图书简介
The clinical use of Artificial Intelligence (AI) in radiation oncology is in its infancy. However, it is certain that AI is capable of making radiation oncology more precise and personalized with improved outcomes. Radiation oncology deploys an array of state-of-the-art technologies for imaging, treatment, planning, simulation, targeting, and quality assurance while managing the massive amount of data involving therapists, dosimetrists, physicists, nurses, technologists, and managers. AI consists of many powerful tools which can process a huge amount of inter-related data to improve accuracy, productivity, and automation in complex operations such as radiation oncology.This book offers an array of AI scientific concepts, and AI technology tools with selected examples of current applications to serve as a one-stop AI resource for the radiation oncology community. The clinical adoption, beyond research, will require ethical considerations and a framework for an overall assessment of AI as a set of powerful tools.30 renowned experts contributed to sixteen chapters organized into six sections: Define the Future, Strategy, AI Tools, AI Applications, and Assessment and Outcomes. The future is defined from a clinical and a technical perspective and the strategy discusses lessons learned from radiology experience in AI and the role of open access data to enhance the performance of AI tools. The AI tools include radiomics, segmentation, knowledge representation, and natural language processing. The AI applications discuss knowledge-based treatment planning and automation, AI-based treatment planning, prediction of radiotherapy toxicity, radiomics in cancer prognostication and treatment response, and the use of AI for mitigation of error propagation. The sixth section elucidates two critical issues in the clinical adoption: ethical issues and the evaluation of AI as a transformative technology.Key FeaturesBridges the gap between basic didactics and frontline research, responding to the growing amount of literature for AI in radiation oncologyPractical while being broad enough to provide enough overview to develop an AI implementation strategyPresents a possible ground-breaking pathway to improve the precision and scope of radiation oncology, especially in: treatment planning, personal therapy, natural language processing, error mitigation and productivity improvementsInterdisciplinary collaboration brings a rich combination of contributions, with plenty of cross-pollination among different fields of research bringing a nuanced perspective
Define the Future: Clinical Radiation Oncology in 2040 — Vision for Future RO from Clinical Perspective; A Vision for Radiation Therapy in 2030; Strategy: Lessons from the Artificial Intelligence in Radiology for Radiation Oncology; Open Access Data to Enable AI Applications in Radiation Therapy; AI Tools: Science and Tools of Radiomics for Radiation Oncology; Artificial Intelligence for Image Segmentation in Radiation Oncology; Knowledge Representation for Radiation Oncology; Natural Language Processing for Radiation Oncology; AI Applications: Knowledge-Based Treatment Planning: An Efficient and Reliable Planning Technique towards Treatment Planning Automation; Artificial Intelligence in Radiation Therapy Treatment Planning; Clinical Application of AI for Radiation Therapy Treatment Planning; Using AI to Predict Radiotherapy Toxicity Risk Based on Patient Germline Genotyping: Opportunities and Obstacles; Utilization of Radiomics in Cancer Prognostication and Treatment Response; How AI Can Help Us Understand and Mitigate Error Propagation in Radiation Oncology; Assessment and Outcomes: Ethics and Artificial Intelligence in Radiation Oncology; Evaluation of Artificial Intelligence in Radiation Oncology
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