ReviewJournal of applied clinical medical physics2026
The complementary roles of oversight, education, and collaboration in the responsible integration of artificial intelligence in radiation medicine: White paper of CADRA.
Review in Journal of applied clinical medical physics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
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.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.