ArticleJMIR mHealth and uHealth2026
A Community-Based Usability Study of an AI-Enabled Oral Cancer Screening App Operated by Village Health Volunteers: Mixed Methods Study.
Article in JMIR mHealth and uHealth, 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: Oral cancer is a major public health concern in low- and middle-income countries, where access to specialist care and early detection remains limited. Mobile health technologies supported by artificial intelligence (AI) offer a scalable approach to extend screening services into underserved communities. In Thailand, village health volunteers (VHVs) are key frontline workers who provide preventive services and bridge gaps between rural populations and specialist care. Objective: This study aimed to describe the technical development of RiskOCA (Risk Assessment for Oral Cancer using Artificial Intelligence), a smartphone-based, AI-assisted oral cancer screening platform, and evaluate its usability when deployed by VHVs in a rural Thai province. Methods: RiskOCA was developed using a 3-tier architecture comprising a patient-facing interface for risk factor profiling and guided imaging, an embedded deep learning engine (DeepLab v3+with ResNet-50 backbone) for lesion detection, classification, and segmentation, and a secure specialist portal for expert review of all cases. The AI model was trained on 2226 annotated intraoral images and validated for real-world use. Field testing was conducted in the Phu Kamyao district, Phayao province, where 1242 adults (≥40 y) were screened with assistance from VHVs. Usability was evaluated through a structured 25-item questionnaire completed by 250 VHVs, with responses rated on a 5-point Likert scale. Results: The AI model achieved a mean classification accuracy of 93.22% (SD 0.88%) across 3 diagnostic categories. Usability evaluation indicated high satisfaction across all domains, with an overall mean score of 4.17 out of 5. The highest ratings were for the app's impact on older adult surveillance (mean 4.30), while all domains were rated "satisfied" or "very satisfied." Conclusions: RiskOCA demonstrated strong technical performance and high user acceptance among VHVs, supporting its feasibility for community-based oral cancer screening. By integrating AI-assisted triage with expert review, the platform has the potential to reduce diagnostic delays, expand screening coverage, and serve as a scalable model for oral cancer prevention in resource-limited settings.
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