Leveraging Artificial Intelligence for Digital Symptom Management in Oncology: The Development and Evaluation of CRCWeb (Preprint)

Abstract

<sec> <title>BACKGROUND</title> 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. </sec> <sec> <title>OBJECTIVE</title> This study aimed to address health disparities by improving the accessibility of educational resources on symptom management for disadvantaged CRC patients and their caregivers. </sec> <sec> <title>METHODS</title> To address these health disparities, we proposed a generative AI (GenAI)-powered system to customize CRC educational materials into accessible multimedia content, delivered by a mobile platform, CRCWeb. We conducted an 8-week single-arm prospective study with 40 participants (20 patients and 20 caregivers) with both disadvantaged and non-disadvantaged backgrounds. </sec> <sec> <title>RESULTS</title> Analysis of user login activity showed that the login frequency of the disadvantaged group is 2.52 times higher than that of the non-disadvantaged group. Post-intervention ratings from both groups demonstrated significant equivalence (P ≤ .001) in overall satisfaction with CRCWeb. The pre- and post-intervention symptom data showed reductions in symptom scores for both the disadvantaged group (a difference of 0.108, P = .123) and the non-disadvantaged group (a difference of 0.211, P = .002). </sec> <sec> <title>CONCLUSIONS</title> Our findings highlight the potential of GenAI-powered digital solutions to address gaps in healthcare access, offering support that promotes cancer care and health equity for disadvantaged populations. </sec> <sec> <title>CLINICALTRIAL</title> ClinicalTrials.gov NCT05663203. </sec> <sec> <title>INTERNATIONAL REGISTERED REPORT</title> RR2-10.2196/48499 </sec>

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