• Hania Shahzad, Cole Veliky, Hai Le, Sheeraz Qureshi, Frank M Phillips, Yashar Javidan, and Safdar N Khan.
• Department of Orthopaedics, UC Davis Medical Center, Sacramento, CA, USA.
• Eur Spine J. 2024 Nov 1; 33 (11): 407640814076-4081.

PurposeIntegrating machine learning models into electronic medical record systems can greatly enhance decision-making, patient outcomes, and value-based care in healthcare systems. Challenges related to data accessibility, privacy, and sharing can impede the development and deployment of effective predictive models in spine surgery. Federated learning (FL) offers a decentralized approach to machine learning that allows local model training while preserving data privacy, making it well-suited for healthcare settings. Our objective was to describe federated learning solutions for enhanced predictive modeling in spine surgery.MethodsThe authors reviewed the literature.ResultsFL has promising applications in spine surgery, including telesurgery, AI-based prediction models, and medical image segmentation. Implementing FL requires careful consideration of infrastructure, data quality, and standardization, but it holds the potential to revolutionize orthopedic surgery while ensuring patient privacy and data control.ConclusionsFederated learning shows great promise in revolutionizing predictive modeling in spine surgery by addressing the challenges of data privacy, accessibility, and sharing. The applications of FL in telesurgery, AI-based predictive models, and medical image segmentation have demonstrated their potential to enhance patient outcomes and value-based care.© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

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