Seeing Beyond the Surgeon's Eye: Artificial Intelligence for Postoperative Monitoring of Free Vascularized Flaps: A...

Postoperative monitoring of free vascularized flaps (FVFs) is critical for early detection of ischemia and timely salvage. Although traditional monitoring relies heavily on clinical examination and adjunctive devices, these approaches are labor‑intensive, subjective, and resource‑dependent. Recent advances in artificial intelligence (AI), particularly machine learning and deep learning, have enabled automated, data‑driven approaches to postoperative free‑flap monitoring. However, the clinical readiness, methodological robustness, and translational challenges of these AI‑based systems remain incompletely understood.

This review examines recent studies on AI‑assisted postoperative free‑flap monitoring, focusing on technical methodologies, clinical applications, translational relevance, current limitations, and future research directions.

A systematic-narrative hybrid review was conducted by searching PubMed, Embase, Web of Science, and Google Scholar from January 2000 to February 2, 2026. Eligible studies reported clinically relevant outcomes, including flap viability, ischemia detection, venous congestion, thrombosis prevention, and salvage rates. Studies focused on preoperative prediction models, non‑AI monitoring techniques, editorials, and opinion articles were excluded. Two independent reviewers performed study selection and data extraction, with disagreements resolved by consensus.

Nine studies met the inclusion criteria. Most studies used supervised machine‑learning models, primarily using visible‑light images, infrared imaging, photoplethysmography, hyperspectral imaging, or multimodal sensor data for flap surveillance. All but 1 study were retrospective, and no prospective human clinical trials were identified. AI‑based systems demonstrated promising performance in detecting arterial ischemia and venous congestion, often achieving diagnostic accuracies comparable to or exceeding those of traditional monitoring methods. Diagnostic accuracy across studies ranged from 82% to 98.4%. Key challenges included limited data sets, lack of external validation, heterogeneous methodologies, absence of standardized outcome metrics, and concerns about generalizability across diverse skin tones and clinical settings.

AI‑assisted postoperative free‑flap monitoring is a rapidly evolving and promising adjunct to conventional surveillance. Although current evidence suggests benefits for early ischemia detection and workload reduction, significant barriers-particularly methodological heterogeneity, data scarcity, and limited prospective validation-must be addressed before widespread clinical implementation. Future progress will depend on standardized data sets, transparent model development, multicenter prospective studies, and ethically sound, interoperable AI frameworks that enable equitable and scalable precision microsurgical care.