AI in early oral cancer detection: a systematic review of technologies and clinical impact

Luigi de Almeida Albertoni; Letícia dos Santos Sales Martins; Cecília Helpes Rodrigues; Lara Castor Cunha; Henrique Souza Magalhães; Bruno Alves Ferreira; Lívia Garcia Resende; Quézia Soares de Paula; Maria Clara Fernandes Ribeiro Dantas; Gisele Maria Campos Fabri

doi:10.4322/bds.2026.e5017

Authors

Luigi de Almeida Albertoni Universidade Federal de Juiz de Fora, Faculdade de Odontologia, Departamento de Odontopediatria e Ortodontia. Juiz de Fora, MG, Brazil https://orcid.org/0009-0009-4006-490X
Letícia dos Santos Sales Martins Universidade Federal de Juiz de Fora, Faculdade de Odontologia, Departamento de Odontopediatria e Ortodontia. Juiz de Fora, MG, Brazil https://orcid.org/0009-0000-3896-3599
Cecília Helpes Rodrigues Universidade Federal de Juiz de Fora, Faculdade de Odontologia, Departamento de Odontopediatria e Ortodontia. Juiz de Fora, MG, Brazil https://orcid.org/0009-0009-0085-5408
Lara Castor Cunha Universidade Federal de Juiz de Fora, Faculdade de Odontologia, Departamento de Odontopediatria e Ortodontia. Juiz de Fora, MG, Brazil. https://orcid.org/0009-0007-2498-8972
Henrique Souza Magalhães Universidade Federal de Juiz de Fora, Faculdade de Odontologia, Departamento de Odontopediatria e Ortodontia. Juiz de Fora, MG, Brazil. https://orcid.org/0009-0000-9499-9529
Bruno Alves Ferreira Universidade Federal de Juiz de Fora, Faculdade de Odontologia, Departamento de Odontopediatria e Ortodontia. Juiz de Fora, MG, Brazil. https://orcid.org/0009-0008-8771-6349
Lívia Garcia Resende Universidade Federal de Juiz de Fora, Faculdade de Odontologia, Departamento de Odontopediatria e Ortodontia. Juiz de Fora, MG, Brazil https://orcid.org/0009-0003-5442-9419
Quézia Soares de Paula Universidade Federal de Juiz de Fora, Faculdade de Odontologia, Departamento de Odontopediatria e Ortodontia. Juiz de Fora, MG, Brazil. https://orcid.org/0009-0004-4546-4316
Maria Clara Fernandes Ribeiro Dantas Universidade Federal de Juiz de Fora, Faculdade de Odontologia, Departamento de Odontopediatria e Ortodontia. Juiz de Fora, MG, Brazil. https://orcid.org/0009-0009-3907-5046
Gisele Maria Campos Fabri Universidade Federal de Juiz de Fora, Faculdade de Odontologia, Departamento de Odontopediatria e Ortodontia. Juiz de Fora, MG, Brazil. https://orcid.org/0000-0002-8396-0722

DOI:

https://doi.org/10.4322/bds.2026.e5017

Abstract

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.