RT Journal Article
SR Electronic
T1 Nuclear Medicine and Artificial Intelligence: Best Practices for Evaluation (the RELAINCE Guidelines)
JF Journal of Nuclear Medicine
JO J Nucl Med
FD Society of Nuclear Medicine
SP 1288
OP 1299
DO 10.2967/jnumed.121.263239
VO 63
IS 9
A1 Jha, Abhinav K.
A1 Bradshaw, Tyler J.
A1 Buvat, Irène
A1 Hatt, Mathieu
A1 KC, Prabhat
A1 Liu, Chi
A1 Obuchowski, Nancy F.
A1 Saboury, Babak
A1 Slomka, Piotr J.
A1 Sunderland, John J.
A1 Wahl, Richard L.
A1 Yu, Zitong
A1 Zuehlsdorff, Sven
A1 Rahmim, Arman
A1 Boellaard, Ronald
YR 2022
UL http://jnm.snmjournals.org/content/63/9/1288.abstract
AB 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.