Artificial Intelligence in Oral Cancer: A Systematic Review

Introduction: Recent developments in Artificial Intelligence (AI), a novel technology that mimics human cognition, have gained worldwide scientific attention in cancer research. In oral cancer treatment, AI has promising effects on disease detection, prognosis prediction and management, particularly in studies involving squamous cell carcinoma, premalignant lesions and salivary gland tumors. This systematic review was conducted to critically evaluate the available evidence concerning AI’s accuracy, sensitivity and specificity in oral cancer detection, diagnosis, prognosis and clinical management. Methods: Original studies conducted on humans using AI technology and published in English were included. The studies included were conducted on patients with suspected/diagnosed oral cancer (any stage and grade) as the primary site. The search terminologies included “artificial intelligence,” “oral cancer,” and “oral squamous cell carcinoma.” The studies were selected based on study and patient inclusion criteria. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol was used when searching databases, like PubMed and Google Scholar, in August and September 2023. The risk of bias assessment was performed using the JBI evidence synthesis tool and a risk score was calculated for all included studies. Due to heterogeneity among the selected studies, formal quantitative syntheses were not conducted. Results: Of 775 records, 5 were critically appraised and included in this systematic review, with 611 subjects assessed. One of the 5 studies used a Supervised Vector Machine (SVM) and the rest used neural networks either alone or in combination with another modality/algorithm. The research studies have used both machine learning and deep learning algorithms, with the majority of them (4 out of 5 studies) using neural networks, which account for 90% of all the studies included in the systematic review. 60% of the studies present with low risk of bias. The sensitivity, specificity, accuracy and overall performance of all AI algorithms used in all the studies ranged from 45.5% and 84.8%, 54% and 85.3% and 59.9% and 82.05%, respectively. Discussion: The heterogeneity among studies and the majority of included studies with moderate risk of bias are some notable limitations of this present systematic review. AI-based models aid clinicians in early cancer diagnosis, prognosis and treatment efficiency. They enhance proficiency in overall cancer care. They act as an adjunct to reduce the clinician’s work burden and minimize inadvertent errors. Within the present limitations of the review and comprehensive search and analysis of the available literature based on five studies performed in a clinical real-time setting, we can conclude that utilizing AI (both machine learning and deep learning models) is effective in the early detection, diagnosis, management and prognosis of oral cancer. This is achieved irrespective of the AI model used. The systematic review protocol has been registered in the International Prospective Register of Systematic Reviews (PROSPERO) with ID Number: CRD42023461832.