The complementary roles of oversight, education, and collaboration in the responsible integration of artificial intelligence in radiation medicine: White paper of CADRA.

Authors

Caitlin Gillan, Brian Liszewski, Annie Hsu, Fariah Rahman, Mariam Ebady, Michelle Nielsen, Cristyana Aloysious, Yannie Lai, Erika Brown, Heather Donaldson, Amanda Caissie

Published in

Journal of applied clinical medical physics. Volume 27. Issue 7. Pages e70658.

Abstract

Artificial intelligence (AI) is poised to fundamentally transform radiation medicine, with growing influence across clinical decision-making, workflow efficiency, personalization of care, and quality assurance. While the technical potential of AI is well described in the literature, less attention has been given to how these tools should be responsibly implemented within real-world healthcare systems. This paper, developed through the Canadian AI and Data in Radiotherapy Alliance (CADRA), presents a collaborative perspective on preparing for an AI-enabled future in radiation medicine, emphasizing that AI must be understood and governed as a tool shaped by human values, professional judgment, and patient priorities. Following a concise overview of current and emerging AI applications in radiation medicine, the paper focuses on three interconnected domains critical to responsible implementation. First, it frames AI as an enabler of a future intentionally designed by the radiation medicine community, highlighting the need for thoughtful integration into clinical workflows, data governance structures, and oversight mechanisms that prioritize patient benefit. Second, it examines the implications of AI for education, professional roles, and scopes of practice, underscoring the need for comprehensive AI literacy embedded across entry-to-practice curricula and continuing professional development. Third, it emphasizes the importance of interprofessional and pan-Canadian collaboration, leveraging existing structures and national and international partnerships to support coordinated adoption, data standardization, and shared learning. Central to this perspective is the meaningful inclusion of patient voices in AI governance, design, and evaluation. Patient trust, transparency, accountability, and equity are identified as foundational requirements for AI-enabled care. By aligning technological innovation with collaborative governance, evolving education models, and patient-centered values, this paper outlines a practical and ethical pathway for integrating AI into radiation medicine.

PMID:
42444305
Bibliographic data and abstract were imported from PubMed on 14 Jul 2026.

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