Screening tools and strategies for early detection of oral cancer and potentially malignant disorders in rural and low-resource populations: A systematic review
Bibliographic record
Abstract
Oral cancer and its precursor lesions, known as oral potentially malignant disorders (OPMDs), present significant public health challenges due to high morbidity and mortality rates. These issues are particularly pronounced in rural and remote populations, where access to specialized care and early diagnostic services is limited. Socioeconomic disparities, healthcare provider shortages, and geographic isolation contribute to delayed diagnoses and poorer outcomes in these settings. This systematic review evaluated 40 studies focused on early detection strategies for OPMDs and oral squamous cell carcinoma (OSCC) in underserved populations, particularly in rural and low-resource environments. Across 136,257 participants, the review identified diverse screening tools including oral visual examinations, toluidine blue staining, autofluorescence, chemiluminescence, mobile health (mHealth) applications, and artificial intelligence (AI)-based image analysis. The use of low-cost, portable, and technology-driven tools, especially those enabling remote diagnosis and task-sharing with frontline health workers, showed promise in increasing diagnostic accuracy and screening reach. However, variability in performance, training needs, and follow-up adherence remain key limitations. The review also highlighted widespread use of community-based strategies and the value of interdisciplinary collaboration, particularly where dental specialists are not readily available. Innovative, context-sensitive screening tools supported by interdisciplinary networks hold strong potential to enhance early detection of OPMDs and OSCC in rural and underserved communities. Mobile technologies and AI applications are emerging as practical adjuncts to traditional screening methods. To optimize impact, future efforts should prioritize validation of these tools against histopathological standards and the integration of trained non-specialist health workers in community-based screening programs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.012 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".