Abstract
The application of artificial intelligence in the diagnosis and analysis of knee joint MRI has significantly advanced, leveraging technologies like machine learning and deep learning to enhance both accuracy and efficiency. AI models are adept at identifying, classifying, and predicting various pathologies such as osteoarthritis and ligament tears by analyzing complex imaging data. This facilitates more accurate diagnoses by assisting radiologists. Key developments include automation of image segmentation, image resolution enhancement, and noise reduction in MRI scans. Despite these advances, integrating AI into clinical workflows, managing data variability, and ensuring extensive validation pose ongoing challenges. Nonetheless, AI's potential to transform diagnostic processes, improve patient outcomes, and reduce healthcare costs by streamlining workflows remains promising.
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