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Artificial intelligence in total knee arthroplasty: clinical applications and implications
Kyeong Baek Kim1,2, Gi Beom Kim3, Jun-Ho Kim4
1Department of Orthopedic Surgery, Pusan National University Yangsan Hospital, Research Institute for Convergence of Biomedical Science and Technology, 20, Geumo-ro, Mulgeum-eup, Yangsan-si, Gyeongsangnam-do, Yangsan, Republic of Korea.
Background:
Artificial intelligence (AI), including machine learning (ML) and deep learning (DL), is increasingly being integrated into total knee arthroplasty (TKA) to improve accuracy, efficiency, and personalized care. These technologies enable the analysis of large, complex datasets to support evidence-based clinical decision-making across all phases of the surgical process.
Main Body:
AI has demonstrated utility in multiple stages of TKA. In patient selection, ML algorithms can predict postoperative complications such as transfusion needs with high accuracy (AUC up to 0.842). For preoperative planning, DL techniques facilitate 3D anatomical reconstruction and implant size prediction, with some models achieving over 90% accuracy for exact component sizing, significantly outperforming traditional 2D templating. Intraoperatively, AI-assisted robotic systems and sensor technologies offer real-time feedback on alignment and soft tissue balancing. Postoperatively, AI-integrated wearable devices and mobile applications enable continuous monitoring and tailored rehabilitation; in some randomized trials, these tools have been associated with a statistically significant reduction in hospital readmission rates. Despite these advances, significant challenges remain, including algorithmic bias, a lack of model generalizability and explainability, and unresolved ethical and regulatory hurdles that present formidable barriers to widespread clinical implementation.
Conclusions:
AI has the potential to significantly reshape TKA by enabling more precise, data-driven, and patient-centered care. However, its promise is contingent on overcoming critical limitations. Broader implementation requires robust multicenter validation to ensure model reliability, the development of explainable algorithms to build clinical trust, and a commitment to responsible innovation. With continued progress, AI can serve as a powerful complementary tool to augment surgical expertise and enhance patient outcomes in orthopedic surgery.
