炒股就看金麒麟分析师研报，权威，专业，及时，全面，助您挖掘潜力主题机会！ （来源：智通财经） 智通财经2月24日讯（记者 王俊仙 何凡）紧随马年春晚的机器人热潮，正月初八，常州市第一人民医院 （下称"常州一院"）门诊大厅内，两个人形机器人"智能导诊员"——"真真"和"儒儒"正式上岗。它们不 仅具备预检分诊、信息查询等功能，还能与患者握手互动，让就医更高效、更有温度。 据了解，这是此类人形机器人在我国医院场景的全新落地应用。机器人背后搭载的是该院的"数字人"智 能系统，该系统集成了预检分诊、信息查询、适老服务、娱乐互动等多重功能，已累计提供超过8.4万 人次的交互服务。 在常州一院，类似的前沿技术探索已非孤例。早在2024年，医院便开通了江苏省内首条5G-A低空医疗 航线。更深度的融合体现在人工智能的日常渗透中：目前全院约43%-45%的门诊电子病历由AI辅助生 成，AI辅助病理科将诊断效率提升约30%，并已形成覆盖多环节的八大AI应用场景…… 在国家大力倡导"人工智能+医疗卫生"应用发展的当下，AI医疗已成为资本市场反复提及的关键词，而 常州一院的实践，则提供了一种更具现实意义的样本——这家地市级三甲医院，面 ...

21世纪经济报道记者闫硕 这个春节，元宝、千问、豆包掀起AI红包大战，主打C端健康场景的蚂蚁阿福也迅速入局。另一方面， 短视频平台的爆款内容让部分AI产品成功出圈，大模型这股风正加速吹向下沉市场。 在皖北一座小县城，用大模型当"健康顾问"已并不鲜见。49岁的秀梅前段时间体检时查出甲状腺结节， 跑了好几家医院，医生都建议静养观察，但她心里始终不踏实。侄子给她推荐了一款垂类大模型，自 此，她便频繁咨询，看着"不必过于担心"的回复，这款产品成了她康复路上的一颗"定心丸"。 后来，侄子推荐给她一款大模型APP，"我最开始问的是'良性甲状腺结节会不会转成恶性的'，输入问题 后几秒便有了回复。"秀梅说，看着"恶变概率非常低，通常在5%以下，很多研究甚至认为低于1%"的回 复，而且其中还附了多份权威资料链接，她松了一口气，反复读了好多遍。此后她又追问康复期间的各 类注意事项，AI的答复总能接住她的细碎担忧。 用户对大模型的应用，早已不局限于健康咨询场景。记者通过走访线下药店了解到，有不少人已经开始 拿着大模型推荐的药品进行咨询、购买。"不少人过年期间存有忌讳就医的习俗，即便出现小病小痛也 不愿前往医院，大多会选择自行购药缓 ...

Group 1 - The main shareholder of iFlytek Medical Technology has extended the lock-up period for one year until December 29, 2026, demonstrating confidence in the company's long-term development and potentially stabilizing market expectations [2] - iFlytek Medical won a significant national project worth 428 million yuan for AI application in healthcare, marking a shift from traditional software delivery to a "model as a service" approach, which may lead to sustained revenue and strengthen its industry position [3] - The AI healthcare sector is expected to experience a large-scale application wave by 2026, with iFlytek Medical positioned to benefit from policy-driven order growth and market expansion [4] Group 2 - The company continues to optimize its self-developed "Spark" medical model, maintaining industry leadership in general auxiliary diagnosis and health consultation, with technology upgrades and clinical implementation expected to enhance monetization capabilities [5] - In the first half of 2025, the company reported a revenue increase of 30.26% to 299 million yuan, with a net loss reduction of 42.86%, indicating potential for improved profitability as medical model orders are expected to materialize in 2025 [6]

