Core Insights - The first Greater Bay Area Medical Artificial Intelligence Conference highlighted the necessity of integrating AI into healthcare, as emphasized by academic leaders like Zhong Nanshan, who stated that "medical AI is not a choice but a must" [1][3]. Group 1: Industry Challenges and Opportunities - The uneven distribution of medical resources and weak grassroots service capabilities in China necessitate innovative solutions through new-generation information technology [3]. - The integration of "AI + healthcare" requires a collaborative approach involving technological innovation, systemic mechanism innovation, and ecological synergy, making it a complex system engineering task [3]. - The medical AI sector is identified as a highly promising area for application due to its vast data, diverse scenarios, and essential public needs [3]. Group 2: Collaboration and Ecosystem Development - Zhong Nanshan emphasized the importance of collaboration among various institutions to advance medical AI, advocating for the integration of industry, academia, and research to facilitate rapid implementation [3][5]. - The establishment of a credible data space for medical testing and AI exploration was showcased at the conference, aiming to expand the ecological cooperation network and develop new scenarios and data products [5]. - The health data is recognized as a crucial strategic resource for the nation and a foundational element for the development of medical AI, with expectations for high-level medical institutions and tech companies to engage in innovative practices [5].

三年改造189家县级医院 "广东整合卫健、发改、财政、医保等13家省直单位力量，创新梳理'能力账'与'帮扶账'两本台账——前 者摸清粤东粤西粤北57个县（市）常见病、多发病诊疗短板，后者量化下沉团队、投入资源及帮扶成 效，实现'对账买单、闭环管理'。"省卫生健康委党组书记、主任刘利群在发布会上介绍。 三年间，广东以"县强、镇活、村稳"为目标，分层夯实基层医疗网底：在县级，改造189家县级医院， 建成急诊急救"五大中心"，规范化建设近300个特色临床专科，57个县58家综合医院全部达到国家推荐 标准，具备三级医院服务能力；在镇级，完成1883家乡镇卫生院、社区卫生服务中心改造，100%达国 家基本标准；在村级，落实每个行政村2.5万元村医补贴，实现2万个村卫生站规范化建设全覆盖，符合 条件的全部纳入医保结算，农村群众"家门口看病"不再难。 "现在镇卫生院CT、血透机配齐了！"河源市紫金县蓝塘中心卫生院院长陈豪展示的对比图直观呈现变 化，镇上73岁的血透患者陈伯，再也不用每周往返90公里到市区治疗。 每年订单式培养2000名基层医生 在人才下沉方面，广东实施"万名医师下乡"项目，每年选派不少于2000名中级以上职 ...

上 市 公 司 公 司 研 究 / 公 司 深 度 医药生物 2025 年 12 月 12 日 祥生医疗 (688358) ——打造"便携+智能"优势，外部合作前景可期 报告原因：首次覆盖 买入（首次评级） | 市场数据： | 2025 年 12 月 11 日 | | --- | --- | | 收盘价（元） | 29.65 | | 一年内最高/最低（元） | 40.91/22.79 | | 市净率 | 2.4 | | 股息率%（分红/股价） | 3.37 | | 流通 A 股市值（百万元） | 3,324 | | 上证指数/深证成指 | 3,873.32/13,147.39 | | 注："股息率"以最近一年已公布分红计算 | | | 基础数据: | 2025 年 09 月 30 日 | | --- | --- | | 每股净资产（元） | 12.49 | | 资产负债率% | 11.24 | | 总股本/流通 A 股（百万） | 112/112 | | 流通 B 股/H 股（百万） | -/- | 一年内股价与大盘对比走势： -50% 0% 50% 12-11 01-11 02-11 03-11 04-11 ...

小病不出村，大病不出 县！广东卫健"百千万工 程"三年答卷亮眼_南方 +_南方plus 12月11日,广东省人民政 府新闻办公室召开新闻 发布会，介绍全省卫生 健康系统助力"百千万工 程"三年初见成效有关情 况。 会上，广东省卫生健康 委党组书记、主任刘利 群表示，经过三年的努 力，衡量县域医疗服务 水平的几项核心指标， 都取得了初步成效。县 域内住院率稳定在85% 左右，开展县域医共体 建设的65个县（市、 区）中，基层医疗卫生 机构诊疗量占比达到 67%。今年上半年，57 个县的县级公立医院三 四级手术占比超过 51%、出院人次数达到 125.9万，与2022年上半 年相比，分别提升了4.4 和11.4个百分点。 深化改革 织密县镇村三级医疗网 刘立群介绍，广东加强 力量统筹，协同联动效 应持续增强。工作机制 上，省"百千万工程"指 挥部专门设立卫生健康 工作专班，由分管省领 导任总召集人，纳入卫 健、发改、财政、医保 等13家省直单位作为成 员，强化"一盘棋"协 作。具体实施上，认真 梳理算好"两本账"，一 本是粤东粤西粤北57个 县（市）医疗水平的"能 力账"，摸清区域常见 病、多发病的专科治疗 能力 ...

