Unlock the Power of Machine Learning and Artificial Intelligence in Radiation Oncology

As a clinician, staying ahead of the curve in the rapidly evolving field of radiation oncology requires a deep understanding of the applications of machine learning and artificial intelligence (AI). “Machine Learning and Artificial Intelligence in Radiation Oncology: A Guide for Clinicians” by Barry S. Rosenstein is a comprehensive guide designed to bridge the gap between theoretical concepts and practical application.

A Structured Approach to Machine Learning in Radiation Oncology

This indispensable resource is divided into three sections, carefully crafted to provide a comprehensive understanding of machine learning in radiation oncology. The first section lays the foundation by exploring fundamental concepts of machine learning and radiation oncology, including techniques applied in genomics. The second section delves into translational opportunities, such as radiogenomics and autosegmentation, while the third section focuses on current clinical applications in clinical decision making, workflow integration, and cross-collaborations with industry.

A Multidisciplinary Approach to Radiation Oncology

This book brings together the expertise of practicing clinicians and research scientists, offering a unique blend of innovative clinical ideas and perspectives on translating research findings into clinical practice. Additionally, contributions from AI industry researchers provide insights into novel theoretical approaches and possibilities for academic collaborations. The diverse points-of-view from an international group of experts ensure a balanced understanding of this complex topic.

A Valuable Resource for Oncologists, Radiologists, and Biomedical Professionals

“Machine Learning and Artificial Intelligence in Radiation Oncology: A Guide for Clinicians” is an essential resource for oncologists, radiologists, and biomedical professionals seeking to enhance their knowledge of machine learning as a support for radiation oncology. With its comprehensive coverage and practical approach, this book is poised to become a go-to reference for anyone looking to harness the power of machine learning and AI in their work.

Published by Elsevier Science, this book is set to release on December 2, 2023. With its 312 pages, it offers a wealth of information on the applications of machine learning and AI in radiation oncology. ISBN: 9780128220009 (Hardcover) and 9780128220016 (E-book).