Abstract Background: Cancer patients frequently experience persistent symptoms such as pain, fatigue, and treatment-related distress. Despite guideline-based recommendations, real-time symptom monitoring and supportive care delivery remain suboptimal. We developed an AI-based chatbot application designed to provide personalized symptom management education, self-management guidance, and interactive support. This study evaluates user experience, self-efficacy, psychological distress, and early clinical impact. Methods: A prospective digital-health intervention study was conducted using an AI chatbot delivering education aligned with NCCN supportive-care guidance. A total of 192 participants were enrolled, including both solid tumor and hematologic malignancy patients. Validated instruments included UMUX-Lite for usability, SEMCD-6 for self-management self-efficacy, DT for psychological distress, perceived usefulness (PU), engagement frequency, and e-health literacy measures (instrument details in slide deck). Outcomes were assessed at baseline and follow-up. Group × time effects were analyzed using mixed-effects models. Results: User experience remained consistently high throughout the study period, with favorable UMUX and PU scores. Although short-term and mid-term objective improvements were modest, the intervention group demonstrated significantly greater improvements in key patient-reported outcomes, including:- Reduced psychological distress (P = 0.013)- Improved self-efficacy domains (multiple SEMCD items showing significant change; P = 0.008 in primary domains) Higher e-health literacy was associated with larger improvements in self-efficacy and perceived usefulness. Subgroup analyses suggested heterogeneous treatment effects, with males, lung-cancer patients, and individuals with lower educational attainment showing greater unmet needs and requiring tailored support. Engagement (days of chatbot use per week) showed a positive dose-response trend with outcome improvement. Conclusions: The AI chatbot application demonstrated high sustained user satisfaction and early signals of benefit in psychological distress reduction and self-management confidence among cancer patients. While objective clinical markers showed limited short-term changes, differential effects across demographic and diagnostic subgroups highlight the need for targeted personalization. Ongoing analyses will evaluate mediators such as digital literacy, therapeutic alliance, intervention usability, and long-term clinical outcomes. Citation Format: Hyun Woo Lee, Tae Jun Park, Seok Yun Kang, Jang Hee Kim. Early outcomes of an AI-driven chatbot application for symptom management in patients with cancer: Interim analysis of a prospective digital health study abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 1241.
Lee et al. (Fri,) studied this question.