Application of Artificial Intelligence in the Diagnosis of Oral Squamous Cell Carcinoma: A Synthesis of Systematic Reviews
Uma síntese de revisões sistemáticas
DOI:
https://doi.org/10.36557/2674-8169.2026v8n3p330-351Keywords:
Câncer de Boca, Carcinoma. Patologia Bucal. Estomatologia. Neoplasias de Tecidos Moles., Inteligência Artificial.Abstract
Introduction: Oral squamous cell carcinoma (OSCC), also known as squamous cell carcinoma, accounts for approximately 90% of all malignant neoplasms of the oral cavity. According to the World Cancer Research Fund, in 2022, around 389,846 new cases of oral cancer were reported worldwide, with a higher incidence among men. Despite the global trend of increasing incidence, underreporting remains a major issue, particularly in developing countries, which may directly affect patient prognosis and reduce survival rates.
Objective: To evaluate and synthesize systematic reviews that analyzed the performance and accuracy of different artificial intelligence (AI) applications in the diagnosis of oral squamous cell carcinoma. Materials and Methods: The databases PUBMED/MEDLINE, Web of Science, LILACS (BVS), and SciELO were searched, as well as “grey literature” sources through Google Scholar. The following descriptors were used: oral cancer, oral squamous cell carcinoma, AI, artificial intelligence, deep learning, and machine learning, combined with the Boolean operators “AND” and “OR”. Inclusion criteria comprised systematic reviews with robust and detailed data, published between 2020 and 2025, addressing the use of artificial intelligence as a diagnostic tool for oral squamous cell carcinoma. Only studies written in English were included. Results: After identification, screening, and application of eligibility criteria, 11 systematic reviews were selected for inclusion in this study. Conclusion: Artificial intelligence models demonstrated promising performance in the diagnosis of oral squamous cell carcinoma. However, further studies are required to standardize methodological protocols, expand and diversify datasets, and clinically validate the AI models applied
Downloads
References
Dong L, Xue L, Cheng W, Tang J, Ran J, Li Y. Comprehensive survival analysis of oral squamous cell carcinoma patients undergoing initial radical surgery. BMC Oral Health [Internet]. 2024 Dec 1 [cited 2025 May 12];24(1). Available from: https://pubmed.ncbi.nlm.nih.gov/39123139/
Oliveira LL, Bergmann A, Melo AC, Thuler LCS. Prognostic factors associated with overall survival in patients with oral cavity squamous cell carcinoma. Med Oral Patol Oral Cir Bucal [Internet]. 2020 Jul 1 [cited 2025 May 12];25(4):e523. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC7338068/
Farias Sousa L, Silva VB, Rezende D, Sarri A, Almeida I, Lima B. Aspectos clínicos do carcinoma epidermóide oral: uma revisão integrativa da literatura. Brazilian Journal of Health Review [Internet]. 2023 Jun 4 [cited 2025 May 12];6(3):11710–26. Available from: https://ojs.brazilianjournals.com.br/ojs/index.php/BJHR/article/view/60417
Zou Z, Li B, Wen S, Lin D, Hu Q, Wang Z, et al. The Current Landscape of Oral Squamous Cell Carcinoma: A Comprehensive Analysis from ClinicalTrials.gov. Cancer Control [Internet]. 2022 Mar 8 [cited 2025 May 12];29:10732748221080348. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC8968992/
Hirsch JM, Sandy R, Hasséus B, Lindblad J. A paradigm shift in the prevention and diagnosis of oral squamous cell carcinoma. Journal of Oral Pathology and Medicine [Internet]. 2023 Oct 1 [cited 2025 May 1];52(9):826–33. Available from: /doi/pdf/10.1111/jop.13484
de Chauveron J, Unger M, Lescaille G, Wendling L, Kurtz C, Rochefort J. Artificial intelligence for oral squamous cell carcinoma detection based on oral photographs: A comprehensive literature review. Vol. 13, Cancer Medicine. John Wiley and Sons Inc; 2024.
Swanson K, Wu E, Zhang A, Alizadeh AA, Zou J. From patterns to patients: Advances in clinical machine learning for cancer diagnosis, prognosis, and treatment. Cell [Internet]. 2023 Apr 13 [cited 2025 May 12];186(8):1772–91. Available from: https://pubmed.ncbi.nlm.nih.gov/36905928/
Vinay V, Jodalli P, Chavan MS, Buddhikot CS, Luke AM, Ingafou MSH, et al. Artificial Intelligence in Oral Cancer: A Comprehensive Scoping Review of Diagnostic and Prognostic Applications. Diagnostics [Internet]. 2025 Feb 1 [cited 2025 May 12];15(3):280. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC11816433/
Sajithkumar A, Thomas J, Saji AM, Ali F, Haneena HH, Adampulan HAG, et al. Artificial Intelligence in pathology: current applications, limitations, and future directions. Ir J Med Sci [Internet]. 2024 Apr 1 [cited 2025 May 12];193(2):1117–21. Available from: https://pubmed.ncbi.nlm.nih.gov/37542634/
Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, et al. Artificial intelligence in healthcare: Past, present and future. Stroke Vasc Neurol [Internet]. 2017 Dec 1 [cited 2025 May 12];2(4):230–43. Available from: https://pubmed.ncbi.nlm.nih.gov/29507784/
Pirayesh Z, Mohammad-Rahimi H, Ghasemi N, Motamedian SR, Sadeghi TS, Koohi H, et al. Deep Learning-Based Image Classification and Segmentation on Digital Histopathology for Oral Squamous Cell Carcinoma: A Systematic Review and Meta-Analysis. Journal of Oral Pathology and Medicine [Internet]. 2024 Oct 1 [cited 2025 May 11];53(9). Available from: https://pubmed.ncbi.nlm.nih.gov/39256895/
Beristain-Colorado MDP, Castro-Gutiérrez MEM, Torres-Rosas R, Vargas-Treviño M, Moreno-Rodríguez A, Fuentes-Mascorro G, et al. Application of neural networks for the detection of oral cancer: A systematic review. Dent Med Probl [Internet]. 2024 Jan 1 [cited 2025 May 6];61(1):121–8. Available from: https://pubmed.ncbi.nlm.nih.gov/37098828/
Warin K, Suebnukarn S. Deep learning in oral cancer- a systematic review. BMC Oral Health. 2024 Dec 1;24(1).
