Agentic AI for Precision Radiotherapy: A Multi-Agent Framework for Clinical Decision Support

Precision radiotherapy is becoming increasingly complex, requiring integration of multimodal imaging, treatment planning, adaptive radiotherapy, and diverse clinical data. This paper proposes agentic AI as a responsible clinical decision-support framework in which multiple specialized AI agents collaborate across imaging, contouring, treatment planning, quality assurance, adaptive radiotherapy, response monitoring, and multidisciplinary decision-making. Rather than replacing clinicians, these agents function within a coordinated ecosystem that emphasizes explainability, safety, fairness, privacy, regulatory compliance, and meaningful human oversight. Although challenges remain in clinical validation, interoperability, workflow integration, and governance, agentic AI offers a promising approach to transforming isolated AI applications into transparent, human-centered systems that support personalized cancer treatment.

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Publication Details

Journal
Radiation
Published
2026-10-07
DOI
https://doi.org/10.3390/radiation6040037
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
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article

Agentic AI for Precision Radiotherapy: A Multi-Agent Framework for Clinical Decision Support

Kay Li, James C. L. Chow
Radiation
Artificial Intelligence in Healthcare and Education
article

Agentic AI for Precision Radiotherapy: A Multi-Agent Framework for Clinical Decision Support

Kay Li, James C. L. Chow
article en

Abstract

Precision radiotherapy is becoming increasingly complex, requiring integration of multimodal imaging, treatment planning, adaptive radiotherapy, and diverse clinical data. This paper proposes agentic AI as a responsible clinical decision-support framework in which multiple specialized AI agents collaborate across imaging, contouring, treatment planning, quality assurance, adaptive radiotherapy, response monitoring, and multidisciplinary decision-making. Rather than replacing clinicians, these agents function within a coordinated ecosystem that emphasizes explainability, safety, fairness, privacy, regulatory compliance, and meaningful human oversight. Although challenges remain in clinical validation, interoperability, workflow integration, and governance, agentic AI offers a promising approach to transforming isolated AI applications into transparent, human-centered systems that support personalized cancer treatment.

RadiationVol. 6(4)
University Health Network (CA), University of Toronto (CA), Princess Margaret Cancer Centre (CA)
Openalex Percentile: Top 19%
Artificial Intelligence in Healthcare and Education
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