ReviewTechnical innovations & patient support in radiation oncology2026
Mapping patients' and professionals' perceptions of artificial intelligence in radiotherapy: a scoping review.
Review in Technical innovations & patient support in radiation oncology, 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.
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Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Artificial intelligence (AI) is increasingly integrated into radiotherapy workflows. Evidence on patients' and professionals' attitudes toward AI in radiotherapy remains limited and fragmented. Objectives: To summarize the current evidence on patients and professionals' attitudes toward AI in radiotherapy, identify knowledge gaps, and highlight priorities for future research. Methods: 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. Results: 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. Conclusions: 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.
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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 OpenQuestion graph.