Oral cancer remains a leading cause of cancer-related deaths in India and worldwide, as late-stage diagnosis raises the complexity of treatment and lowers survival rates. Often subjective, traditional screening methods—usually visual inspection and biopsy—can miss early lesions, especially in busy clinical environments with limited time and expertise.
Integrating artificial intelligence (AI) into regular dental care is ushering in a new era of early and accurate diagnosis.
Finding oral cancer presents several challenges. While early diagnosis allows for excellent prevention and care, late diagnosis is still common, especially in resource-poor settings. The consequences are serious: advanced-stage oral cancer is associated with higher mortality, increased treatment burden, and reduced quality of life. The need for accurate, scalable, and effective screening methods has never been more urgent[1].
Every year, thousands of lives are lost to oral cancer—not because we lack treatments, but because we detect it too late. Now, artificial intelligence is stepping in where traditional methods fall short.
Early detection driven by artificial intelligence employs deep learning and machine learning techniques to assess vast numbers of oral image data, including histopathological slides and clinical photographs.
Convolutional Neural Networks (CNNs), a type of deep learning model especially good at analyzing images, have demonstrated incredible precision; studies have shown sensitivity and specificity rates sometimes exceeding 85% [2].
For instance, a recent meta-analysis combining data from 18 studies found that artificial intelligence models achieved a combined sensitivity of 87% and specificity of 81%, with certain architectures—especially those analyzing histopathological images—achieving sensitivity and specificity over 95% [2].
Mobile and cloud-based artificial intelligence solutions that enable point-of-care screening using cell phone cameras are being developed. These tools, which may be used in remote locations and community health camps, significantly enhance early detection accessibility[3].
In one study, models like DenseNet201 and FixCaps achieved over 82% accuracy (F1 score) and 0.97 AUC (Area Under the ROC Curve)—indicating excellent diagnostic precision [4].
In India, several pioneering projects are bringing AI-powered early detection to the forefront of public health:
MNJ Cancer Centre and Government of Telangana Project: Under this project, artificial intelligence algorithms and high-resolution imaging help detect early signs of oral, breast, and cervical cancers. Experts review results to ensure that flagged cases receive prompt attention. Currently operating in three regions, the pilot initiative aims to offer disadvantaged groups early detection of oral cancer using mobile-based, AI-assisted white light imaging systems[5].
Biocon Foundation and Indian Institute of Science Project: The Biocon Foundation, in partnership with the Indian Institute of Science (IISc), is leading the "Aarogya Aarohan" project. This initiative aims to develop a mobile-based, AI-assisted white light imaging system for early detection of oral cancer, especially in resource-limited settings. The project involves 28 partners and is focused on scalable, point-of-care screening solutions. Dr. Anupama Shetty (Mission Director, Biocon Foundation) and Prof. Debnath Pal (IISc) are the key leaders driving this innovative initiative [5].
Recent systematic reviews and meta-analyses confirm the reliability of artificial intelligence in oral cancer detection. Pooled data from several studies indicate that artificial intelligence algorithms achieve a sensitivity of 87% and a specificity of 81% across multiple imaging modalities.
Deep learning models, in particular, have shown exceptional performance; some research reported sensitivities and specificities over 95% for histopathological images. With a diagnostic odds ratio (DOR) of 131.63 and an area under the summary receiver operating characteristic (SROC) curve (AUC) of 0.9758, the near-perfect accuracy of these models is evident.
Research using large datasets of oral cavity images taken with phone cameras confirms the feasibility and effectiveness of AI-powered early detection in real-world settings. Autofluorescence-based imaging combined with AI methods such as MobileNet has also been proposed as a low-cost, scalable early detection option for oral cancer screening.
The use of AI-powered early detection tools in oral cancer screening marks a paradigm shift in preventive dentistry. These technologies enable doctors to identify and treat high-risk lesions quickly and with unparalleled precision, potentially saving lives.
AI-powered early detection offers several benefits:
Increased Accuracy: AI algorithms consistently outperform conventional screening techniques, reducing the risk of missed diagnoses and unnecessary biopsies.
Speed and Efficiency: Real-time analysis provides immediate feedback during regular check-ups, enabling timely intervention and referral for detected abnormalities.
Risk Stratification: By analyzing patient history, lifestyle choices, and lesion characteristics, AI can help categorize patients into risk groups, supporting targeted monitoring and preventive strategies.
Resource Optimization: AI tools enhance the capabilities of healthcare professionals in resource-limited settings, ensuring that more patients receive prompt and accurate screening.
Ongoing research is focused on refining algorithms, expanding datasets, and seamlessly integrating AI into clinical workflows. The ultimate goal is to make AI-powered early detection a routine part of dental care, ensuring that no suspicious lesion goes undetected.
1. Al-Rawi, N., N. Abdo, and M. S. Al-Kadhim. 2022. “The Effectiveness of Artificial Intelligence in Detection of Oral Cancer.” Journal of Stomatology, Oral and Maxillofacial Surgery 123 (3): 353–60. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9381387/.
2. Kavyashree, C., Vimala H.S., and J. Shreyas. 2024. “A Systematic Review of Artificial Intelligence Techniques for Oral Cancer Detection.” International Journal of Digital Health 4 (1): 1–20. https://www.sciencedirect.com/science/article/pii/S2772442524000066.
3. Vinay, Vineet, Praveen Jodalli, Mahesh S. Chavan, Chaitanya. S. Buddhikot, Alexander Maniangat Luke, Mohamed Saleh Hamad Ingafou, Rodolfo Reda, Ajinkya M. Pawar, and Luca Testarelli. 2025. "Artificial Intelligence in Oral Cancer: A Comprehensive Scoping Review of Diagnostic and Prognostic Applications" Diagnostics 15, no. 3: 280. https://doi.org/10.3390/diagnostics15030280
4. Chakraborty, Parnasree, Tharini Chandrapragasam, Ambika Arunachalam, and Syed Rafiammal. 2023. “Artificial Intelligence-Based Oral Cancer Screening System Using Smartphones”. Engineering, Technology & Applied Science Research 13 (6). Greece:12054-57. https://doi.org/10.48084/etasr.6364.
5. Talwar, Vivek, Pragya Singh, Nirza Mukhia, Anupama Shetty, Praveen Birur, Karishma M. Desai, Chinnababu Sunkavalli, Konala S. Varma, Ramanathan Sethuraman, C. V. Jawahar, and et al. 2023. "AI-Assisted Screening of Oral Potentially Malignant Disorders Using Smartphone-Based Photographic Images" Cancers 15, no. 16: 4120. https://doi.org/10.3390/cancers15164120
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