近日，佛山市南海区"人工智能+医疗卫生"生态共建研讨会成功举办。相关部门领导，佛山市南海区人民医院、浪潮信息、 天锐医健等医疗机构及企业代表齐聚一堂，共话"人工智能+医疗卫生"深度融合的发展蓝图。会上，南海区"人工智能+医疗 卫生"生态共建框架正式发布，标志着区域智慧医疗生态建设从试点迈入标准化、规模化推进的新阶段。 为贯彻落实国家、广东省和佛山市人工智能发展部署，会上，相关部门领导，研究院代表、医院代表及生态伙伴代表共同 发布了南海区"人工智能+医疗卫生"生态共建框架，以"人民健康"为主体，依托"技术突破"与"场景深化"两翼，通过"产、 研、用"三方协同，形成"一体两翼三轮驱动"的推进机制。 "一体"：以"人民健康"为主体，以基层提质增效为主线，通过人工智能全面赋能，推动服务模式从"以治疗为中心"向"以健 康为中心"转变，为居民提供覆盖全生命周期的健康服务。 "两翼"：以"技术突破"与"场景深化"双向展开。一翼是以算力、算法、数据为核心，持续夯实技术基座，保持区域竞争 力；另一翼是以医疗、医药、医保"三医联动"为脉络，不断拓宽和深化人工智能应用场景，确保技术扎根于真实需求。 "三轮驱动"：即"产、研、用"协 ...

Core Viewpoint - The integration of AI in healthcare is rapidly advancing, with significant potential benefits in diagnostics and treatment, but it also raises concerns about accuracy, practicality, and the impact on medical professionals' skills [1][5][9]. Group 1: AI Applications in Healthcare - AI is being utilized in various medical applications such as diagnostic assistance, medical record writing, and pre-consultation, with ongoing developments supported by government initiatives [1][2]. - The average accuracy of prenatal diagnosis for congenital defects is only 30%-60%, indicating a significant opportunity for AI to improve outcomes in cases like congenital diaphragmatic hernia [2][7]. - AI has shown high efficiency in areas like image recognition, pathology interpretation, and chronic disease management, which can reduce diagnostic time and human error [3][9]. Group 2: Concerns and Challenges - There are concerns that reliance on AI may lead to a decline in the diagnostic skills of younger doctors, as they may become overly dependent on AI tools [5][6]. - The accuracy of AI is not guaranteed, and it is essential for doctors to maintain their diagnostic authority and critical thinking when using AI [5][6]. - Challenges include the lack of a mature reimbursement system for AI-assisted diagnostics and the need for funding and support for AI development in healthcare [8][9]. Group 3: Regulatory and Development Recommendations - Recommendations include establishing a clinical access and evaluation system for AI medical products, ensuring safety and efficacy in real-world applications [9][10]. - It is suggested that pilot programs be initiated in community health centers and secondary hospitals to demonstrate the effectiveness of AI applications in healthcare [10]. - The government is encouraged to integrate AI into health city and digital governance initiatives to promote collaborative development among industry, hospitals, research, and regulation [10].

广州市皮肤病医院是全国性病哨点监测网络协同单位，该院院长叶兴东近日在接受记者采访时表示，广 州同期（2025年1月-11月）梅毒报告数同比降幅同样为12.4%，梅毒感染增长势头已得到有效遏制。 羊城晚报记者 张华 通讯员 潘宁 近期，"日本梅毒感染病例连续4年超过1.3万例"的消息引起广泛关注。广东的梅毒感染病例多吗？据广 东省疾病预防控制局公布的数据，2025年1月-11月，全省梅毒新发病例较2024年同期减少9704例，同比 下降12.4%。 广州梅毒防控成效的背后，是一套集策略创新、技术突破、数字化管理于一体的"组合拳"。叶兴东介 绍，广州市皮肤病医院将梅毒防治与HIV感染防治相结合，建立了完善检测和监测体系，着力提高疫情 报告准确率、梅毒防治知晓率及规范治疗率，重点关注高危人群。 据悉，广州市皮肤病医院开发的"羊城医访"智能平台（微信小程序），实现了风险测评、线上转介挂 号、线下就诊的闭环管理，是国内首个将风险评估与诊疗转介相结合的数字化平台。叶兴东介绍，该智 能管理平台2020年上线运行，2025年进行迭代升级后，1年来累计完成梅毒感染风险评估1324人次，发 现高风险人群705人，为超过1万公众提 ...

