The future of precision orthopaedics: personalized data-driven practice

Abstract

Advances in orthopaedic surgery have been significantly shaped by evidence-based medicine (EBM), which relies on randomized controlled trials (RCTs) to standardize care and improve outcomes. However, EBM's one-size-fits-all approach often fails to account for the heterogeneous nature of individual patients, limiting its ability to deliver personalized care. Personalized data-driven practice (PDDP), powered by AI, provides a transformative solution by integrating diverse data sources, including genetic and clinical data, imaging, and wearable device outputs, into patient-specific treatment strategies. This paper examines the complementary roles of EBM and PDDP, highlighting the capacity of AI-driven tools to enhance decision-making in orthopaedics. AI technologies, such as machine learning and Bayesian networks, enable predictive analytics, treatment personalization, and real-time data integration, fostering a shift from reactive to proactive care. However, challenges related to data quality, algorithm transparency, ethical considerations, and infrastructure development must be addressed to ensure robust and equitable implementation. By merging AI-enhanced PDDP with the established principles of EBM, orthopaedic practice can evolve into a hybrid model that enhances patient outcomes while preserving clinician oversight and ethical integrity. This integration heralds a new era of precision orthopaedics, offering a patient-centred approach in the context of big data and AI innovation.

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