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Generative AI, foundation models and large language models in radiation therapy physics: Clinical applications,
X Sharon Qi1, Yi Wang2, Xiaofeng Yang3
1Department of Radiation Oncology, University of California Los Angeles, Los Angeles, California, USA.
Medical Physics
|August 7, 2026
Summary
Generative AI, Foundation Models, and Large Language Models offer significant potential in radiation oncology for data analysis and workflow optimization. However, careful consideration of educational, ethical, and regulatory challenges is crucial for successful implementation.
Area of Science:
- Artificial Intelligence in Medicine
- Medical Physics and Radiation Oncology
Background:
- Generative AI (Gen AI), Foundation Models (FMs), and Large Language Models (LLMs) are advanced AI technologies with broad data processing capabilities.
- These technologies are increasingly recognized for their potential to improve healthcare outcomes, particularly in radiation oncology.
Purpose of the Study:
- To provide a comprehensive overview of Gen AI, FMs, and LLMs in radiation therapy.
- To highlight the benefits, opportunities, risks, and challenges associated with implementing these technologies in clinical practice.
Main Methods:
- This scoping review synthesizes current literature on Gen AI, FMs, and LLMs in the context of radiation oncology.
- The review examines applications, potential benefits, and implementation challenges.
Main Results:
- Gen AI, FMs, and LLMs can analyze complex data, synthesize medical images, automate tasks, support decision-making, and enhance clinical trials.
- Key challenges include interpretability, data privacy, regulatory compliance, reproducibility, and workflow integration.
Conclusions:
- Gen AI, FMs, and LLMs represent a transformative era in radiation therapy, offering significant advancements.
- Addressing educational, ethical, and regulatory considerations is essential for harnessing the full potential of these disruptive technologies.