Artificial intelligence (AI), machine learning (ML), and deep learning (DL) based approaches have begun to transform measurement, dosimetry, and quality assurance processes in radiotherapy and radiation safety. The increasing complexity of modern radiotherapy techniques, the need for sustained machine performance, growing demands for patient-specific quality assurance, and the widespread adoption of image-guided treatments have exposed the limitations of conventional measurement based and periodic quality control approaches.

By extracting meaningful patterns from large and high dimensional datasets, AI provides powerful solutions for continuous monitoring of machine behavior, enabling patient-specific quality assurance with reduced or even measurement-free workflows, improving accuracy in imaging processes, and achieving clinically feasible computation times for Monte Carlo based dose calculations. In addition, AI-driven early warning systems for monitoring patient, occupational, and environmental radiation exposure facilitate a predictive and proactive approach to radiation safety. This section reviews current AI applications in radiotherapy measurement, dosimetry, quality assurance, and radiation safety, supported by examples from the literature, and discusses existing limitations as well as the potential for future clinical integration.