ABSTRACT Background: Oral cancer (OC) and oral potentially malignant disorders (OPMDs) remain major global public health challenges, particularly in low-and middle-income countries. Although early detection substantially improves prognosis, limited healthcare infrastructure restricts timely diagnosis. Artificial intelligence (AI) enabled, mobile phone-based diagnostic systems offer a promising, accessible solution, and multiple systematic reviews have demonstrated their potential. However, uncertainty persists regarding the comparative performance of AI models across diverse real-world settings. Aim: An umbrella review was aimed at evaluating the comparative performance of different AI models in detecting OC and OPMD. Materials and Methods: This research identified six systematic reviews from databases such as Medline (via PubMed), Web of Science, Scopus, and EMBASE through October 2024 which were checked at the title, abstract, and full-text levels. The risk of bias (ROB) was then assessed using the Joanna Briggs Institute’s ROB assessment tool. Results: Across included reviews, pooled sensitivity and specificity for AI-based detection ranged from 88% to 92%, with reported diagnostic odds ratios ranging from 114 to 2549, indicating strong discriminatory performance. Deep learning architectures such as EfficientNet and ResNet consistently demonstrated high diagnostic accuracy, while hybrid approaches (e.g., MLSO + SVM) showed promising performance in selected analyses. However, substantial heterogeneity was observed across studies ( I 2 often >85%), reflecting variability in populations, image acquisition protocols, and model architectures. Conclusion: Deep learning models like EfficientNet and ResNet are favored in clinical diagnostics for their exceptional performance and adaptability. Hybrid approaches, such as MLSO + SVM, also show great potential by combining the strengths of traditional and modern methods effectively.
Kumar et al. (2026) studied this question.