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REVIEW article
Front. Oral. Health
Sec. Oral Cancers
Volume 6 - 2025 | doi: 10.3389/froh.2025.1569567
This article is part of the Research Topic Advances and Innovative Discoveries in Oral Potentially Malignant Disorders View all 3 articles
Artificial Intelligence and the Diagnosis of Oral Cavity Cancer and Oral Potentially Malignant Disorders from Clinical Photographs: A Narrative Review
Provisionally accepted- 1 Department of Oral & Maxillofacial Pathology, Radiology and Medicine, College of Dentistry, New York University, New York City, New York, United States
- 2 Section of Hematology/Oncology, University of Chicago Medicine, Chicago, Illinois, United States
Oral cavity cancer is associated with high morbidity and mortality, particularly with advanced stage diagnosis. Oral cavity cancer, typically squamous cell carcinoma (OSCC), is often preceded by oral potentially malignant disorders (OPMDs), which comprise eleven disorders with variable risks for malignant transformation. While OPMDs are clinical diagnoses, conventional oral exam followed by biopsy and histopathological analysis is the gold standard for diagnosis of OSCC. There is vast heterogeneity in the clinical presentation of OPMDs, with possible visual similarities to early-stage OSCC or even to various benign oral mucosal abnormalities. The diagnostic challenge of OSCC/OPMDs is compounded in the non-specialist or primary care setting. There has been significant research interest in technology to assist in the diagnosis of OSCC/OPMDs. Artificial intelligence (AI), which enables machine performance of human tasks, has already shown promise in several domains of medical diagnostics. Computer vision, the field of AI dedicated to the analysis of visual data, has over the past decade been applied to clinical photographs for the diagnosis of OSCC/OPMDs. Various methodological concerns and limitations may be encountered in the literature on OSCC/OPMD image analysis. This narrative review delineates the current landscape of AI clinical photograph analysis in the diagnosis of OSCC/OPMDs and navigates the limitations, methodological issues, and clinical workflow implications of this field, providing context for future research considerations.
Keywords: Oral cavity cancer, oral squamous cell carcinoma, Oral potentially malignant disorders, artificial intelligence, deep learning, Computer Vision, image analysis, Clinical photographs
Received: 01 Feb 2025; Accepted: 25 Feb 2025.
Copyright: © 2025 Mirfendereski, Li, Pearson and Kerr. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Payam Mirfendereski, Department of Oral & Maxillofacial Pathology, Radiology and Medicine, College of Dentistry, New York University, New York City, 10010, New York, United States
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.