ABSTRACT: Oral squamous cell carcinoma (OSCC) remains a major public health challenge, particularly in low- and middle-income countries (LMICs), where early detection infrastructure is limited. Vital staining is affordable and chairside-simple but is undermined by operator variability and modest specificity. Artificial intelligence (AI)-assisted image analysis offers reproducibility and scale but is constrained by technological and logistical barriers in resource-poor settings. Our position in this Opinion is straightforward: neither modality alone will close the diagnostic gap in rural India, and withholding a low-cost triage layer from village populations while urban centers benefit from AI-assisted histopathology is inequity by omission. We argue for a hybrid workflow in which frontline workers apply a standardized dye, capture the stained lesion on a smartphone, and route the image through a cloud-hosted deep-learning classifier to a specialist hub. We defend this position with a pragmatic implementation pathway and a four-step, milestone-driven research agenda covering image repositories, prospective accuracy trials, cost-and-equity metrics, and regulatory-sandbox validation.

Impact statement: This Opinion proposes a pragmatic hybrid approach combining vital staining and artificial intelligence to strengthen early oral cancer triage, enhance diagnostic access, and decrease inequities in resource-limited settings.

Key words: Artificial intelligence; early diagnosis; hybrid diagnostic model; oral squamous cell carcinoma; oral potentially malignant disorders; vital staining.