Core Insights - The article discusses the emergence and growth of Internet hospitals in China, highlighting their role in providing convenient and efficient medical services, especially during the COVID-19 pandemic [1][14] - Internet hospitals are seen as a new model in the healthcare system, addressing issues like access to care and hospital transformation [1][14] Industry Overview - Internet hospitals are platforms that integrate online consultations, prescriptions, payments, and drug delivery, connecting patients with healthcare providers [4] - The services offered by Internet hospitals include remote diagnosis, post-hospital management, and health management [4] Industry Development History - The first Internet hospital in China was established in 2015, marking a significant milestone in the integration of healthcare and technology [9] - The COVID-19 pandemic accelerated the growth of Internet hospitals, with over 500 new hospitals established in 2020 alone [1][14] - By October 2022, there were over 2,700 Internet hospitals in China, serving more than 25.9 million patients [1][14] - Projections indicate that by the end of 2024, the number of Internet hospitals will reach 3,340, providing over 100 million consultations annually [1][14] Industry Policies - The Chinese government has increasingly recognized and supported Internet hospitals, leading to a period of policy benefits [11] - Recent policies aim to enhance the integration of artificial intelligence in healthcare, with a goal of widespread implementation by 2030 [11] Industry Value Chain - The upstream of the Internet hospital industry involves medical equipment and information technology, while the midstream consists of solution integrators [12] - The downstream primarily includes patients who utilize these services [12] User Scale and Usage Rate - As of December 2024, the user base for Internet healthcare in China reached 418 million, with a usage rate of 37.7% [13] - By June 2025, the user scale is expected to be 393 million, with a usage rate of 35% [13] Key Companies in the Industry - Notable companies in the Internet hospital sector include Ping An Good Doctor, JD Health, Alibaba Health, and WeDoctor, among others [2][15] - Ping An Good Doctor reported a revenue of 1.278 billion yuan in the first half of 2025, marking a year-on-year growth of 20.23% [15] - JD Health's revenue from health product sales reached 29.331 billion yuan in the first half of 2025, with a growth of 22.67% [17] Challenges Facing the Industry - Issues such as patient information sharing, cross-regional medical insurance reimbursement, and regulatory frameworks remain significant challenges for Internet hospitals [18][19][20] - The complexity of online diagnosis and potential medical risks also pose challenges that need to be addressed [21] Future Trends - The future of Internet hospitals is expected to focus on personalized health management driven by data and AI technologies [22] - Remote medical services will become standardized and integrated into the healthcare system, enhancing accessibility and efficiency [23] - A seamless integration of online and offline services will create a comprehensive healthcare ecosystem centered around patient needs [24]

Core Viewpoint - The pharmaceutical sector, particularly companies integrating AI technologies, has shown significant growth, with Ark Health experiencing a surge of over 76% in stock price, while WuXi AppTec and WuXi Biologics led the blue-chip stocks with notable gains [2][8]. Group 1: Market Performance - The Hang Seng Index rose by 0.9% to close at 26,848.47 points, with a total market turnover of HKD 315.19 billion, an increase from HKD 306.22 billion in the previous trading day [2]. - Among the constituents of the Hang Seng Index, 53 stocks increased while 33 declined, with WuXi AppTec rising by 8.30% and WuXi Biologics by 5.85% [4]. - Notable stock performances included Alibaba, which increased by 3.63%, and China Life, which rose by 3.51% [4]. Group 2: Company Highlights - Ark Health's stock opened with a peak increase of 76.37%, closing at HKD 3.93 per share, marking a daily increase of 65.8% due to its collaboration with Tencent Health in the "AI + Chronic Disease Management" sector [8][9]. - WuXi AppTec's stock price surged by 9.66% to close at HKD 120 per share, driven by a positive earnings forecast indicating a projected net profit growth of approximately 102.65% for 2025 [10][12]. - WuXi Biologics also saw a significant increase, with a maximum rise of 6.92%, closing at HKD 39.78 per share, as the company prepares to present at the 44th Annual J.P. Morgan Healthcare Conference [16][17]. Group 3: Financial Projections - WuXi AppTec's earnings forecast includes an expected revenue of approximately HKD 45.46 billion for 2025, reflecting a year-on-year growth of about 15.84%, with adjusted net profit projected at HKD 14.96 billion, a 41.33% increase [12][13]. - WuXi Biologics anticipates signing a record 209 new projects in 2025, with a focus on expanding its commercial pipeline and maintaining a positive outlook for revenue growth in 2026 [20].

