Mapping patients' and professionals' perceptions of artificial intelligence in radiotherapy: a scoping review.

Authors

Frederik Voigt Carstensen, Belinda Bøgh Irankunda, Darja Molan, Desiree van den Bongard, Maja Vestmø Maraldo

Published in

Technical innovations & patient support in radiation oncology. Volume 40. Pages 100430. Epub Aug 26, 2026.

Abstract

Artificial intelligence (AI) is increasingly integrated into radiotherapy workflows. Evidence on patients' and professionals' attitudes toward AI in radiotherapy remains limited and fragmented.
To summarize the current evidence on patients and professionals' attitudes toward AI in radiotherapy, identify knowledge gaps, and highlight priorities for future research.
Studies including adult cancer patients receiving radiotherapy and/or radiotherapy professionals, reporting attitudes toward AI in radiotherapy were eligible for inclusion. The sources of evidence PubMed, MEDLINE, EMBASE, and CINAHL were searched on September 11, 2025. Two reviewers independently screened the studies. Data were extracted using a standardized form. Studies were categorized post hoc as positive, cautiously positive, cautiously negative, or negative based on overall orientation, and themes were identified inductively. The review followed PRISMA-ScR guidelines.
1901 studies were identified, and nineteen studies were included in the review. Most studies were cross-sectional surveys. Patients generally accepted AI when framed as supportive, but emphasized trust, transparency, and the desire to be informed when AI is being used. One study reported more skeptical patient views. Radiotherapy professionals were generally cautiously positive, seeing benefits for efficiency, consistency, and quality, but expressed concerns about deskilling, training gaps, governance, and accountability.
Attitudes toward AI in radiotherapy are predominantly cautiously positive but conditional on transparency, human oversight, adequate training, and robust governance. Addressing educational, organizational, and human factors alongside technical development is essential for safe and sustainable AI implementation in radiotherapy. Future research should prioritize longitudinal, qualitative, and implementation-focused studies.

PMID:
42732349
Bibliographic data and abstract were imported from PubMed on 13 Sep 2026.

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