Department of Biomedical Engineering and Mallinckrodt Institute of Radiology, Washington University in St. Louis, Missouri;
Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin.
J Nucl Med. 2022 Sep;63(9):1288-1299. doi: 10.2967/jnumed.121.263239. Epub 2022 May 26.
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.
对于人工智能（AI）算法在核医学中的严格客观临床任务评估策略存在重要需求。为满足这一需求，我们提出了一个 4 级框架，用于评估 AI 算法的承诺、特定技术任务的功效、临床决策和部署后的功效。我们为每一类评估提供了最佳实践。每一类评估都会产生一个声明，提供对 AI 算法的描述性性能。关键最佳实践被列在 RELAINCE（核医学中 AI 评估的建议）指南中。该报告由核医学和分子成像 AI 工作组评估团队编写，成员包括核医学医师、物理学家、计算成像科学家以及来自工业界和监管机构的代表。
