ArticleFrontiers in public health2026
Preparedness for generative AI adoption among Chinese cancer survivors: a multi-center cross-sectional survey study.
Article in Frontiers in public health, 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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Abstract
Background: Generative artificial intelligence (GenAI) is rapidly entering consumer health information environments, yet patient readiness for safe adoption in cancer survivorship remains unclear. This study assessed preparedness for GenAI adoption among Chinese cancer survivors and identified correlates relevant to equitable implementation. Methods: We conducted a multi-center, cross-sectional online survey among digitally reachable adult cancer survivors recruited via clinical encounters, WeChat patient groups, and peer referral across three oncology centers in Sichuan, China. The questionnaire was administered on Wenjuanxing. The primary outcome was a theory-informed, study-specific 0-100 GenAI adoption preparedness composite derived from five Likert items: perceived usefulness, perceived ease of use, access to guidance/support, privacy concern after reverse coding, and near-term intention. Secondary outcomes included GenAI awareness and prior use, willingness for report explanation and symptom advice scenarios, and health information ability. Multivariable linear regression with robust standard errors estimated adjusted associations with preparedness, with sensitivity analyses addressing data quality flags and recruitment pathway. Results: From 1,062 survey visits, 876 participants comprised the analytic sample. Mean preparedness was 57.8 (SD 24.2) with acceptable internal consistency (Cronbach's alpha 0.75). Awareness of GenAI was 61.6 and 40.6% reported prior use. Near-term intention to try GenAI for survivorship information tasks was endorsed by 51.3%. Willingness was higher for test report explanation (56.1% agree/strongly agree) than for symptom advice with referral prompts (40.3%). Preparedness was lower among participants older than 60 years versus 18-45 years (beta -8.1, 95% CI -12.0 to -4.3) and higher with prior generative AI use (beta 9.3, 95% CI 5.7-12.9), higher self-rated generative AI knowledge (beta 5.4 per 1-point, 95% CI 3.7-7.0), and greater health information ability (beta 2.0 per 10 points, 95% CI 1.2-2.7). The model explained 36% of variance (R Conclusion: Among digitally reachable cancer survivors in Sichuan, preparedness for GenAI adoption was moderate and strongly use-case dependent, with lower readiness among older survivors. The online-only sampling strategy means that the observed preparedness level may overestimate readiness in the broader survivorship population. Implementation should begin with lower-risk applications, such as report explanation and question preparation, paired with guidance on verification, privacy protection, and clear escalation to clinicians.
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