Core Viewpoint - The article emphasizes the importance of addressing data challenges in the medical imaging sector, which not only facilitates the revolutionary development of medical AI but also provides valuable experiences and models for AI applications across various industries [1]. Group 1: AI in Medical Imaging - The National Health Commission of China has set a timeline for the development of "AI + Healthcare," aiming for comprehensive coverage of intelligent diagnostic applications in primary care by 2030 [2]. - The AI medical imaging industry has matured, with major hospitals adopting AI products for diagnostic assistance [3]. - AI has significantly improved the efficiency of medical imaging diagnostics, reducing the time required for doctors to complete reports from approximately 30 minutes to 5-10 minutes, thus alleviating the workload of overburdened radiologists [5][6]. Group 2: Commercialization Challenges - Despite the substantial value created by AI in medical imaging, the industry faces a commercialization dilemma, with cumulative revenues projected to be less than 3 billion yuan from 2020 to 2024 [8]. - The low technical barriers and intense competition have led to a market where many companies offer similar products, often resorting to free trials to gain hospital access, which undermines profitability [9][10]. - Many hospitals, especially lower-tier ones, struggle with budget constraints, limiting their ability to invest in AI products, which further compresses the market potential [10]. Group 3: Future Potential of AI - To unlock greater potential, AI must enhance its value in medical imaging analysis, diagnosis, and treatment, which requires higher research and development barriers [12]. - Current AI models primarily based on Convolutional Neural Networks (CNN) have limitations in understanding complex medical images, while the introduction of Transformer models could significantly improve diagnostic capabilities [13][14]. - The integration of multi-modal data processing through Transformer models could lead to comprehensive clinical decision-making models, breaking down barriers between different types of medical data [14]. Group 4: Data Challenges - The transition from CNN to Transformer-based models presents significant data challenges, as training such models requires vast amounts of high-quality labeled data, which is difficult to obtain in the medical field due to privacy regulations [18][19]. - The complexity of multi-modal data integration further complicates the data landscape, necessitating extensive coordination and processing efforts [19]. - Addressing data issues is crucial for advancing AI in medical imaging, and companies that can establish robust capabilities in data collection, governance, and utilization will likely lead the next generation of medical AI [20].

医疗影像（X光片、CT、MRI、超声等）指利用各种成像技术，将人体内部的结构或组织以可视化的形式呈现出来，对疾病的诊断、治疗和监测起到重要的 作用。 刘劲、段磊、李嘉欣/文 近日，国家卫生健康委办公厅等五部门发布《关于促进和规范"人工智能+医疗卫生"应用发展的实施意见》，提出"人工智能+医疗卫生"发展的时间表：到 2030年，基层诊疗智能辅助应用基本实现全覆盖，推动实现二级以上医院普遍开展医学影像智能辅助诊断、临床诊疗智能辅助决策等人工智能技术应 用，"人工智能+医疗卫生"应用标准规范体系基本完善，建成一批全球领先的科技创新和人才培养基地。 当前，中国的医疗影像智能化建设确实正在提速，推广医学影像智能诊断服务，为提升基层医疗服务能力提供新路径。 由于医疗影像的数字化起步较早，数据结构相对标准化，便于计算机视觉处理，早在90年代，业界便开始尝试将医疗影像与计算机辅助诊断相结合；后来， 以卷积神经网络（CNN）为代表的深度学习技术在图像识别领域取得巨大突破。自2017年左右起，AI技术与医疗影像的研究、临床试验和实际应用开始快 速发展，成为AI技术在各行业中最早实现规模化落地的场景之一。 目前，AI医疗影像产业的 ...

国家级背书筑牢行业话语权，实现品牌跃迁 当AI医疗行业普遍困于"技术空转难落地"与"盈利模式不清晰"的双重枷锁时，讯飞医疗一纸4.276亿元 的中标公告，为行业撕开增长突破口。 此次拿下国家人工智能应用中试基地（医疗领域基层卫生服务方向）软件服务项目，是政策导向、市场 需求与技术实力共振的必然结果。 一组数据直观揭示出我国医疗健康领域的核心需求。2024年卫生健康事业发展统计公报显示，当前人均 预期寿命达到79岁，老年健康管理任务愈发繁重，2024年单是在基层医疗卫生机构接受健康管理的65岁 及以上老年人数就高达14136万。同时基层医疗服务承载持续加重，乡镇卫生院、社区卫生服务中心 （站）等基层机构诊疗人次达39.8亿，较上年新增2.3亿人次，而专业公共卫生机构卫生技术人员却同比 微降0.1万人至80.7万人，供需矛盾愈发凸显。 在这样的民生需求倒逼，以及在《关于深入实施"人工智能+"行动的意见》等顶层设计的推动下，讯飞 医疗的中标既是"国家队"对其能力的认可，更是其品牌价值与战略布局进入规模化兑现期的核心信号。 技术筑基、联动增效，GBC战略的全链路价值 此次中标更深层的战略意义，在于直接验证了讯飞医疗坚 ...

