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 ...

对AI期盼热切的同时，业内也出现了对AI实用性、准确性、负面影响等问题的担忧。北京市两会近期 开幕，记者联系了多位政协委员及医务工作者，围绕AI的前景与争议展开探讨。 用AI保住孩子 分隔人体胸腔和腹腔的，是一片"张开"的膈肌，一旦膈肌发育缺陷，新生儿在出生后不久就可能憋死， 也可能在产前筛查阶段便被无望的父母引产放弃。在临床工作中，北京市政协委员、首都儿童医学中心 新生儿外科主任医师马立霜了解到不少类似的案例。 当AI医生上岗，人类医生会被取代吗？在使用AI的过程中，医生会不会被误导，又会不会"变笨"？ 去年11月，国家卫健委发布《关于促进和规范"人工智能+医疗卫生"应用发展的实施意见》，点出了众 多AI应用场景，北京市卫健委随后发文支持AI发展。记者获悉，AI辅助诊断、病历书写、预问诊等功 能已在线下开展应用。 "AI或许能'保下'一些孩子。"马立霜说，膈疝不是不治之症，难点在于产前诊断与准确分型，受限于超 声技术等原因，全球出生缺陷结构畸形产前诊断准确率平均仅为30%-60%。如果AI拥有外科医生的"眼 力"，就能帮助基层超声医生识别出有救治可能的胎儿，基于这一构想，马立霜正在参与AI大模型的研 发，她 ...

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]

Core Insights - The article discusses the evolving role of AI in the medical field, particularly in traditional Chinese medicine (TCM), highlighting the integration of AI technologies to enhance diagnostic and treatment processes [3][4][5] - It emphasizes the shift from traditional experience-based practices to data-driven approaches, with AI expected to play a crucial role in modernizing TCM and making it more accessible [29][30] AI in Healthcare - By the end of 2025, AI applications in healthcare are expected to achieve high penetration but remain superficially integrated, with a focus on practical performance rather than just model parameters [5][6] - AI's role in healthcare is expanding beyond single-task assistance to encompass multi-scenario and full-chain empowerment, particularly in drug development and patient management [7][8] TCM and AI Integration - The integration of AI in TCM is seen as a potential breakthrough area, with the development of digital twins of renowned TCM practitioners to enhance knowledge transfer and patient care [10][11] - The "Shuowen" model developed by the company is noted for its ability to replicate expert diagnostic reasoning, achieving a consistency rate of 95% in treatment recommendations [11][12] Challenges and Opportunities - The article identifies significant challenges in the adoption of AI in TCM, including skepticism from patients and practitioners regarding AI's reliability and the lack of regulatory frameworks for AI applications in healthcare [20][21] - Despite these challenges, the potential for AI to transform TCM practices is highlighted, particularly in enhancing the efficiency of healthcare delivery and improving patient outcomes [19][20] Future Directions - Looking ahead to 2026, the article predicts that AI will evolve into "scenario-based intelligent agents" that can assist in various aspects of TCM, including psychological health and wellness [24][25] - The focus will be on creating personalized health management solutions that integrate traditional practices with modern technology, aiming to provide continuous support to patients [28][29]

Group 1 - Ark Health (06086) experienced a significant stock increase of nearly 40%, currently trading at 3.21 HKD with a transaction volume of 1.03 million HKD [1] - The recent collaboration between Ark Health and Tencent Health marks a new development phase in chronic disease management in China, providing comprehensive support from technology validation to large-scale deployment [1] - Ark Health, as a leading "AI + chronic disease management" service platform, leverages its innovative "AI + H2H (Hospital to Home) smart healthcare ecosystem" to establish a unique technological advantage in chronic disease management [1] Group 2 - The partnership represents a systematic output of technological capabilities and is a significant exploration of the deep integration model of "artificial intelligence + healthcare" [2] - In response to recent policy initiatives from the National Health Commission and other departments promoting the application of AI in healthcare, this collaboration aims to drive the healthy development of the AI healthcare industry [2]

Core Viewpoint - Ark Health (06086) has seen a significant stock increase of nearly 40%, currently trading at 3.21 HKD, following the announcement of a collaboration with Tencent Health on an "AI + Chronic Disease Management" solution, marking a new development phase in the domestic chronic disease management sector [1] Group 1: Company Collaboration - The partnership between Ark Health and Tencent Health aims to provide comprehensive support for industry partners, from technology validation to scenario implementation and large-scale deployment, injecting new momentum into the intelligent, compliant, and efficient development of the industry [1] - Ark Health, as a leading "AI + Chronic Disease Management" service platform in China, leverages its innovative "AI + H2H (Hospital to Home) smart medical ecosystem" to establish unique technological advantages and practical experience in chronic disease management [1] - Tencent Cloud, recognized as a leading cloud service provider, excels in key technology areas such as cloud computing, big data, and artificial intelligence, enhancing the partnership's capability to support the digital upgrade of the "AI + Chronic Disease Management" field [1] Group 2: Industry Context - This collaboration represents not only a systematic output of technical capabilities but also an important exploration of the deep integration model of "Artificial Intelligence + Healthcare" [2] - In response to recent policy initiatives from the National Health Commission and other departments promoting the application of "Artificial Intelligence + Healthcare," the partnership aligns with national efforts to advance the healthy development of the AI healthcare industry [2]