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Bone Bulletin

Abstract

Background

Artificial intelligence (AI) is rapidly transforming shoulder and elbow surgery, driving significant advancements across various domains. Currently, AI is enhancing diagnostic accuracy through imaging-based analysis, predictive modeling of clinical outcomes, implant identification, and automated image segmentation.1,2 AI-driven personalized treatment plans are also anticipated to optimize patient care by tailoring interventions based on individual data analysis.3 Additionally, deep learning algorithms have been developed to automatically classify large sets of preoperative and postoperative radiographs in the setting of shoulder arthroplasty.4 These technologies, combined with AI, could revolutionize surgical training and intraoperative guidance, offering real-time data and enhanced visualization. Looking ahead, the potential for AI in shoulder and elbow surgery is expansive. Its integration with robotic-assisted surgery is expected to improve precision and outcomes, particularly in procedures such as total shoulder arthroplasty (TSA).5 Additionally, the combination of AI with augmented reality (AR) and virtual reality (VR) offers the promise of revolutionizing surgical training and intraoperative guidance by providing enhanced visualization and real-time data.5,6 Despite the potential for revolutionizing different domains of shoulder surgery, as with any new piece of technology, there are potential problem areas regarding artificial intelligence. The main areas of concern to date include: the quality and bias of the data used to train algorithms and the subsequent validation of the algorithm, challenges with regulation and implementation, and skill atrophy of shoulder surgeons due to over-reliance on artificial intelligence.

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