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Area of Science:

  • Orthopaedic Surgery
  • Artificial Intelligence
  • Personalized Medicine

Background:

  • Evidence-based medicine (EBM) and randomized controlled trials (RCTs) have advanced orthopaedics but struggle with patient heterogeneity.
  • The one-size-fits-all nature of EBM limits personalized care delivery in complex cases.

Purpose of the Study:

  • To examine the complementary roles of EBM and AI-powered personalized data-driven practice (PDDP) in orthopaedics.
  • To highlight how AI can enhance clinical decision-making and personalize treatment strategies.

Main Methods:

  • Review of AI technologies (machine learning, Bayesian networks) for predictive analytics and data integration.
  • Integration of diverse data sources: genetic, clinical, imaging, and wearable device data.
  • Conceptual framework for merging EBM principles with AI-driven PDDP.

Main Results:

  • AI enables predictive analytics, treatment personalization, and real-time data integration for proactive orthopaedic care.
  • A hybrid model combining EBM and AI-enhanced PDDP can improve patient outcomes.
  • Challenges include data quality, algorithm transparency, ethics, and infrastructure.

Conclusions:

  • Merging AI-driven PDDP with EBM principles creates a hybrid model for precision orthopaedics.
  • This integration supports patient-centered care within a big data and AI framework.
  • The approach aims to enhance outcomes while maintaining clinician oversight and ethical standards.