PT  - JOURNAL ARTICLE
AU  - Jha, Abhinav K.
AU  - Bradshaw, Tyler J.
AU  - Buvat, Irène
AU  - Hatt, Mathieu
AU  - KC, Prabhat
AU  - Liu, Chi
AU  - Obuchowski, Nancy F.
AU  - Saboury, Babak
AU  - Slomka, Piotr J.
AU  - Sunderland, John J.
AU  - Wahl, Richard L.
AU  - Yu, Zitong
AU  - Zuehlsdorff, Sven
AU  - Rahmim, Arman
AU  - Boellaard, Ronald
TI  - Nuclear Medicine and Artificial Intelligence: Best Practices for Evaluation (the RELAINCE guidelines)
AID  - 10.2967/jnumed.121.263239
DP  - 2022 May 01
TA  - Journal of Nuclear Medicine
PG  - jnumed.121.263239
4099  - http://jnm.snmjournals.org/content/early/2022/05/26/jnumed.121.263239.short
4100  - http://jnm.snmjournals.org/content/early/2022/05/26/jnumed.121.263239.full
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 four-class framework to evaluate AI algorithms for promise, technical task-specific efficacy, clinical decision making, and post-deployment 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 taskforce Evaluation team, which consisted of nuclear-medicine physicians, physicists, computational imaging scientists, and representatives from industry and regulatory agencies.