Baroudi Hana, Brock Kristy K, Cao Wenhua, Chen Xinru, Chung Caroline, Court Laurence E, El Basha Mohammad D, Farhat Maguy, Gay Skylar, Gronberg Mary P, Gupta Aashish Chandra, Hernandez Soleil, Huang Kai, Jaffray David A, Lim Rebecca, Marquez Barbara, Nealon Kelly, Netherton Tucker J, Nguyen Callistus M, Reber Brandon, Rhee Dong Joo, Salazar Ramon M, Shanker Mihir D, Sjogreen Carlos, Woodland McKell, Yang Jinzhong, Yu Cenji, Zhao Yao
Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA.
Diagnostics (Basel). 2023 Feb 10;13(4):667. doi: 10.3390/diagnostics13040667.
Developers and users of artificial-intelligence-based tools for automatic contouring and treatment planning in radiotherapy are expected to assess clinical acceptability of these tools. However, what is 'clinical acceptability'? Quantitative and qualitative approaches have been used to assess this ill-defined concept, all of which have advantages and disadvantages or limitations. The approach chosen may depend on the goal of the study as well as on available resources. In this paper, we discuss various aspects of 'clinical acceptability' and how they can move us toward a standard for defining clinical acceptability of new autocontouring and planning tools.
基于人工智能的放射治疗自动轮廓勾画和治疗计划工具的开发者和使用者，需要评估这些工具的临床可接受性。然而，什么是“临床可接受性”呢？定量和定性方法都已被用于评估这个定义不明确的概念，所有这些方法都有优点和缺点或局限性。所选择的方法可能取决于研究目标以及可用资源。在本文中，我们将讨论“临床可接受性”的各个方面，以及它们如何能推动我们朝着定义新的自动轮廓勾画和计划工具临床可接受性的标准迈进。
