Purpose of Review <p>Artificial intelligence (AI) has emerged as a useful tool across the field of orthopedic surgery. This review highlights recent literature on AI’s role in surgical outcome prediction, musculoskeletal imaging, economic and ethical considerations, with a focus on its integration in sports medicine workflow and procedures.</p> Recent Findings <p>Machine learning AI models have demonstrated superior accuracy in predicting orthopedic related patient-reported outcomes, surgical complications, and the utilization of healthcare compared to traditional, non-AI methods. Within imaging, AI applications now produce automated measurements for clinical and presurgical planning with precision equivalent to expert-level measurements. Large language AI models are increasingly used for clinical documentation, research workflows, and administrative support for healthcare delivery and effectiveness. Despite increasing integration of AI into orthopedics and its subspecialties, challenges in validation, accessibility due to cost, and ethical considerations remain.</p> Summary <p>Orthopedic surgery and sports medicine are particularly well suited for AI applications due to their well-defined, measurable clinical outcomes. Emerging AI tools and models show promise in enhancing patient outcomes, surgical planning, and healthcare efficiency. Continued AI research must prioritize external validation, ethical implementation, and educational integration to ensure responsible, effective, and reproducible use.</p>

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Artificial Intelligence and its Current Role in Clinical Outcome Prediction, Musculoskeletal Imaging, and Economic and Ethical Considerations within Orthopedics and Sports Medicine

  • Emmett O’Malley,
  • Bryan Soth,
  • Alex Capitano,
  • Alessandro Bensa,
  • Joshua Eskew,
  • Malik Dancy,
  • Benedict Nwachukwu

摘要

Purpose of Review

Artificial intelligence (AI) has emerged as a useful tool across the field of orthopedic surgery. This review highlights recent literature on AI’s role in surgical outcome prediction, musculoskeletal imaging, economic and ethical considerations, with a focus on its integration in sports medicine workflow and procedures.

Recent Findings

Machine learning AI models have demonstrated superior accuracy in predicting orthopedic related patient-reported outcomes, surgical complications, and the utilization of healthcare compared to traditional, non-AI methods. Within imaging, AI applications now produce automated measurements for clinical and presurgical planning with precision equivalent to expert-level measurements. Large language AI models are increasingly used for clinical documentation, research workflows, and administrative support for healthcare delivery and effectiveness. Despite increasing integration of AI into orthopedics and its subspecialties, challenges in validation, accessibility due to cost, and ethical considerations remain.

Summary

Orthopedic surgery and sports medicine are particularly well suited for AI applications due to their well-defined, measurable clinical outcomes. Emerging AI tools and models show promise in enhancing patient outcomes, surgical planning, and healthcare efficiency. Continued AI research must prioritize external validation, ethical implementation, and educational integration to ensure responsible, effective, and reproducible use.