The integration of artificial intelligence (AI) and machine learning in orthopedic care is transforming diagnosis, treatment planning, and surgical precision. By analyzing vast datasets, AI-driven algorithms can detect fractures, predict disease progression, and assist in early diagnosis of musculoskeletal disorders with greater accuracy than traditional methods. In surgical settings, machine learning enhances robotic-assisted procedures, ensuring precise implant placement and reducing the risk of complications. AI-powered predictive modeling is also optimizing patient-specific rehabilitation strategies, improving post-surgical recovery and long-term mobility outcomes. Furthermore, real-time data analysis is being used to personalize orthopedic interventions, allowing for targeted treatments based on individual biomechanics. As AI continues to advance, its applications in orthopedic research and clinical practice will further refine patient care, making treatments more efficient, cost-effective, and tailored to specific needs.
Title : Knotless suture repair for chronic lateral ankle instability: A systematic review & single- arm meta-analysis
Hussein Jaber, University of Cambridge, United Kingdom
Title : The UK profemur recall and implant cobaltism
Stephen S Tower, University of Alaska Anchorage, United States
Title : The tomographic phenotype and the genotype of wormain bones
Ali Al Kaissi, National Ilizarov Medical Research Center for Traumatology and Orthopaedics, Russian Federation
Title : Total Knee Arthroplasty (TKA) in hemophilic arthropathy: Modern outcomes and perioperative strategies
Jack Russek, Touro University California, United States
Title : Musculoskeletal and orthopedic implications of Gender-Affirming Hormone Therapy (GAHT): A PRISMA-Guided systematic narrative review
Jack Russek, Touro University California, United States