European journal of cancer (Oxford, England : 1990)
August 14, 2026 · via Europe PMC
Perspectives of people affected by cancer toward generative AI in oncology: A scoping review of applications, evidence gaps, and future priorities.
Menz BD, Scarfo NL, Cornelisse E, +10 more, Hopkins AM.
AI summary — for doctors, generated from the abstract only
People affected by cancer are generally favourable toward generative AI that improves information accessibility, but their trust depends on personalisation, emotional appropriateness, human oversight and accuracy.
What was studied
A scoping review of 32 studies (4,919 participants) examining perspectives of people affected by cancer on generative AI in cancer care.
Key findings
- 21 studies on patient communication/education, 6 on lay-summary note generation, 2 on decision support
- Most measured usefulness (21/32), comprehension (20/32) and trust (14/32)
- Trust conditional on personalisation, emotional appropriateness, human oversight and accuracy
- Safety, transparency, equity and autonomy not assessed from patient perspectives
Clinical relevance
This suggests generative AI tools for patients should incorporate human oversight and personalisation, and that patient views on safety and equity remain unexplored.
Who should care
Limitations
Scoping review with descriptive synthesis; included studies concentrated on narrow applications.
Patients welcome AI that makes information clearer, but only with human oversight and accuracy.
This is an AI-generated summary of the published abstract, not a clinical recommendation. It may not reflect the full text or subsequent updates — verify against the original source before acting on it.
Abstract
Background
Generative AI is increasingly being explored in cancer care, yet little is known about whether these tools align with the priorities and concerns of the people they are intended to support. We conducted a scoping review to identify studies that examined the perspectives of people affected by cancer towards generative AI.
Methods
PubMed, Embase, Web of Science, and MEDLINE were searched to July 2026. Two independent reviewers screened identified records for peer-reviewed studies reporting primary empirical data on the perspectives of people affected by cancer regarding generative AI in cancer care contexts. Study characteristics, applications evaluated, and perspectives assessed were extracted and synthesised descriptively and narratively.
Results
Of 2441 records identified, 32 studies comprising 4919 participants were included. Of these, 29 assessed specific applications: 21 Patient Communication and Education, 6 Clinical Note Generation involving lay summaries, 2 Clinical Decision Support; and 3 assessed general perspectives on AI without a defined task. Where perspectives were assessed, studies predominantly measured usefulness (21/32), comprehension (20/32), and trust (14/32). People affected by cancer were generally favourable when generative AI improved the accessibility and readability of health information; however, trust was conditional on personalisation, emotional appropriateness, human oversight, and perceived accuracy. These perspectives may help inform the design and implementation of generative AI, including interface design, patient education, consent processes, workflow integration, governance, monitoring, and feedback mechanisms.
Conclusions
Perspectives towards generative AI involving people affected by cancer remain concentrated in a narrow subset of applications. Considerations for responsible AI deployment, including safety, transparency, equity, and autonomy, have not been assessed from the perspectives of people affected by cancer.
DOI
10.1016/j.ejca.2026.116990
PMID
42628140
Research metadata (titles, abstracts, authors, publication dates) is sourced from Europe PMC, which aggregates content from PubMed/MEDLINE, PubMed Central, and other providers under the U.S. National Library of Medicine (NLM/NCBI). NLM does not claim copyright on abstracts; the original journal publishers or authors may hold rights to the full articles. AI-generated summaries are derived only from the abstract, never the full text, and are always labeled as AI-generated.
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