In the rapidly evolving landscape of radiation therapy, the integration of artificial intelligence (AI) in adaptive radiation therapy (ART) brings unprecedented advancements but also necessitates a comprehensive understanding of the associated risks and safety considerations that must be made. When implementing new technology into clinical practice, the departmental risk management and staff training program must be re-evaluated to accommodate the changing workflow [1]. The process of utilizing AI ART methods for patient treatment differs substantially from standard linac-based external beam workflows [2, 3]. Specifically, real-time plan adaptation requires an in-depth understanding of patient anatomy, contour quality, and treatment planning. This dynamic adaptation process introduces distinct roles for therapists, physicists, and physicians that may differ from their prior practice. The intricacies of real-time plan adaptation necessitate specialized training to ensure that healthcare professionals have the expertise required to navigate the nuances of patient-specific anatomy and optimize treatment plans effectively. Therefore, to safely and effectively implement AI ART, updates should be made to the risk management and staff training programs, and appropriate end-to-end testing should be performed before clinical deployment.

Prior to introducing ART into clinical practice, a series of prospective risk assess­ments should be performed in order to proactively anticipate and mitigate issues before they occur. These risk assessments should be performed by a multidiscipli­nary team. To determine the appropriate specialties that should be represented on the team, the process should first be examined from start to end to identify all role groups that lend a hand to the process. For example, successful implementation of AI ART requires contributions from radiation therapists, both during CT simu­lation and at the treatment machine, dosimetrists, medical physicists, and radiation oncologists [3]. Therefore, a representative should be nominated from each role group to participate in the risk assessment process. At a minimum, we recommend that a failure mode and effects analysis and hazard testing of the major components of the ART workflow be performed prior to go-live. Each of these methods will be discussed in the following sections.