Quality Assurance for AI-Based Applications in Radiation Therapy

• Published in: Seminars in radiation oncology Vol. 32; no. 4; pp. 421 - 431
• Main Authors: Claessens, Michaël, Oria, Carmen Seller, Brouwer, Charlotte L., Ziemer, Benjamin P., Scholey, Jessica E., Lin, Hui, Witztum, Alon, Morin, Olivier, Naqa, Issam El, Van Elmpt, Wouter, Verellen, Dirk
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.10.2022
• ISSN: 1053-4296; 1532-9461
• DOI: 10.1016/j.semradonc.2022.06.011

Recent advancements in artificial intelligence (AI) in the domain of radiation therapy (RT) and their integration into modern software-based systems raise new challenges to the profession of medical physics experts. These AI algorithms are typically data-driven, may be continuously evolving, and their behavior has a degree of (acceptable) uncertainty due to inherent noise in training data and the substantial number of parameters that are used in the algorithms. These characteristics request adaptive, and new comprehensive quality assurance (QA) approaches to guarantee the individual patient treatment quality during AI algorithm development and subsequent deployment in a clinical RT environment. However, the QA for AI-based systems is an emerging area, which has not been intensively explored and requires interactive collaborations between medical doctors, medical physics experts, and commercial/research AI institutions. This article summarizes the current QA methodologies for AI modules of every subdomain in RT with further focus on persistent shortcomings and upcoming key challenges and perspectives.