This ground-breaking book investigates the effects of AI approaches, such as machine learning, on millions of cancer patients who benefit from ionizing radiation therapy. With an emphasis on the clinical applications of machine learning for medical physics, it includes contributions from researchers and clinicians around the world.

In recent times, artificial intelligence (AI) and machine learning have gained significant traction in the medical field. Commercial software and other clinical components now incorporate machine learning features. A review of clinical applications is included, with a focus on radiomics, outcome prediction, registration and segmentation, treatment planning, quality assurance, image processing, and clinical decision-making. General concepts and significant machine learning approaches are then presented. Lastly, a futuristic perspective on AI’s application in radiation oncology is given.

This book provides radiation oncologists and medical physicists with the most recent developments in machine learning applications for medical physics. Practitioners will value each chapter’s comprehensive explanations and thought-provoking debates. A large audience in the medical physics field is reached by its emphasis on clinical applications.