Diagnostic performance of artificial intelligence in oral squamous cell carcinoma detection: a higher-order evidence synthesis through umbrella review and meta-meta-analysis
Abstract
Background: The integration of AI into oral squamous cell carcinoma (OSCC) diagnostics is a major milestone for digital dentistry and computational pathology, enabling faster, highly accurate detection. While numerous systematic reviews have attempted to synthesize this burgeoning evidence, their conclusions remain discordant and methodological quality varies substantially.
Aim: This umbrella review undertakes a rigorous synthesis of systematic review evidence to establish definitive accuracy metrics, evaluate methodological robustness, quantify evidence redundancy, and delineate critical implementation barriers.
Methods: A comprehensive search was conducted across MEDLINE/PubMed, Embase, Scopus, Web of Science, and Cochrane CENTRAL from inception through September 30, 2025, following PRISMA guidelines and an a priori PROSPERO-registered protocol (CRD420251157762). Systematic reviews evaluating AI for OSCC diagnosis were included. Methodological quality was appraised using AMSTAR-2, while primary study overlap was quantified via Corrected Covered Area (CCA). A meta-meta-analysis employing robust variance estimation was performed to pool diagnostic accuracy measures, accounting for statistical dependency.
Results: Fifteen systematic reviews synthesizing 341 primary studies were included. AMSTAR-2 assessment revealed a concerning distribution: only 3 reviews (20%) achieved high confidence, while 3 (20%) were critically low. Moderate primary study overlap was evident (CCA =…
How strong is the evidence?
Evidence Hub reads this as a meta-analysis. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
Read the paper
Related studies
- Active Learning during Federated Learning for Object Detection
- Fairness-aware federated learning
- Decentralized Federated Learning
- Building a Personally Identifiable Information Recognizer in a Privacy Preserved Manner Using Automated Annotation and Federated Learning
- Preservation of Patient Level Privacy: Federated Classification and Calibration Models
- Efficient explainable federated learning
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.