Accepted for/Published in: JMIR Cancer
Date Submitted: Nov 9, 2024
Open Peer Review Period: Nov 25, 2024 - Jan 20, 2025
Date Accepted: May 5, 2025
Date Submitted to PubMed: May 6, 2025
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Leveraging Artificial Intelligence for Digital Symptom Management in Oncology: The Development and Evaluation of CRCWeb
ABSTRACT
Background:
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.
Objective:
This study aimed to address health disparities by improving the accessibility of educational resources on symptom management for disadvantaged CRC patients and their caregivers.
Methods:
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.
Results:
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).
Conclusions:
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. Clinical Trial: ClinicalTrials.gov NCT05663203.
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.