Leveraging Artificial Intelligence for Digital Symptom Management in Oncology: The Development of CRCWeb

作者：Darren Liu, Yu‐Fen Lin, Runze Yan, Zhiyuan Wang, Delgersuren Bold, Xiao Hu · 发表于：JMIR Cancer · 年份：2025 · DOI：10.2196/68516 · 被引用次数：6 · 研究领域：Artificial Intelligence in Healthcare and Education、Radiomics and Machine Learning in Medical Imaging

UNSTRUCTURED: Digital health interventions offer promise for scalable and accessible healthcare, but access is still limited by some participatory challenges, especially for disadvantaged families facing limited health literacy, language barriers, low income, or living in marginalized areas. These issues are particularly pronounced for colorectal cancer (CRC) patients, who often experience distressing symptoms and struggle with educational materials due to complex jargon, fatigue, or reading level mismatches. To address these issues, we developed and assessed the feasibility of a digital health platform, CRCWeb, to improve the accessibility of educational resources on symptom management for disadvantaged CRC patients and their caregivers facing limited health literacy or low income. CRCWeb was developed through a stakeholder-centered participatory design approach. Two-phase semi-structured interviews with patients, caregivers, and oncology experts informed the iterative design process. From the interviews, we developed the following five key design principles: user-friendly navigation, multimedia integration, concise and clear content, enhanced accessibility for individuals with vision and reading disabilities, and scalability for future content expansion. Initial feedback from iterative stakeholder engagements confirmed high user satisfaction, with participants rating CRCWeb an average of 3.98 out of 5 on the post-intervention survey. Additionally, using GenAI tools, includi...