Core Insights - The implementation of artificial intelligence (AI) in healthcare is transitioning from pilot projects to large-scale promotion, with a roadmap set to achieve widespread application by 2027 and full coverage by 2030 [1][2] Group 1: AI Applications in Healthcare - The "Implementation Opinions" outlines 24 key applications of AI across eight areas, with a primary focus on grassroots applications [2] - AI will enhance diagnostic capabilities for common diseases at the grassroots level, providing support for diagnosis, prescription review, and follow-up management [2][3] - AI technologies are already being integrated into various hospital scenarios, significantly improving diagnostic accuracy and efficiency [2][3] Group 2: Efficiency and Capacity Building - AI systems can handle routine patient inquiries, allowing doctors to focus on more complex cases, thus increasing their daily patient load by 3-5 cases [3] - AI provides real-time reference suggestions to doctors, enhancing their professional skills and ensuring better patient care [3][5] Group 3: Data Management and Quality - By 2027, a high-quality data set and trustworthy data space for the healthcare industry will be established, requiring collaboration among various hospital departments [4][5] - AI model developers are working on specialized models for common tumors and chronic diseases, aiming to provide high-level medical services even in resource-scarce areas [5] Group 4: Safety and Regulation - The "Implementation Opinions" emphasizes the importance of safety in healthcare AI, proposing measures for regulatory oversight, data security, and privacy protection [6][7] - A comprehensive governance mechanism involving government regulation, institutional autonomy, industry self-discipline, and social supervision is being developed [6] - Techniques like federated learning are being explored to ensure data privacy while allowing collaborative AI model training across hospitals [7]

"我头很疼""您最近有没有压力大？睡不好？我教您几个放松的动作"……问答中，基于人工智能医疗大 模型开发的"数字医生"为居民提供了个性化健康管理建议。 辅助诊疗、处方审核等智能应用，让基层医生有了帮手；上传舌诊照片，中医智能诊断设备可辨识患者 健康状态……近年来，人工智能广泛应用于医学影像诊断、临床决策支持、慢病管理等多个医疗领域场 景。"促进人工智能在医疗卫生领域的规范应用，不断丰富应用场景，提升服务能力，保障服务安全， 优化资源配置，更好满足人民群众日益增长的健康服务需求。"国家卫生健康委有关负责人介绍。 到2030年基本实现基层诊疗智能辅助应用全覆盖 促进规范人工智能深度融入健康服务(政策速递) 本报记者 杨彦帆 《人民日报》(2025年11月30日 第 02 版) 北京市海淀区花园路社区卫生服务中心，门诊大厅的"数字医生"交互屏吸引不少居民驻足。 突出基层。提出加强紧密型县域医共体智能应用，建立基层医生智能辅助诊疗应用，加强居民慢性病规 范管理服务，强化健康管理、养老和托育服务。比如建立智能慢性病管理和个人健康画像应用，推动居 民电子健康档案规范向个人开放；推广老年人、孕产妇、儿童等重点人群的健康管理 ...

北京市海淀区花园路社区卫生服务中心，门诊大厅的"数字医生"交互屏吸引不少居民驻足。 突出融合。鼓励发展智能健康体检、健康咨询、健康管理等新型服务业态；支持医疗装备生产企业联合 医疗机构、科研院所等产业链上下游开展智能医疗装备研发攻关；支持国产智能医疗装备在医疗机构的 首台（套）应用；鼓励研发医疗卫生行业垂直大模型应用。 同时，在促进"人工智能+医疗卫生"发展中，坚持人工智能赋能而不替代的定位，优化行业管理和审核 体系，创新监管方式和预警机制，强化数据安全和个人隐私保护。 国家卫生健康委有关负责人表示，将加快建立临床专病数据集和人工智能语料库，探索建立垂直大模型 行业公共支撑服务平台，推动人工智能医疗服务体系全链条运用落地见效。 （文章来源：人民日报） 为推动新一代人工智能深度赋能卫生健康行业高质量发展，5部门发布《关于促进和规范"人工智能+医 疗卫生"应用发展的实施意见》，提出"人工智能+医疗卫生"发展的时间表：到2030年，基层诊疗智能辅 助应用基本实现全覆盖，推动实现二级以上医院普遍开展医学影像智能辅助诊断、临床诊疗智能辅助决 策等人工智能技术应用，"人工智能+医疗卫生"应用标准规范体系基本完善，建成一批 ...