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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\u2010world healthcare systems. This paper, developed through the Canadian AI and Data in Radiotherapy Alliance (CADRA), presents a collaborative perspective on preparing for an AI\u2010enabled future in radiation medicine, emphasizing that AI must be understood and governed as a tool shaped by human values, professional judgment, and patient priorities.<\/jats:p>\n                  <jats:p>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\u2010to\u2010practice curricula and continuing professional development. Third, it emphasizes the importance of interprofessional and pan\u2010Canadian collaboration, leveraging existing structures and national and international partnerships to support coordinated adoption, data standardization, and shared learning.<\/jats:p>\n                  <jats:p>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\u2010enabled care. By aligning technological innovation with collaborative governance, evolving education models, and patient\u2010centered values, this paper outlines a practical and ethical pathway for integrating AI into radiation medicine.<\/jats:p>","DOI":"10.1002\/acm2.70658","type":"journal-article","created":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T07:04:01Z","timestamp":1784012641000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["The complementary roles of oversight, education, and collaboration in the responsible integration of artificial intelligence in radiation medicine: White paper of CADRA"],"prefix":"10.1002","volume":"27","author":[{"given":"Caitlin","family":"Gillan","sequence":"first","affiliation":[{"name":"Canadian Artificial intelligence and Data in Radiotherapy Alliance (CADRA)  Toronto Canada"},{"name":"BC Cancer  Vancouver Canada"},{"name":"Division of Radiation Oncology Department of Surgery 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