Classification of Oral Cancer Through Artificial Intelligence-Based Histopathological Image Analysis: A Comprehensive Review of Diagnostic Methods and Clinical Challenges
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Background: Artificial intelligence (AI) is increasingly applied to oral squamous cell carcinoma (OSCC) detection and oral epithelial dysplasia (OED) grading in digital histopathology. Objective: To characterize diagnostic tasks, model families, reported performance, and evidence relevant to clinical generalizability. Methods: We developed a structured, task-stratified evidence map from 39 topic-specific bibliographic sources, supplemented by two methodological guidance references and three cross-modal methodological examples. Sources were classified by evidence role, and published performance estimates were interpreted according to endpoint and validation setting. Comprehensive multi-database retrieval records, independent eligibility screening, and completed study-level risk-of-bias assessments were not available; the synthesis is therefore provisional. Results: The topical inventory comprised 27 candidate classification or grading studies, three adjacent original investigations, five secondary reviews, two dataset sources, and two clinical or pathology background publications. Several classifiers reported high internal accuracy, but differences in case mix, diagnostic labels, prediction units, and independent testing precluded a valid cross-study ranking. No meta-analysis was undertaken because comparable patient-level diagnostic contingency data were not consistently available. Conclusion: The principal barrier to clinical translation is not model discrimination alone but reproducible patient-independent evaluation, dependable reference diagnoses, external validation, and demonstrated benefit in clinical workflows. The review defines evidence requirements for more reliable future investigations.
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