Diagnostic Accuracy of Artificial Intelligence Models for Predicting Postoperative Complications Following Free Flap Reconstruction: A Systematic Review and Meta‐Analysis

ABSTRACT Introduction To systematically evaluate the diagnostic performance of artificial intelligence (AI) models in predicting postoperative complications following flap surgery, and to compare the efficacy of different input modalities used in model training. Methods A comprehensive literature search was conducted across PubMed, Embase, Scopus, and Web of Science to identify studies utilizing AI for flap monitoring and postoperative complication prediction. A total of 12 studies comprising 18,520 patients and 32,148 input data points were included. Pooled sensitivity, specificity, likelihood ratios, and SROC curves were calculated using a bivariate random‐effects model. Results The meta‐analysis revealed a pooled sensitivity of 78.0% [95% CI: 0.54–0.91] and a pooled specificity of 88.0% [95% CI: 0.76–0.94]. The positive and negative likelihood ratios were 6.36 [95% CI: 2.54–15.91] and 0.25 [95% CI: 0.10–0.64], respectively. The area under the SROC curve was 0.91 [95% CI: 0.88–0.93], indicating excellent overall diagnostic performance. Conclusion AI models, particularly those incorporating photographic data and deep learning models, demonstrate high diagnostic accuracy and hold promise as adjunct tools for postoperative flap monitoring.