Explainable Machine Learning for Predicting Postoperative Pain Outcomes Following Femoral–Sciatic Nerve Block in Knee Arthroscopy - Leonard Carrington; Sophia L. Carter; Andrew Hollis (International Journal of Clinical and Translational Medicine, 2026)
Accurate prediction of postoperative pain remains one of the most persistent challenges in perioperative medicine despite substantial advances in regional anesthesia and multimodal analgesia. In knee arthroscopy, femoral–sciatic nerve block techniques have demonstrated considerable effectiveness in reducing postoperative discomfort and improving recovery trajectories. Nevertheless, substantial inter-patient variability continues to influence analgesic outcomes, creating uncertainty in clinical decision-making and resource allocation. Recent developments in machine learning have enabled the construction of predictive models capable of identifying complex interactions among demographic, procedural, physiological, and perioperative variables. However, the increasing complexity of these predictive systems has raised concerns regarding transparency, accountability, and clinical trustworthiness. This study examines the role of explainable machine learning in predicting postoperative pain outcomes following femoral–sciatic nerve block in knee arthroscopy. Rather than focusing exclusively on predictive accuracy, the paper adopts a socio-technical systems perspective emphasizing interpretability, deployment architecture, governance mechanisms, fairness considerations, and long-term sustainability. The analysis explores how explainable artificial intelligence methods can bridge the gap between advanced predictive analytics and practical clinical adoption. Furthermore, the study evaluates the implications of integrating interpretable prediction models into perioperative workflows, electronic health record infrastructures, and institutional decision-support ecosystems. The findings suggest that explainability serves not merely as a technical enhancement but as a foundational requirement for safe and effective clinical implementation. Transparent predictive systems can improve clinician confidence, facilitate patient-centered communication, strengthen regulatory compliance, and support equitable healthcare delivery. The paper concludes by outlining future research directions involving federated learning, multimodal clinical intelligence, digital twins, and human-centered explainability frameworks for next-generation perioperative pain management systems.