Title

AI-based diagnostic tools for oral cancer: A systematic review

Authors

Sindhoori Goud¹, Anuj Singh Parihar^2,*, Anuj Mishra³, Tanu Sahney⁴, Sujit Anil Vyavahare⁵, Madhura Govind Titar⁶ & Julee Mandal⁷

Affiliation

¹Department of Conservative Dentistry and Endodontics, Mallareddy Institute of Dental Science, Mallareddy Viswavidyapet, Hyderabad, Telangana, India; ²Department of Periodontology, People's Dental Academy, Bhopal, Madhya Pradesh, India; ³Department of Oral Medicine and Radiology, Sardar Patel Post Graduate Institute of Dental and Medical Sciences, Lucknow, Uttar Pradesh, India; ⁴Department of Periodontology, Sardar Patel Post Graduate Institute of Dental and Medical Sciences, Lucknow, Uttar Pradesh, India; ⁵Department of Oral and Maxillofacial Surgery, MGM Dental College and Hospital, Kamothe, Navi Mumbai, Maharashtra, India; ⁶Department of Prosthodontics and Crown and Bridge, MGM Dental College and Hospital, Kamothe, Navi Mumbai, Maharashtra, India; ⁷Department of Dentistry, Kalinga Institute of Dental Sciences, Kalinga Institute of Industrial Technology (KIIT) Deemed to be University, Bhubaneswar, Odisha, India; *Corresponding author

Email

Sindhoori Goud - E-mail: dr.sindhoori.mempally@gmail.com
Anuj Singh Parihar - E-mail: Dr.anujparihar@gmail.com
Anuj Mishra - E-mail: diagnosticcentre89@gmail.com
Tanu Sahney - E-mail: Tanu.sahney910@gmail.com
Sujit Anil Vyavahare - E-mail: sujitvyavahare@gmail.com
Madhura Govind Titar - E-mail: madhuravyavahare05@gmail.com
Julee Mandal - E-mail: juleemandal230828@gmail.com

Article Type

Review

Date

Received September 1, 2025; Revised September 30, 2025; Accepted September 30, 2025, Published September 30, 2025

Abstract

Artificial intelligence (AI) as a means of enhancing and improve the early detection and diagnosis of oral cancer. While traditional diagnostic methods are effective, they are inherently subjective, not widely accessible, and often result in detection only at later stages of disease progression. The literature over the past few years has importantly identified that AI models, in particular deep learning and convolutional neural networks, displayed high diagnostic accuracy on clinical, histopathological and optical imaging data. However, challenges exist in the form of variability in the data sets, limited external validation in clinical practice and or interpretability of AI models for clinical use. In conclusion, AI presents a compelling opportunity as a supportive adjunct for oral oncology, although standardized validation and real-world implementation should occur before widespread utilization.

Keywords

Artificial intelligence, oral cancer, deep learning, diagnostic accuracy, convolutional neural networks, image analysis

Citation

Goud et al. Bioinformation 21(9): 3324-3328 (2025)

Edited by

A Prashanth

ISSN

0973-2063

Publisher

Biomedical Informatics

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.