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.

迫切需要对核医学人工智能 (AI) 算法进行严格、客观、基于临床任务的评估的策略。为了满足这一需求，我们提出了一个 4 类框架来评估 AI 算法的前景、特定技术任务的功效、临床决策和部署后的功效。我们提供了评估每个类别的人工智能算法的最佳实践。每类评估都会产生一个声明，提供人工智能算法的描述性性能。主要最佳实践被列为 RELAINCE（核医学人工智能评估建议）指南。该报告由核医学和分子成像人工智能工作组评估小组编写，该小组由核医学医师、物理学家、计算成像科学家以及行业和监管机构的代表组成。