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Beyond its established roles in robotic surgery and diagnostics, AI now supports advances in biomechanics, imaging, tissue engineering, drug discovery, genomics, and prosthetic control. In biomechanics, AI enables faster finite-element simulations, markerless gait analysis, and data augmentation using synthetic signals. Imaging applications include automated segmentation of the spine and hip, opportunistic screening for osteoporosis, bone metastasis detection, and three-dimensional analysis of knee osteoarthritis. In regenerative medicine, AI assists in scaffold optimization, bioprinting, and personalized cell therapies, while integration with genomic and proteomic data enhances precision orthopaedics. Machine learning\u2013based control systems also improve the usability of prosthetics and exoskeletons, reducing cognitive burden for patients. Despite challenges such as data scarcity, validation, and ethical considerations, AI is emerging as a powerful complement to traditional research methods. By accelerating workflows, improving accuracy, and enabling individualized care, AI holds strong potential to bridge laboratory discoveries with clinical applications in orthopaedics. 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