2022年，创业第七年，薛翀开始跑步，用汗水来对抗郁闷和失意。 以下文章来源于36氪Pro ，作者海若镜 36氪Pro . 36氪旗下官方账号。深度、前瞻，为1%的人捕捉商业先机。 在薛翀的认知里： 产品创新，是商业世界里最大的杠杆。 文 ｜ 海若镜 来源｜ 36氪Pro（ID：krkrpro） 封面来源 ｜ Unsplash 资本寒冬、市场骤冷，谈好的5000万融资无法到账，他创立的医疗SaaS公司全诊医学只好收缩战线，退守浙江。彼时，医疗行业普遍面临大裁员的苦 楚，薛翀也是。濒死挣扎后，他决定还是挤出资源，保留一支10人小队，探索并不清晰的AI创新业务。 没想到，正是这个反常识的决策，让全诊医学翻了盘，接住了大模型时代的"馈赠"： 2025年连获创新医疗等投资的3轮融资；医疗SaaS外的AI新业务，签约ARR （年度经常性收入） 增长1 2倍，达到六七千万元，2026年签约合同额有 望达1.5亿元。 一位医疗AI从业者讲道，以前他并不知道这家公司，直到2025年全诊拿下了广安门医院、常州市第一人民医院的标。广安门医院很重视数字化；且这两 个医院大模型的标，单价不低，说明他家基础不错，有现成的东西。 在薛翀 ...

Investment Rating - The report does not explicitly state an investment rating for the CDSS industry Core Insights - The Clinical Decision Support System (CDSS) is evolving from basic rule-based systems to intelligent, scenario-based solutions that integrate artificial intelligence and clinical knowledge, enhancing diagnostic accuracy and treatment standardization [5][7] - The market for CDSS in China is projected to grow from 540 million yuan in 2020 to 900 million yuan in 2024, with a compound annual growth rate (CAGR) of 13.4%, and is expected to reach 2.01 billion yuan by 2029, with a CAGR of 18.5% [8] - The penetration rate of CDSS in China is around 20%, significantly lower than the 74% in the United States, indicating substantial growth potential in the Chinese market [9] Market Background - CDSS is defined as an information tool that integrates medical knowledge and patient data to provide intelligent assistance to healthcare professionals, aimed at improving the quality and safety of clinical decisions [6] - The evolution of CDSS has gone through three main stages: rule-based systems, integration with hospital information systems, and the current phase that incorporates artificial intelligence and multi-source data [7] Market Status - The CDSS market in China is expected to see significant growth, particularly in specialized CDSS, which is projected to grow from 20 million yuan in 2020 to 190 million yuan in 2024, with a CAGR of 80.3% [8] - The demand for CDSS is primarily focused on improving medical quality and efficiency, with high agreement across different economic regions on the need for optimized business processes [10] Market Competition - The competitive landscape of the CDSS market in China features established IT vendors, emerging tech companies, and cross-industry giants, each leveraging their strengths to capture market share [15] - Key competitive dimensions include system performance, technological innovation, and market positioning, with a focus on understanding the differentiated needs of healthcare institutions [12][13][14] Recommended Brands - The report highlights ten leading brands in the CDSS market, including Weining Health, Chuangye Huikang, and Neusoft Group, each recognized for their unique contributions to the industry [16][18][19][20][21][22][23][24][25][26] Development Trends - The CDSS industry is transitioning towards data and knowledge-driven systems, utilizing machine learning to enhance decision-making capabilities [28] - There is a trend towards diversified solutions tailored to specific clinical scenarios, including specialized CDSS for various diseases and lightweight versions for grassroots healthcare [29] - The deployment model is shifting from traditional localized systems to cloud-based solutions, reducing costs and enabling rapid updates [30]

Investment Rating - The report does not explicitly state an investment rating for the CDSS industry Core Insights - The Clinical Decision Support System (CDSS) is evolving from basic rule-based systems to intelligent, scenario-based solutions that integrate artificial intelligence and clinical knowledge, enhancing diagnostic accuracy and treatment standardization [5] - The market for CDSS in China is projected to grow from 540 million yuan in 2020 to 900 million yuan in 2024, with a compound annual growth rate (CAGR) of 13.4%, and is expected to reach 2.01 billion yuan by 2029, with a CAGR of 18.5% [8] - The penetration rate of CDSS in China is around 20%, significantly lower than the 74% in the United States, indicating substantial growth potential in the Chinese market [9] Market Background - CDSS is defined as an information tool that integrates medical knowledge and patient data to provide intelligent assistance to healthcare professionals, aimed at improving the quality and safety of clinical decisions [6] - The evolution of CDSS has gone through three main stages: rule-based systems, integration with hospital information systems, and the current phase that incorporates artificial intelligence and multi-source data [7] Market Status - The CDSS market in China is expected to see significant growth, particularly in specialized CDSS, which is projected to grow from 20 million yuan in 2020 to 190 million yuan in 2024, with a CAGR of 80.3% [8] - The demand for CDSS is primarily focused on enhancing medical quality and efficiency, with a high consensus across different economic regions on the need to optimize business processes and improve healthcare quality [10] Market Competition - The competitive landscape of the CDSS market in China is characterized by leading traditional IT vendors, emerging tech companies focusing on AI and big data, and cross-industry giants entering the market [15] - Key competitive dimensions include system performance and data advantages, technological innovation, and market positioning [12][13][14] Recommended Brands - The report highlights ten recommended brands in the CDSS market, including: 1. Weining Health 2. Chuangye Huikang 3. Neusoft Group 4. Yidu Tech 5. Senyi Intelligent 6. Huimei Technology 7. iFlytek 8. Baidu Lingyi Zhihui 9. Alibaba Health 10. Ping An Smart Healthcare [16][18][20][22][24][26] Development Trends - The CDSS industry is moving towards data and knowledge-driven upgrades, with a focus on utilizing machine learning to analyze clinical data and build dynamic medical knowledge graphs [28] - There is a trend towards diversified scenarios and solutions, with CDSS being tailored for specific diseases and integrated into new healthcare models [29] - Deployment and operational models are shifting towards cloud-based solutions, reducing costs and enabling rapid updates of algorithms and knowledge bases [30]