A narrative review on radiotherapy practice in the era of artificial intelligence: how relevant is the medical physicist?

Abstract

Background and Objective: Artificial intelligence (AI) uses computers and machines to simulate how the human mind makes decisions and solves problems. In radiotherapy practice, AI technologies continue to be promising in image registration, synthetic computed tomography (CT), image segmentation, motion management, treatment planning, and delivery procedures, patient follow-up and quality assurance (QA). This, therefore, provides a new window of opportunity to improve upon the accuracy and output times of the manual implementation of these procedures. The goal of this review was to explore how machine learning AI technologies in radiotherapy could affect the clinical practice of medical physicists. Methods: A narrative literature review was conducted from PubMed, Science Direct and Scopus using the search terms: in the English language within 6 months. Key Content and Findings: The roles of AI and the clinical medical physicist are complementary in radiotherapy practice. Both the medical physicists and AI technology are highly needed to support the full implementation and optimization of radiotherapy procedures. Conclusions: To achieve successful implementation of AI in radiotherapy and optimize radiotherapy procedures, clinical medical physicist should receive some compulsory training in AI technologies during their education and training. They should ultimately be involved in the incorporation of machine learning technologies in radiotherapy equipment. patient-specific dosimetric of patient treatment Dosimetric measurements in phantoms one of following detectors: portal imaging The third type of looks for delivery errors in log files generated during during delivery time-series linear quality