Abstract :
The increasing prevalence of degenerative and post-traumatic musculoskeletal disorders has amplified the demand for accurate prognostic tools to guide orthopedic decision-making. Radiographs remain the most accessible imaging modality worldwide, yet traditional radiographic grading systems (e.g., Kellgren–Lawrence) are limited by subjectivity and relatively poor predictive power for long-term outcomes. Recent advances in artificial intelligence (AI), radiomics, and deep learning now enable extraction of quantitative, reproducible radiographic biomarkers that may predict important orthopedic outcomes — such as progression to joint replacement, implant sizing accuracy, risk of revision, or functional recovery. This review critically examines the current state of development and validation of AI-driven radiographic biomarkers in orthopedics, summarizing candidate biomarker classes, methodological pipelines, validation strategies, reporting standards, and challenges for clinical translation. We emphasize the need for rigorous external validation, transparent reporting (e.g., via TRIPOD AI and CLAIM), and demonstration of clinical utility to ensure safe and equitable adoption.
Keyword :
AI, Deep learning, Radiomics, Radiographic biomarker, Joint replacement, Knee osteoarthritis, Tripod-AI.