ArticleCancer medicine2026
Cancer Patients' Perception, Acceptance, and Utilization of Artificial Intelligence-Based Emotional Distress Assessment Tools: A Scoping Review.
Article in Cancer medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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Who cites it
1 citing paper in PubMed.
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Authors and funding
4 authors.
Funding
Abstract
objectiveEmotional distress in cancer patients and survivors impacts overall well-being and quality of life. Several barriers to adequate screening have been identified and are currently being addressed by artificial intelligence (AI)-based tools. However, there is a critical need to explore cancer patients' and survivors' perspectives on these new technologies. This scoping review aims to synthesize the available evidence on their perception, acceptance, and utilization of AI-based voice, speech semantics, and facial expression (AIVSFE) tools for emotional distress screening.
methodsA systematic search was conducted in Scopus, Web of Science, PubMed Central, Cochrane Central Register of Controlled Trials (CENTRAL), PsycINFO, and Epistemonikos on July 1, 2025. Empirical studies published from January 1, 2019, to the search date that focused on adult cancer patients at any stage of treatment or survivorship and their perception, acceptance, or use of AIVSFE tools were retrieved. Participant sociodemographics, AI-based distress screening modalities, technological frameworks, measurement tools, outcomes, and the studies' methodological quality were analyzed.
resultsThree studies met the eligibility criteria. They included a combined sample of 316 cancer patients and survivors with heterogeneous clinical characteristics. Two studies utilized speech semantics technologies, while one utilized facial expression technology. The results show high acceptance, satisfaction, and usefulness rates (70%-98%), suggesting AIVSFE tools could address barriers associated with traditional distress screening.
conclusionThe findings indicate a favorable view of AIVSFE tools for detecting distress. Future studies should prioritize developing standardized evaluation frameworks, diversifying participant demographics, and addressing broader usability and ethical concerns to ensure equitable adoption of these technologies.
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