Edited by Gilmer Valdes and Lei Xing
Md Akhtaruzzaman, PhD, Radiation Oncology Department, Evercare Hospital Chattogram, Bangladesh
Artificial intelligence (AI) is increasingly being incorporated into radiation oncology and medical physics. A thorough overview of the most recent developments in AI applications in radiation oncology and biomedical physics may be found in the book “Artificial Intelligence in Radiation Oncology and Biomedical Physics“, which was released by CRC Press in 2023. This book, which is edited by two well-known specialists, Gilmer Valdes and Lei Xing, brings together the works of top researchers and specialists from around the globe and offers insightful information about how AI might improve patient care., treatment planning, and clinical decision-making.
The book covers a wide range of AI applications in medical physics and radiation therapy and is organized into eight chapters. Before diving into machine learning-based outcome prediction, treatment planning, picture segmentation, motion management, and quality assurance, it starts with an overview of AI’s importance in radiotherapy. A forward-looking view of the developing function of AI in medical physics and radiation oncology is given in the final chapter.
The following topics are included:
- AI-driven automation in radiotherapy – Techniques such as auto-contouring, dose prediction, and knowledge-based planning, error detection, outcome modeling.
- Machine learning for radiotherapy outcome prediction – Utilizing AI to improve patient-specific treatment outcomes using various imaging modalities, quantitative imaging biomarkers, machine learning predictors.
- Image registration and segmentation – Applying deep learning models for precise tumor and organ segmentation by anatomical region.
- Reinforcement learning in treatment planning and image processing – Enhancing radiation dose delivery optimization and improving personalized treatment approaches through advanced data analysis.
- AI and ML in motion management and image guided radiation therapy – Exploring the roles of AI and ML in inter- and intra-fractional imaging, adaptive planning, and real-time tumor tracking.
- Quality assurance and error detection – AI’s contribution to improving radiation therapy’s safety and effectiveness, including patient-specific QA, treatment delivery, and chart review.
- Challenges and future directions – Interpretability, ethical issues, and clinical validation of AI models. Furthermore, to guarantee safety and effectiveness, the incorporation of AI into clinical practice necessitates frequent upgrades and strict standards.
The main strength of the book is its practical orientation. Medical physicists, radiation oncologists, and AI researchers working in the healthcare sector may find it especially helpful as each chapter covers theoretical ideas of AI while also emphasizing practical applications. The book is enhanced with citations to the latest AI techniques, case examples, and illustrations. Another notable aspect of the book is its emphasis on clinical translation. Rather than merely presenting AI models, it assesses their feasibility in medical settings, considering challenges such as data constraints, regulatory barriers, and interpretability concerns.
Although the book is highly informative but it requires some knowledge of medical physics, radiation oncology, and artificial intelligence. Some parts may be difficult for readers who
don’t have a strong foundation in machine learning and artificial intelligence. Furthermore, given how quickly AI is developing, some of the approaches presented may soon be replaced by more sophisticated ones. It would be helpful to get updates on new developments in medical physics AI in a future edition.
For professionals and researchers who want to understand how AI affects medical physics and cancer treatment, “Artificial Intelligence in Radiation Oncology and Biomedical Physics” is a valuable resource. The book provides a progressive viewpoint on how machine learning can revolutionize radiotherapy while skillfully bridging the gap between AI research and clinical practice. The book adds a lot to the literature on artificial intelligence in healthcare, even though some of its portions could be technically challenging. For specialists in radiation oncology, medical imaging, and related fields, it is therefore strongly advised.