对话｜联影智能首席科学家高耀宗：人机协同是AI医疗最优解

Core Viewpoint - Geoffrey Hinton, a Turing Award and Nobel Prize winner, has shifted his perspective on AI, now viewing it as a symbiotic relationship rather than a threat, particularly in the medical imaging field [1] Group 1: AI in Medical Imaging - AI is transforming disease screening, diagnosis, risk assessment, and clinical decision-making in the medical imaging market in China [1] - The company United Imaging established a subsidiary, United Imaging Intelligence, in 2017, focusing on AI medical solutions, and has launched over 100 AI applications, with numerous certifications from NMPA, FDA, and CE [1] - AI-assisted diagnosis is now a common tool for radiologists, significantly reducing the rate of missed diagnoses [3] Group 2: Key Personnel and Contributions - Gao Yaozong, the Chief Scientist at United Imaging Intelligence, has a background in computer vision and AI, previously working at Apple before returning to China to focus on medical AI [2][18] - Gao emphasizes the greater value of AI in healthcare compared to entertainment, highlighting the urgent need for AI solutions in China's medical landscape [2] Group 3: AI Development and Applications - The company has developed a lung nodule diagnostic grading system, C-Lung-RADS, based on extensive data from Chinese populations, enhancing early lung cancer screening accuracy [4] - United Imaging has created a mobile health management unit that provides lung cancer screenings to underserved areas, successfully identifying early-stage lung cancer cases [4] - The company has also launched an intelligent electronic medical record system that significantly reduces the time doctors spend on documentation [4][17] Group 4: Future Directions and Challenges - The ideal future technology path combines the strengths of general large models and specialized small models to enhance disease recognition and ensure precision in critical tasks [4][15] - The company faces challenges in developing truly universal, cross-modal medical imaging models and effectively integrating multi-modal information [12][13] - Regulatory challenges exist as AI medical products are classified as high-risk and require stringent approval processes [13][14] Group 5: Collaboration and Data Utilization - The company collaborates with hospitals to gather data while ensuring patient privacy and data security, employing a "data does not leave the hospital" approach [9] - Partnerships with leading hospitals are prioritized to ensure high-quality data for model training, with plans for multi-center validation for broader application [10] Group 6: Market Reach and Deployment - United Imaging's AI applications have been deployed in over 4,000 hospitals globally, integrating AI into imaging devices and providing independent AI platforms for various medical scenarios [11]