An artificial intelligence-assisted comprehensive clinical decision-making guide for total knee arthroplasty

With the demand for total knee arthroplasty (TKA) projected to reach 3.48 million surgeries annually by 2030, leveraging artificial intelligence (AI) is crucial for optimizing outcomes and healthcare efficiency. Hence, we present a systematic approach for integrating AI and machine learning approaches into clinical decision-making for TKAand other orthopedic interventions. This guide outlines evidence-based protocols for various scenarios, from comprehensive pre-operative assessment and risk stratification using natural language processing and biomarker analysis, to intraoperative decision support with computer vision for optimal component positioning. It also covers the detection of early osteoarthritis in athletes through molecular biomarkers and advanced imaging, as well as systematic post-operative monitoring for complication prevention. This guide encompasses chronic pain management, population health screening for early osteoarthritis, acute knee injury assessment in the emergency room, and complex revision TKA planning. Essential technological integrations include ResNet for imaging analysis, Claude 3 for complex reasoning, OpenCV/MediaPipe for biomechanical evaluation, and GPT-4 Turbo for documentation. For the safe, ethical, and efficient application of AI—and ultimately for the improvement of individualized orthopedic care—this framework places a strong emphasis on thorough clinical validation, regulatory compliance, bias mitigation, and economic considerations.