Efficacy of AI in the diagnosis of oral squamous cell carcinoma from clinical images: A systematic review and meta-analysis

Introduction: Oral squamous cell carcinoma (OSCC) is a common malignancy that is frequently diagnosed at advanced stages, partly because of limitations in conventional diagnostic approaches. This systematic review aimed to evaluate the diagnostic performance of artificial intelligence (AI) models in detecting OSCC from clinical images. Methods: PubMed, ScienceDirect, the Cochrane Library, and Embase were searched from January 2000 to September 2024. Studies assessing the diagnostic accuracy of AI models for OSCC using clinical images were included. Studies that did not involve clinical images, were not specific to OSCC, or were not published in English were excluded. In accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, study selection was performed independently by 4 reviewers, with disagreements resolved through consensus. Subsequently, diagnostic accuracy data were extracted, risk of bias was assessed using Quality Assessment of Diagnostic Accuracy Studies using AI, and thematic analysis was performed by 2 reviewers. Where appropriate, a random-effects meta-analysis was conducted. Results: Nine studies comprising 47,987 oral clinical images were included, with 6 studies in the meta-analysis. AI models demonstrated a pooled sensitivity of 0.94 (95% confidence interval [CI] 0.87–0.97), specificity of 0.95 (95% CI 0.86–0.98), and an area under the curve of 0.975 (95% bootstrap CI 0.911–0.986). AI systems generally achieved higher diagnostic accuracy than general practitioners and performance comparable to specialists under controlled study conditions. Conclusion: AI models show strong diagnostic performance for OSCC detection from clinical images. However, current evidence is largely derived from retrospective and heterogeneous datasets without external validation. Prospective and blinded studies are required to determine their role as decision-support tools in real-world settings.