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Nuclear Medicine and Artificial Intelligence: Best Practices for Evaluation (the RELAINCE Guidelines).
Jha, Abhinav K; Bradshaw, Tyler J; Buvat, Irène; Hatt, Mathieu; Kc, Prabhat; Liu, Chi; Obuchowski, Nancy F; Saboury, Babak; Slomka, Piotr J; Sunderland, John J; Wahl, Richard L; Yu, Zitong; Zuehlsdorff, Sven; Rahmim, Arman; Boellaard, Ronald.
Afiliação
  • Jha AK; Department of Biomedical Engineering and Mallinckrodt Institute of Radiology, Washington University in St. Louis, Missouri; a.jha@wustl.edu.
  • Bradshaw TJ; Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin.
  • Buvat I; LITO, Institut Curie, Université PSL, U1288 Inserm, Orsay, France.
  • Hatt M; LaTiM, INSERM, UMR 1101, Univ Brest, Brest, France.
  • Kc P; Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, Maryland.
  • Liu C; Department of Radiology and Biomedical Imaging, Yale University, Connecticut.
  • Obuchowski NF; Quantitative Health Sciences, Cleveland Clinic, Cleveland, Ohio.
  • Saboury B; Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Maryland.
  • Slomka PJ; Department of Imaging, Medicine, and Cardiology, Cedars-Sinai Medical Center, California.
  • Sunderland JJ; Departments of Radiology and Physics, University of Iowa, Iowa.
  • Wahl RL; Mallinckrodt Institute of Radiology, Washington University in St. Louis, Missouri.
  • Yu Z; Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, Missouri.
  • Zuehlsdorff S; Siemens Medical Solutions USA, Inc., Hoffman Estates, Illinois.
  • Rahmim A; Departments of Radiology and Physics, University of British Columbia, Canada; and.
  • Boellaard R; Department of Radiology & Nuclear Medicine, Cancer Centre Amsterdam, Amsterdam University Medical Centers, Netherlands.
J Nucl Med ; 63(9): 1288-1299, 2022 09.
Article em En | MEDLINE | ID: mdl-35618476
An important need exists for strategies to perform rigorous objective clinical-task-based evaluation of artificial intelligence (AI) algorithms for nuclear medicine. To address this need, we propose a 4-class framework to evaluate AI algorithms for promise, technical task-specific efficacy, clinical decision making, and postdeployment efficacy. We provide best practices to evaluate AI algorithms for each of these classes. Each class of evaluation yields a claim that provides a descriptive performance of the AI algorithm. Key best practices are tabulated as the RELAINCE (Recommendations for EvaLuation of AI for NuClear medicinE) guidelines. The report was prepared by the Society of Nuclear Medicine and Molecular Imaging AI Task Force Evaluation team, which consisted of nuclear-medicine physicians, physicists, computational imaging scientists, and representatives from industry and regulatory agencies.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Medicina Nuclear Tipo de estudo: Guideline / Prognostic_studies Idioma: En Revista: J Nucl Med Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Medicina Nuclear Tipo de estudo: Guideline / Prognostic_studies Idioma: En Revista: J Nucl Med Ano de publicação: 2022 Tipo de documento: Article