Nuclear Medicine and Artificial Intelligence: Best Practices for Evaluation (the RELAINCE Guidelines).
PET
SPECT
artificial intelligence
best practices
clinical decision making
clinical task
evaluation
generalizability
postdeployment
technical efficacy
Journal
Journal of nuclear medicine : official publication, Society of Nuclear Medicine
ISSN: 1535-5667
Titre abrégé: J Nucl Med
Pays: United States
ID NLM: 0217410
Informations de publication
Date de publication:
09 2022
09 2022
Historique:
received:
17
09
2021
revised:
11
05
2022
pubmed:
27
5
2022
medline:
13
10
2022
entrez:
26
5
2022
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 35618476
pii: jnumed.121.263239
doi: 10.2967/jnumed.121.263239
pmc: PMC9454473
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1288-1299Subventions
Organisme : NCATS NIH HHS
ID : UL1 TR001863
Pays : United States
Informations de copyright
© 2022 by the Society of Nuclear Medicine and Molecular Imaging.
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