Core Insights - The introduction of large medical models in grassroots hospitals has faced significant challenges, leading to suboptimal performance and increased workload for healthcare professionals [2][3][7] - The mismatch between the training environment of these models in top-tier hospitals and the operational realities of grassroots facilities is a critical issue [4][10][11] - There is a growing consensus that grassroots hospitals require simpler, more tailored AI solutions rather than complex models designed for advanced medical scenarios [15][20] Group 1: Challenges in Implementation - Grassroots hospitals often struggle with data integrity and structured input, which are essential for the effective functioning of large models [8][9] - The patient treatment pathways in grassroots settings are fragmented, making it difficult to gather comprehensive longitudinal data necessary for accurate model predictions [10] - The disease spectrum in grassroots hospitals differs significantly from that in top-tier hospitals, leading to inaccuracies when applying models trained on complex cases to common ailments [10][11] Group 2: Financial and Operational Constraints - The ongoing costs associated with deploying large models, including computational power and human resources, can be prohibitive for grassroots hospitals [13][14] - Many grassroots hospitals find themselves in a dilemma where investing in AI does not yield immediate operational benefits, leading to dissatisfaction among decision-makers [14][18] - The need for specialized personnel who understand both healthcare and data science further complicates the implementation of AI solutions in these settings [17][18] Group 3: Alternative Approaches - Some grassroots hospitals are opting to develop their own smaller, more focused models that align better with their specific needs and patient demographics [16][20] - There is a shift towards creating AI applications that assist with high-frequency, low-controversy tasks such as chronic disease management and patient follow-up [15][20] - Collaborative models, such as those formed within medical alliances, are seen as a viable way to share resources and reduce costs associated with AI implementation [21][22] Group 4: Future Directions - The focus is shifting from merely creating models to understanding the context of their application, including who will implement them and how they will be sustained [20][22] - Policymakers are emphasizing the need for standardized, scalable solutions that can be adapted to the unique challenges faced by grassroots healthcare providers [20][22] - The development of lightweight, modular AI solutions tailored to specific workflows is emerging as a practical strategy for grassroots hospitals [21][22]

Core Insights - The introduction of advanced medical AI models in grassroots hospitals faces significant challenges, leading to suboptimal performance and increased workload for healthcare professionals [2][11][12] - The structural issues in data integrity and the mismatch between model training environments and grassroots healthcare settings contribute to the inefficacy of these models [8][10][19] - There is a growing consensus among grassroots hospitals that they require simpler, more tailored AI solutions rather than complex models designed for larger institutions [15][18][20] Group 1: Implementation Challenges - Liu Gang, a hospital director, introduced a medical AI model to improve electronic medical record efficiency but found it did not meet expectations, causing additional workload for doctors [2][11] - The AI model struggled with local dialects and lacked access to comprehensive patient data, leading to inaccuracies in diagnosis and documentation [3][10] - The mismatch between the model's training context in top-tier hospitals and its application in grassroots settings is a common issue, resulting in ineffective outcomes [3][10][19] Group 2: Data and Structural Issues - The data environment in top hospitals is highly structured and standardized, which is not the case in grassroots hospitals, where data is often fragmented and unstructured [8][10] - Grassroots hospitals primarily deal with common diseases, while advanced models are trained on complex cases, leading to a misalignment in application [10][19] - The lack of continuous patient data in grassroots settings complicates the use of AI models that rely on comprehensive patient histories [10][19] Group 3: Financial and Operational Considerations - The ongoing costs associated with implementing AI models, including computational power and skilled personnel, pose significant financial burdens on grassroots hospitals [12][17] - Many grassroots hospitals are cautious about investing in AI due to the uncertainty of immediate returns and the need for ongoing operational support [12][17][21] - The potential for collaboration within medical alliances could provide a more sustainable model for implementing AI solutions in grassroots settings [20][21] Group 4: Future Directions - There is a shift towards developing lightweight, modular AI solutions that are more aligned with the specific needs of grassroots healthcare [20][21] - The focus is on creating AI tools that assist with common conditions and streamline workflows rather than attempting to replicate complex models from larger hospitals [15][20] - Policymakers and healthcare leaders are encouraged to adopt a cautious approach, assessing the effectiveness of AI solutions before widespread implementation [21]