Mobile AI-Assisted Oral Lesion Triage for Community Oral Cancer Screening: Prospective Field Evaluation Study.

Published in JMIR mHealth and uHealth. Abstract: AI-based tools for oral cancer screening have shown promising performance in curated image datasets, but their performance in real-world mobile community screening workflows remains less clear. This study evaluated the prospective field performance of a parameter-locked, mobile AI-assisted oral lesion triage system embedded in a human-supervised community oral cancer screening workflow. We conducted a prospective community-based field evaluation in eastern Taiwan from June 17 to December 31, 2025. Trained nonspecialist personnel acquired standard white-light intraoral images using handheld mobile devices during routine screening. The AI system incorporated on-site image quality assessment and lesion-level risk stratification into green, yellow, and red triage categories. Technically inadequate images after repeated acquisition attempts were classified as ungradable and excluded from performance analysis. Three board-certified oral and maxillofacial specialists established an operational clinical reference standard through structured image review and consensus adjudication. The primary outcome was lesion-level identification of high-risk lesions requiring specialist referral, defined as red versus nonred triage. Diagnostic performance was estimated with Wilson 95% CIs; precision-recall performance and preconsensus specialist agreement were also assessed. Among 602 screened participants, 4283 interpretable intraoral images were included in expert adjudication and lesion-level a...