DOI 10.1007/s00520-026-11254-0
Exploring young adult cancer survivors' perspectives on generative AI chatbots for symptom support.
Аннотация
BACKGROUND
Young adult cancer survivors (ages 18-39) often experience persistent treatment-related symptoms that negatively affect daily functioning, psychosocial well-being, and quality of life, yet report unmet educational needs for symptom management. Generative artificial intelligence (AI) technologies, such as large language model-powered chatbots, offer scalable, personalized, and empathetic symptom support but remain largely untested in young adults' survivorship care.
OBJECTIVE
This qualitative descriptive study explored young adult cancer survivors' perspectives, preferences, and experiences related to the use of generative AI chatbots for oncology symptom management.
METHODS
Eighteen post-treatment young adult cancer survivors who had completed cancer treatment within the past two years were recruited from the University of Michigan Rogel Cancer Center. Participants completed the PROMIS-29, PROMIS Self-Efficacy for Managing Symptoms, and the Digital Health Literacy Scale to quantify symptom burden, self-efficacy, and digital readiness. Semi-structured interviews explored experiences with cancer treatment-related symptoms and AI use, including preferences for personalization, tone, functionality, and data governance.
RESULTS
Qualitative analyses revealed three themes: (1) the need for personalized, emotionally supportive, and credible AI symptom guidance; (2) accessible and user-centered design drives engagement with AI symptom support; and (3) trust, privacy, and human oversight as preconditions for AI symptom support. Survivors viewed AI as a supplemental resource to enhance symptom monitoring, decision support, and emotional reassurance without replacing clinical care.
CONCLUSION
Generative AI chatbots were perceived as acceptable and potentially valuable for AYA symptom management. Findings can guide the development of tailored digital self-management interventions responsive to the complex and evolving needs of this population.
Предметные рубрики MeSH
Подготовка и перевод публикации
Обработка статьи · этапы, ошибки и управление
Ошибки и ограничения
История · было → стало
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