یکپارچه‌سازی یادگیری عمیق با سیستم‌های pacs و رویکردهای سازمانی برای طبقه‌بندی تصاویر mri و تشخیص آسیب‌های زانو

'recent advancements in artificial intelligence (ai) technologies have significantly impacted the enhancement of diagnostic processes, particularly in magnetic resonance imaging (mri). this paper explores organizational strategies for optimizing diagnostic workflows through the integration of ai and picture archiving and communication systems (pacs). pacs, which manage and store medical images and facilitate communication between imaging data and analytical software, play a crucial role in effectively utilizing ai techniques for disease diagnosis.deep learning models in mri analysis, particularly for knee injuries such as anterior cruciate ligament tears and meniscus damage, demonstrate the high potential of these technologies to improve diagnostic accuracy and reduce errors. a key aspect of this study is the introduction of the pacs-ai platform, which serves as an interface between pacs systems and ai models. this platform enables automated, near real-time processing of medical images, providing immediate analytical results to users.the paper also addresses challenges related to integrating ai into healthcare systems, including software compatibility issues and legal and ethical concerns, emphasizing the importance of developing necessary infrastructure for effective implementation of these technologies. finally, the study examines future perspectives and research directions in the field of knee injury diagnosis using ai and pacs systems, offering recommendations for further improvements and development of these methods.'