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.
Authors
- Kay Li (ORCID: https://orcid.org/0000-0002-5765-1635)
- James C. L. Chow (ORCID: https://orcid.org/0000-0003-4202-4855)
Institutions
- University Health Network (CA)
- University of Toronto (CA)
- Princess Margaret Cancer Centre (CA)
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