Advancements in microsurgery: A comprehensive systematic review of artificial intelligence applications

Machine learning (ML) is a branch of artificial intelligence (AI) that enables computers to learn from data and discern patterns without direct instruction. This review explores cutting-edge developments in microsurgery through the lens of AI applications. By analyzing a wide range of studies, this paper highlights AI's transformative role in enhancing microsurgical techniques and decision-making processes. A systematic literature search was conducted using Ovid MEDLINE, Ovid Embase, Web of Science, and PubMed (2005-2023). Extensive data on ML model function and composition, as well as broader study characteristics, were collected from each study. Study quality was assessed across 7 methodological areas of AI research using an adapted methodological index of nonrandomized studies (MINORS) tool. Seventeen studies met the inclusion criteria. ML was used primarily for prognosis (35%), postoperative assessment (29%), and intraoperative assistance/robotic surgery (24%). Only 2 studies were conducted beyond phase 0 of AI research. Fourteen studies included a training group, but only one of these reported both validation and training sets. ML model performance was assessed most frequently using accuracy, specificity, and sensitivity. Scores for the adapted MINORS criteria ranged from 10 to 14 out of 14, with a median of 12. Through collation of all available preclinical and clinical trials, this review suggests the efficacy of ML for various microsurgical applications. Despite this, widespread adoption of this technology remains scarce, currently limited by methodological flaws of individual studies and structural barriers to disruptive technologies. However, with growing evidence supporting its use, microsurgeons should be receptive to implementing ML-incorporated technologies or may risk falling behind other specialties.