Evidence map›Paper›PMID 42773243›Full record

ArticleSupportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer2026

Exploring young adult cancer survivors' perspectives on generative AI chatbots for symptom support.

Yupawadee Kantabanlang, Grace Kanzawa-Lee, Rachel A Pozzar, Robert Knoerl

Abstract read
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Article in Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Yupawadee KantabanlangDepartment of Human Behavior and Clinical Sciences, University of Michigan School of Nursing, Ann Arbor, MI, 48109, USA.
Grace Kanzawa-LeeDepartment of Human Behavior and Clinical Sciences, University of Michigan School of Nursing, Ann Arbor, MI, 48109, USA.
Rachel A PozzarPhyllis F. Cantor Center for Research in Nursing and Patient Care Services, Dana-Farber Cancer Institute, Boston, MA, 02215, USA.
Robert KnoerlDepartment of Health Behavior and Clinical Sciences, University of Michigan School of Nursing, 400 North Ingalls St., Office 2350, Ann Arbor, MI, 48109, USA. rjknoerl@med.umich.edu.ORCID http://orcid.org/0000-0002-6996-5068

Funding

XenograftP30CA046592 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Eric R. Fearon · 1988 to 2026
$178.2M
National Cancer Institutes of Health P30CA046592NCI NIH HHS P30 CA046592
6 · The paper itself

Abstract

backgroundYoung 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.

objectiveThis qualitative descriptive study explored young adult cancer survivors' perspectives, preferences, and experiences related to the use of generative AI chatbots for oncology symptom management.

methodsEighteen 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.

resultsQualitative 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.

conclusionGenerative 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.

Indexed as

Cancer SurvivorsNeoplasmsAdolescentAdultFemaleGenerative Artificial IntelligenceHumansLarge Language ModelsMaleQualitative ResearchQuality of LifeSelf EfficacySymptom BurdenYoung AdultCancer treatment-related symptomsGenerative artificial intelligenceQualitative researchYoung adult

Identifiers

PMID42773243
PMCPMC13597565

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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.