从早期推动医疗信息化联网，到如今向"智能云生态企业"转型，成立于1997年的创业慧康，在医疗信息 化行业已深耕近三十年，见证了行业从单机系统到网络化、从信息化到智能化的全过程。 "当前行业已进入全新的'效率竞争'阶段。"创业慧康董事长张吕峥近日接受中国证券报记者专访时表 示，公司始终以医疗业务痛点为导向，通过构建开放的云生态平台"慧康云3.0"，积极布局AI和数据要 素，正从单纯的IT产品供应商转变为能够持续为医疗机构乃至医药、保险行业提供医疗数据智能服务的 云生态企业。 以数据先发优势构筑行业壁垒 谈及医疗信息化行业的现状，张吕峥表示，传统IT产品服务商模式已难以满足医疗机构的需求，行业正 在从"系统建设"向"数据驱动的价值创造"转型。 他进一步解释，这一转型围绕三个维度展开：从"一次性产品交付"转向"持续性云服务"，与客户建立长 期价值绑定；从实现技术功能转向挖掘数据价值，运用大模型等工具激活医疗数据潜力；从独立系统开 发转向开放生态协同，整合第三方资源构建医疗数字化生态。 支撑这一转型的核心，是创业慧康多年来积累的数据先发优势。早在2015年，公司就前瞻性布局数据要 素相关业务，与浙江大学联合成立大数据 ...

Investment Rating - The report maintains a "Positive" investment rating for the AI+Healthcare industry [6] Core Insights - The reasoning and multimodal capabilities of large models are continuously upgrading, and application costs are decreasing, driving healthcare institutions to accelerate the integration of AI technology. The global generative AI market in healthcare is expected to reach $17.2 billion by 2031, with a compound annual growth rate (CAGR) of 32.60% from 2023 to 2031 [1][18] - The current phase of AI in healthcare has transitioned into a multimodal integration stage, addressing issues such as information silos and data fragmentation that existed in earlier AI applications. Large models utilize a "pre-training + fine-tuning" architecture to process multimodal healthcare data [1][12] - DeepSeek, a domestic open-source large model, is characterized by low cost and high performance, accelerating its penetration into the healthcare industry. It can quickly analyze various types of medical data, aiding doctors in complex case management [2][13] - Major international players like NVIDIA and Microsoft are actively entering the healthcare sector, leveraging their core capabilities through acquisitions and ecosystem empowerment. Companies like Tempus AI and HIMS have successfully commercialized AI solutions, showing significant revenue growth [3][42] Summary by Sections 1. Large Model Technology Upgrade Driving AI in Healthcare - The evolution of AI technology in healthcare has progressed through four key stages: rule-driven systems, traditional machine learning, deep learning with single-modal models, and the current multimodal integration era [11] - The multimodal capabilities of large models enable comprehensive data processing, enhancing clinical decision support, drug development, and telemedicine applications [12][18] 2. International Landscape: Major Players and Innovations - NVIDIA and Microsoft are leading the charge in AI healthcare, with NVIDIA focusing on hardware and ecosystem investments, while Microsoft integrates AI tools into its cloud services [22][28] - Tempus AI has built the largest multimodal database, supporting personalized treatment plans and achieving significant revenue growth [35][37] - HIMS has seen rapid growth in subscription users and revenue, driven by its AI-powered healthcare solutions [42][43] 3. Domestic AI+Healthcare Company Overview - Domestic companies in the AI healthcare sector can be categorized into three types: general large model providers, data service companies, and traditional medical IT companies transitioning to AI [4][47] - iFlytek's Starfire medical model has shown superior performance in diagnostic recommendations and health consultations compared to other models [48][50] - Yunzhisheng is leveraging its self-developed "Shanhai" large model to provide specialized medical information support [54]