Checklist for Artificial Intelligence in Medical Imaging (CLAIM): Explanation, Elaboration, and Examples
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.
Authors
- Lisa C. Adams (ORCID: https://orcid.org/0000-0001-5836-4542)
- Amine Amyar (ORCID: https://orcid.org/0000-0002-1689-1544)
- Merel Huisman (ORCID: https://orcid.org/0000-0003-2289-7130)
- Ricardo A. Gonzales (ORCID: https://orcid.org/0000-0002-9384-4602)
- Mana Moassefi (ORCID: https://orcid.org/0000-0002-0111-7791)
- Tugba Akinci D’Antonoli (ORCID: https://orcid.org/0000-0002-7237-711X)
- Anthony A. Gatti (ORCID: https://orcid.org/0000-0001-6717-8979)
- Ken Chang (ORCID: https://orcid.org/0000-0001-6956-5059)
- Salvatore Claudio Fanni (ORCID: https://orcid.org/0000-0002-4003-3320)
- John Mongan (ORCID: https://orcid.org/0000-0003-2765-7451)
- Gunvant Chaudhari (ORCID: https://orcid.org/0000-0003-2872-3828)
- Michail E. Klontzas (ORCID: https://orcid.org/0000-0003-2731-933X)
- Ali Shah Tejani (ORCID: https://orcid.org/0000-0002-6862-5299)
- Mahsa Mayeli (ORCID: https://orcid.org/0000-0003-4656-156X)
- Charles E. Kahn (ORCID: https://orcid.org/0000-0002-6654-7434)
- Dogan S. Polat (ORCID: https://orcid.org/0000-0001-7483-7485)
Institutions
- University of Pisa (IT)
- Beth Israel Deaconess Medical Center (US)
- Mayo Clinic (US)
- University of Crete (GR)
- Radboud University Nijmegen (NL)
- University of California, San Francisco (US)
- University of Washington (US)
- University Hospital of Basel (CH)
- TUM Klinikum (DE)
- Radboud University Medical Center (NL)
- Yale University (US)
- University Children’s Hospital Basel (CH)
- Athinoula A. Martinos Center for Biomedical Imaging (US)
- Stanford Medicine (US)
- Universidad de Ingeniería y Tecnología (PE)
- University of Pennsylvania (US)
- Stanford University (US)
- Icahn School of Medicine at Mount Sinai (US)
Publication Details
- Journal
- Radiology Artificial Intelligence
- Published
- 2026-09-30
- DOI
- https://doi.org/10.1148/ryai.260835
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00