AI医疗的繁荣之下，投资人仍在探究企业的商业底色 编｜王小 一轮轮的寒潮不断将气温拉低，2025年慢慢走进尾声，此前传出年内有望启动IPO(首次公开募股)的两家医疗科技企业——丁香园和医联，似 乎凝滞了。 医联相关负责人对《财经》表示，目前没有具体信息可以披露。丁香园未予回应。 文｜《财经》记者 辛颖 同赛道的微医控股（下称"微医"）在9月底更新招股说明书，冲击港交所，正在焦灼地等待结果。 互联网卖药越来越火热，"互联网医疗"却还是在资本市场溅不起水花。微医、丁香园、医联不约而同转向了一个热度更高的赛道——AI医疗。 "AI医疗的热度，在项目路演现场就能感受到。"一位专注港股IPO业务人士介绍，像互联网医疗企业，如果有一些创新的AI技术与业务融合， 肯定是更投资人欢迎。 港股市场目前整体情况较好，IPO发行有很多是超额认购。冠上AI医疗概念的健康160、健康之路、方舟健客，近一年已陆续在港股挂牌上 市。 微医、微脉、镁信健康等也纷纷向港交所递表排队中。这些互联网医疗赛道曾经的战友们，虽然主攻的方向已经截然不同，不约而同开始努力 凸显自己的AI实力，奔向香港。 借AI押注IPO 微医，已经脱胎成一家AI医疗企业 ...

Core Insights - AI large models are significantly impacting the healthcare industry, driving transformation while presenting both opportunities and challenges [1][2][7] Application Status of AI Large Models in Healthcare - AI large models enhance medical efficiency and are primarily integrated into existing workflows due to industry-specific characteristics and regulatory constraints [2] - Current applications include intelligent diagnostic assistance and personalized medical recommendations, with hundreds of hospitals adopting AI solutions like DeepSeek [2] Specific Practices of AI Large Model Applications - DXY (Dingxiangyuan) has developed various solutions leveraging AI large models to improve healthcare services, drawing on over 20 years of industry data [3] Doctor-side Efficiency - DXY's "Clinical Progress Brief" tool addresses information overload for doctors by filtering and summarizing relevant medical literature, thus improving clinical decision-making efficiency [4] Medical Examination and Learning - "Dingxiang Medical Exam" provides personalized learning support for medical professionals, utilizing AI to tailor study plans based on individual performance and knowledge gaps [5] Patient-side Efficiency - DXY's "Dingxiang Doctor" enhances online consultation efficiency by organizing patient information and improving the search experience for health-related queries [6] Rational Considerations for AI Large Model Applications - Emphasis on safety and quality in healthcare applications, with a focus on quantifying impacts on critical health metrics [7][8] - The need for human assistance in AI processes to bridge the digital divide for vulnerable populations [8] Cost-Benefit Balance - Importance of evaluating hidden costs in AI development to avoid unsustainable practices [8] Enhancing AI Literacy Among Medical Personnel - Continuous education and training in AI technologies are essential for healthcare professionals to effectively integrate AI into their practices [8] Cross-Institutional Collaboration - Establishing data-sharing platforms to enhance AI model training and improve accuracy through collaborative efforts across institutions and disciplines [8] Integration of Traditional Medicine - AI can support the preservation and development of traditional medicine, facilitating its integration with modern practices [9] Future Outlook - The healthcare industry is moving towards a more intelligent, efficient, and human-centered approach, with ongoing exploration of AI applications to enhance service delivery [9]