AI in early oral cancer detection: a systematic review of technologies and clinical impact - Albertoni, Luigi de Almeida; Martins, Letícia dos Santos Sales; Rodrigues, Cecília Helpes; Cunha, Lara Castor; Magalhães, Henrique Souza; Ferreira, Bruno Alves; Resende, Lívia Garcia; Paula, Quézia Soares de; Dantas, Maria Clara Fernandes Ribeiro; Fabri, Gisele Maria Campos (Brazilian Dental Science, 2026)
Objective: To evaluate the effectiveness and clinical applicability of artificial intelligence (AI) in the early diagnosis of oral cancer compared to conventional methods. Material and Methods: This systematic review was prospectively registered in the PROSPERO database under the number CRD420251083609. A descriptive systematic review was conducted based on the PICO question: “In patients with suspected oral cancer, is AI more effective than traditional methods for early diagnosis?” Searches were performed between April and June 2025 in PubMed, LILACS, SciELO, Scopus, Web of Science, Embase, and Google Scholar using the keywords “artificial intelligence,” “oral neoplasm,” and “early diagnosis.” Comparative studies reporting diagnostic metrics such as sensitivity, specificity, and accuracy were included. Methodological quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies – Artificial Intelligence extension (QUADAS-AI) and the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) system. Results: Of 1,704 identified studies, 12 met eligibility criteria. Most studies employed retrospective observational designs, primarily using convolutional neural networks (CNNs) or hybrid models on clinical and histopathological images. Reported diagnostic accuracy was generally above 80%, and some lightweight models demonstrated potential for remote screening. Common limitations included lack of external validation, methodological heterogeneity, and dependence on image quality. Conclusion: AI shows promising potential to support early diagnosis of oral cancer, improving diagnostic speed and accuracy. Broader clinical implementation will require multicenter validation, standardized datasets, and integration with clinical and histopathological evaluation. KEYWORDS Artificial Intelligence; Computer-aided diagnosis; Early diagnosis; Mouth neoplasms; Squamous Cell Carcinoma of Head and Neck.