Supplementary file 1_Artificial intelligence in the biomechanics of knee osteoarthritis: biomechanical phenotyping, risk stratification, diagnosis, treatment and clinical translation—a narrative review.docx

Available studies indicate that artificial intelligence can support cartilage and meniscus segmentation, imaging-based grading, biomechanical load estimation, preoperative planning, and rehabilitation monitoring, although the strength and clinical maturity of the evidence vary substantially across these applications. Multimodal AI models have identified imaging, biomechanical, sensor-derived, and clinical features associated with structural progression, pain, and functional outcomes, but these associations should not be interpreted as causal or clinically actionable without longitudinal, external, and prospective validation.

However, limited external validation, heterogeneous data acquisition, label bias, limited causal interpretability, limited use of patient-centered outcomes and poor workflow integration continue to constrain clinical use. Future AI-enabled KOA assessment should be grounded in biomechanical mechanisms and oriented toward patient outcomes and actionable clinical decisions. Translation will require standardized multimodal data, prospective validation and real-world utility evaluation.</p>