Core Viewpoint - The medical device industry is experiencing a transformation driven by AI, evolving from an efficiency tool to an "intelligent collaborator" [1][9][11] Group 1: AI Integration in Medical Devices - AI functionalities have become standard in medical devices showcased at the recent CMEF, indicating widespread adoption [2][8] - Major companies like GE Healthcare and Siemens have been intensively integrating AI into their products over the past few years [4][6] - The demand for AI in China's medical market is significant, with hospitals facing high patient volumes and a need for efficiency [5][6] Group 2: Efficiency and Workflow Improvements - AI applications have visibly improved efficiency in medical processes, such as reducing radiation therapy time from days to 24 minutes [6][9] - AI is helping to alleviate the workload of doctors, particularly in high-demand environments like China, where patient scans can exceed hundreds per day [6][9] - The integration of AI allows for enhanced image quality and diagnostic capabilities without the need for advanced hardware [7][9] Group 3: Challenges and Future Directions - Despite advancements, AI still faces challenges in data acquisition, learning capabilities, and the complexity of medical problems [1][11] - The industry is moving towards multi-modal diagnostics, integrating various data types for comprehensive health assessments [11][12] - Collaboration among device manufacturers and software companies is accelerating to address the challenges of diverse AI applications in healthcare [11][13] Group 4: Ecosystem Development - Companies are focusing on building digital ecosystems to enhance innovation and clinical application of AI technologies [14] - The emergence of platforms that integrate AI across various medical scenarios is being prioritized to streamline patient care and clinical workflows [12][13] - The competitive landscape is shifting from parameter-based competition to ecosystem collaboration and practical application of AI in healthcare [10][11]