Kingmed Diagnostics(SH:603882) Zheng Quan Ri Bao·2025-12-08 17:12Core Insights - The application of artificial intelligence (AI) in the medical field is rapidly advancing, with significant developments showcased at the first Greater Bay Area Medical AI Conference in Guangzhou [1] - Companies like Guangzhou Kingmed Diagnostics Group Co., Ltd. are leading the way in AI medical testing, having developed various AI products and models that enhance diagnostic efficiency [1][2] - The Chinese government is actively supporting the integration of AI in healthcare, aiming to establish high-quality data sets and intelligent applications by 2027 [3][4] Company Developments - Kingmed Diagnostics has been focusing on AI in medical testing since 2020, creating the first large model "Yujian Yiyan" and the intelligent agent "Xiaoyuyue," interpreting nearly 8 million reports [1] - The company has developed specialized AI models for cervical cancer pathology and multimodal pathology genetics, significantly improving diagnostic efficiency [1] - Kingmed's cloud testing platform supports 86,000 active doctors and 168,000 active users with clinical and research assistance [1] Industry Trends - Other companies, such as Shanghai United Imaging Healthcare Co., Ltd. and Shenzhen Mindray Bio-Medical Electronics Co., Ltd., are also innovating in the "AI + healthcare" sector [2] - Mindray has launched the "Ruiying AI+" solution to enhance ultrasound capabilities through AI, while United Imaging has integrated AI into medical imaging and radiotherapy equipment since around 2018 [2] - The integration of AI in medical imaging is currently one of the most mature applications, significantly improving diagnostic accuracy and efficiency [3] Policy and Market Dynamics - The Chinese government has issued guidelines to promote and regulate the application of AI in healthcare, aiming for widespread use of intelligent decision-making tools in clinical settings by 2027 [3] - The healthcare sector, characterized by vast data and essential public needs, is a critical area for AI implementation [4] - Challenges such as inconsistent data quality, lack of standardization, and insufficient cross-institutional sharing mechanisms need to be addressed to ensure high-quality development of AI in healthcare [4]