AI-Assisted Personalized Anesthetic Planning for Arthroscopic Knee Surgery Based on Peripheral Nerve Block Characteristics

Abstract

Arthroscopic knee surgery has become one of the most frequently performed orthopedic procedures worldwide due to its minimally invasive characteristics, reduced hospitalization requirements, and favorable postoperative recovery profiles. Despite these advantages, perioperative pain management remains a substantial clinical challenge because postoperative analgesic responses vary significantly across patients. Peripheral nerve block techniques, particularly femoral nerve block, sciatic nerve block, and combined approaches, have demonstrated substantial benefits in improving pain control and reducing opioid consumption. However, existing anesthetic planning strategies often rely heavily on generalized clinical guidelines and practitioner experience, limiting the ability to accommodate individual variability in anatomical structures, physiological conditions, procedural complexity, and recovery trajectories. Recent advances in artificial intelligence have created opportunities for personalized anesthetic planning through the integration of heterogeneous clinical data sources. This study proposes a system-level framework for AI-assisted anesthetic planning in arthroscopic knee surgery based on peripheral nerve block characteristics. Rather than focusing exclusively on prediction accuracy, the paper examines the broader socio-technical ecosystem required to support personalized decision-making. The proposed framework integrates multimodal patient information, perioperative monitoring data, historical outcomes, and institutional knowledge repositories to generate individualized anesthetic recommendations. Particular attention is given to infrastructure architecture, governance mechanisms, model robustness, fairness considerations, deployment constraints, and long-term sustainability. The analysis demonstrates that successful implementation requires coordination across clinical, computational, organizational, and regulatory domains. AI-assisted planning has the potential to improve analgesic effectiveness, reduce adverse events, optimize resource allocation, and support evidence-based anesthesia practice. Nevertheless, challenges related to data quality, algorithmic transparency, interoperability, clinician trust, and institutional governance remain significant. The paper concludes by outlining future research directions aimed at creating adaptive, trustworthy, and scalable anesthetic intelligence platforms capable of supporting personalized perioperative care.