Digital Twin Modeling of Perioperative Analgesia Pathways in Arthroscopic Knee Surgery: An AI-Enabled Approach - Christopher Doldwell; Sophia R. Bennett; Michael Carver (International Journal of Clinical and Translational Medicine, 2026)
The growing complexity of perioperative pain management has intensified the demand for intelligent decision-support infrastructures capable of integrating patient-specific characteristics, procedural variables, and dynamic physiological responses throughout surgical care. Arthroscopic knee surgery represents an important clinical setting in which regional anesthesia, multimodal analgesia, and postoperative recovery trajectories interact through highly heterogeneous pathways. Traditional perioperative management approaches often rely on static protocols and fragmented data streams that limit real-time adaptation and individualized optimization. Recent advances in digital twin technologies and artificial intelligence have created opportunities to establish computational representations of patients and clinical processes capable of supporting predictive, adaptive, and continuously updated analgesic decision-making. This paper proposes a comprehensive framework for digital twin modeling of perioperative analgesia pathways in arthroscopic knee surgery. The study examines how digital twins can integrate electronic health records, physiological monitoring systems, imaging data, anesthesia records, and patient-reported outcomes into unified computational environments that mirror perioperative conditions. Particular attention is given to system architecture, interoperability requirements, machine learning integration, governance mechanisms, clinical deployment challenges, and ethical considerations. The paper further investigates the role of digital twins in optimizing regional anesthesia selection, forecasting postoperative pain trajectories, evaluating resource allocation, and enhancing patient safety. Through a socio-technical systems perspective, the analysis highlights the opportunities and limitations associated with implementing AI-enabled digital twins within contemporary healthcare infrastructures. The findings suggest that digital twin architectures can substantially improve perioperative decision support while introducing new challenges related to transparency, bias mitigation, cybersecurity, data governance, and long-term sustainability. Future healthcare ecosystems may increasingly rely on digital twin technologies as foundational components of intelligent perioperative care networks that support precision analgesia and continuous learning health systems.