Malhotra M, Shaw AK, Priyadarshini SR, Metha SS, Sahoo PK, Gachake A. Diagnostic Accuracy of Artificial Intellige
nce Compared to Biopsy in Detecting Early Oral Squamous Cell Carcinoma: A Systematic Review and Meta Analysis. Asian Pacific Journal of Cancer Prevention [Internet]. 2024 [cited 2025 May 2];25(8):2593–603. Available from: https://pubmed.ncbi.nlm.nih.gov/39205556/
Khanagar S, Alkadi L, Alghilan M, Biomedicines SK, 2023 undefined. Application and performance of artificial intelligence (AI) in oral cancer diagnosis and prediction using histopathological images: a systematic review. mdpi.com [Internet]. 2021 [cited 2025 Apr 29]; Available from: https://www.mdpi.com/2227-9059/11/6/1612
Ferreira JC, Patino CM. Understanding diagnostic tests. Part 1. Jornal Brasileiro de Pneumologia [Internet]. 2017 Sep 1 [cited 2025 May 18];43(5):330–330. Available from: https://www.scielo.br/j/jbpneu/a/rHy8WhCg5cWVWypdf4phDXj/?lang
Janiesch C, Zschech P, Heinrich K. Machine learning and deep learning. Electronic Markets [Internet]. 2021 Sep 1 [cited 2025 May 18];31(3):685–95. Available from: https://link.springer.com/article/10.1007/s12525-021-00475-2
Lecun Y, Bengio Y, Hinton G. Deep learning. Nature [Internet]. 2015 May 27 [cited 2025 May 12];521(7553):436–44. Available from: https://www.nature.com/articles/nature14539
Filho RBL, Carneiro MG. Diagnóstico do câncer oral através da classificação de alto nível. Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS) [Internet]. 2023 Jun 27 [cited 2025 May 20];54–9. Available from: https://sol.sbc.org.br/index.php/sbcas_estendido/article/view/25331
Jerjes W, Stevenson H, Ramsay D, Hamdoon Z. Enhancing Oral Cancer Detection: A Systematic Review of the Diagnostic Accuracy and Future Integration of Optical Coherence Tomography with Artificial Intelligence. J Clin Med [Internet]. 2024 Oct 1 [cited 2025 May 2];13(19). Available from: https://pubmed.ncbi.nlm.nih.gov/39407882/
Sahoo RK, Sahoo KC, Dash GC, Kumar G, Baliarsingh SK, Panda B, et al. Diagnostic performance of artificial intelligence in detecting oral potentially malignant disorders and oral cancer using medical diagnostic imaging: a systematic review and meta-analysis. Frontiers in Oral Health [Internet]. 2024 [cited 2025 May 2];5. Available from: https://pubmed.ncbi.nlm.nih.gov/39568787/
Elmakaty I, Elmarasi M, Amarah A, … RAC reviews in, 2022 undefined. Accuracy of artificial intelligence-assisted detection of oral squamous cell carcinoma: a systematic review and meta-analysis. Elsevier [Internet]. 2022 [cited 2025 Apr 29]; Available from: https://www.sciencedirect.com/science/article/pii/S1040842822002013
Veeraraghavan V, Minervini G, … DRJ of, 2024 undefined. Assessing Artificial Intelligence in Oral Cancer Diagnosis: A Systematic Review. journals.lww.com [Internet]. 2024 [cited 2025 Apr 29]; Available from: https://journals.lww.com/jcraniofacialsurgery/fulltext/2024/11000/assessing_artificial_intelligence_in_oral_cancer.47.aspx?context=latestarticles
Kim JS, Kim BG, Hwang SH. Efficacy of Artificial Intelligence-Assisted Discrimination of Oral Cancerous Lesions from Normal Mucosa Based on the Oral Mucosal Image: A Systematic Review and Meta-Analysis. Cancers (Basel) [Internet]. 2022 Jul 1 [cited 2025 May 2];14(14). Available from: https://pubmed.ncbi.nlm.nih.gov/35884560/
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Nycole Susi Ferreira de Araújo, Laís Inês Silva Cardoso, Thalita Santana
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors are copyright holders under a CCBY 4.0 license.
