Artificial Intelligence Performance on Pressure Injury Diagnosis: A Systematic Review and Meta-Analysis

Objective: With the advent of artificial intelligence (AI), new diagnostic tools have emerged, potentially offering more consistent and accurate detection. This study aimed to systematically evaluate the diagnostic performance of AI in identifying pressure injuries (PIs). Approach: This systematic review and meta-analysis was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and was registered in Prospective Register of Systematic Reviews (CRD 42024618716). Comprehensive literature searches were performed across PubMed, Embase, IEEE, Cochrane, arXiv, and ACM databases for studies published up to November 2024. Two independent reviewers screened studies, extracted data, and assessed methodological quality using the QUADAS-AI (Quality Assessment of Diagnostic Accuracy Studies–Artificial Intelligence) tool. Statistical analyses were performed using R 4.3.3, RevMan 5.4, Stata 17, and Meta-Disc 1.4. Results: A total of 16 studies met the inclusion criteria, among which 12 provided 147 2 × 2 confusion matrices for quantitative synthesis. The meta-analysis showed a pooled sensitivity of 0.77 (95% CI: 0.76–0.77), specificity of 0.92 (95% CI: 0.92–0.92), and an area under the summary receiver operating characteristic curve of 0.928 (SE = 0.0079), indicating high diagnostic accuracy. Methodological quality was generally fair, but most studies were retrospective and lacked external validation. Innovations: This study applied the QUADAS-AI tool for quality assessment and conducted subgroup analyses by PIs stage, algorithm type, and region to offer a nuanced understanding of AI diagnostic performance. Conclusions: AI-based systems demonstrate promising diagnostic accuracy in detecting PIs, with high sensitivity and specificity.