| Format | Publisher PDF |
|---|---|
| Size | 9.7 MB |
| Publisher | Elsevier |
| Edition | 1 |
| Language | ‎English |
Machine Learning and Artificial Intelligence in Radiation Oncology: A Guide for Clinicians is a state-of-the-art resource designed to help medical professionals understand and apply AI-powered tools in the context of modern radiation therapy. This original PDF bridges the gap between complex algorithms and clinical workflows—turning machine learning models into actionable insights.
It is an essential read for oncologists, radiologists, physicists, data scientists, and postgraduate students involved in cancer treatment and digital health transformation.
This book sits at the intersection of Bioinformatics and clinical oncology, offering practical guidance on how to incorporate machine learning into radiation planning, image analysis, and outcome prediction. It introduces both the fundamentals of AI and the real-world barriers to implementation in high-stakes healthcare settings.
| Feature | Description |
|---|---|
| Format | Original PDF from Publisher |
| Target Audience | Radiation oncologists, AI researchers, clinical physicists, residents |
| Topics Covered | Deep learning, image segmentation, outcome prediction, AI ethics in oncology |
| Level | Advanced / Clinical / Research |
Clinicians applying AI-based tools in radiotherapy
Researchers building predictive oncology models
Residents in medical physics and computational oncology
Health tech professionals improving clinical decision support
If you’re working at the interface of data science and oncology, Machine Learning and Artificial Intelligence in Radiation Oncology: A Guide for Clinicians (Original PDF from Publisher) is your go-to reference for responsible and effective implementation.
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