AI science, journal editing, and biostatistics. This article provides a detailed explanation and elaboration of each of the 44 items in the CLAIM 2024 Update, clarifying the intent, common misinterpretations, and appropriate implementation of each item. Illustrative examples from the published literature demonstrating adherence to each item are provided in an accompanying Supplement and through a user-friendly online tool at https://rsna.github.io/claim/. The scope of this work spans the full AI study lifecycle covered by CLAIM, from study design and data sourcing to model development, evaluation, and reporting of results. This resource is intended to support authors, reviewers, and editors in the accurate and consistent application of CLAIM, thereby improving the quality, transparency, and reproducibility of AI research in medical imaging.