医疗卫生健康业务与人工智能技术脱节、人工智能应用与现有医疗卫生领域信息化系统冲突、医疗领域 垂类模型低水平重复建设……近日，聚焦我国当前AI赋能基层医疗存在的一些难题，国家人工智能应 用中试基地（合肥）正式启动建设，将助力人工智能技术在基层医疗卫生领域实现深度应用。 上接科技攻关，下接产业示范和量产推广，中试是科技创新和成果转化的重要环节。据合肥市副市长何 逢阳介绍，当前AI赋能基层医疗尚面临三重难点，即医疗卫生健康业务与人工智能技术脱节、人工智 能应用与现有医疗卫生领域信息化系统冲突、医疗领域垂类模型低水平重复建设。 针对这些"痛点"，合肥本次获批建设的国家人工智能应用中试基地，创新构建"政府主导、国资运营、 生态共建"的运行机制，以业务模式创新为技术落地提供场景；整合国产算力资源、科研资源、生态资 源等战略力量，形成资源聚合、优势互补的发展态势，有效降低AI赋能医疗的创新研发门槛与产业应 用成本。 据介绍，中试基地构建高效数据流转体系，汇聚5PB医疗数据，深度融合34个专业医疗知识库、42个多 模态数据集等核心资源，为基层医疗提供数据支撑；重点研发五大医学大模型，开发医疗行业应用工具 链，推动大模型快速服 ...

Core Viewpoint - The event focused on facilitating direct communication between AI companies and government officials to address pressing issues faced by the industry, emphasizing a collaborative approach to problem-solving [1][3][21] Group 1: Event Overview - The event titled "Face-to-Face with AI Enterprises" was held in Chengdu, featuring 62 company representatives and city leaders engaging in direct dialogue [1] - The format encouraged open discussions without lengthy speeches, allowing entrepreneurs to raise specific concerns related to financing, policies, and application scenarios [1][3] - The event was characterized by a lively atmosphere, with many participants eager to engage, leading to an expansion from 15 to over 60 attending companies due to high demand [7][9] Group 2: Key Issues Raised by Companies - Companies expressed a dual concern regarding financing and application scenarios, highlighting the need for government support in these areas [9][10] - Specific requests included access to the newly established Future Industry Fund in Chengdu, which aims to support local AI enterprises [10][12] - The need for practical application scenarios was emphasized, with calls for state-owned enterprises to open up opportunities for local AI solutions [13][15] Group 3: Government Responses and Initiatives - Government officials responded promptly to company inquiries, outlining specific measures such as the establishment of a 17.8 billion yuan Future Industry Fund to support various stages of investment [15] - A list of 48 innovative application scenarios related to AI was shared, with plans for further expansion to facilitate real-world applications [15] - The health sector was highlighted as a key area for AI integration, with initiatives to pilot AI-assisted diagnostics in community health centers [15][21] Group 4: Collaborative Ecosystem Development - The event fostered a collaborative environment, encouraging companies to shift from merely stating needs to exploring potential partnerships and synergies [17][21] - The presence of various stakeholders, including government departments and investment institutions, aimed to create an "industrial ecosystem" conducive to growth [17] - Future plans include weekly industry chain events to maintain ongoing dialogue and support for local enterprises [21]

Summary of Yidu Technology Conference Call Company Overview - **Company**: Yidu Technology - **Fiscal Year**: 2025 - **Key Financials**: - Total revenue: 715 million RMB - Net loss: 135 million RMB, a decrease of 38.9% year-on-year [2][3][10] - Operating cash flow outflow: 250 million RMB, a decrease of 23.8% year-on-year [2][4][11] Key Business Segments 1. AI for Medical - Revenue growth: 10.3% year-on-year in the big data platform and solutions segment [2][10] - AI platform deployed in over 30 top-tier hospitals, reducing medical record writing time to 30 seconds and TNM staging assessment time by 70% [5][14][33] - AI diagnostic assistant served 26,000 patients from February to June 2025 [9][12] 2. AI for Life Science - Revenue: 270 million RMB, a decrease of 23.7% year-on-year [18] - Active clients: 132, with 16 out of the top 20 global pharmaceutical companies as clients [8][18] - Completed 411 clinical trials and 275 real-world studies [7][18] 3. AI for Care - Revenue: 122 million RMB, a decrease of 28% year-on-year [22] - Main operator for Shenzhen and Beijing's health insurance programs, with over 6 million and 15 million insured individuals respectively [24][30] Operational Efficiency - Operating expenses (OPEX) decreased by 23% year-on-year, with OPEX as a percentage of revenue down by 10 percentage points [2][11] - Sales expenses as a percentage of revenue decreased from 26% to 20% [11] - R&D expenses as a percentage of revenue decreased from 29% to 26% [11] AI Model Development - Self-developed medical model's hallucination rate decreased by 80%, trained on over 500 billion tokens [8][10] - Performance in medical scenarios rated better than Deepseek R1 [9] Strategic Initiatives - Launched "1+N+X" product matrix for physician dictation, integrating multiple large models to enhance the entire medical process [5][14] - New data platform EVA 5.0 significantly improved data processing efficiency by over 4 times [15] Future Outlook - Expected revenue growth of approximately 20% in AI for Medical for FY 2026 [29][30] - Focus on high-quality revenue growth in AI for Life Science, with a target to exceed industry growth rates [29][30] - Plans for stock buyback due to current low stock prices, with sufficient cash reserves of approximately 3.78 billion RMB [30] Additional Insights - The company has established a strong presence in the healthcare AI sector, with significant partnerships and projects in various hospitals and research institutions [17][18][35] - Continuous investment in AI technology and data management to maintain competitive advantages in the healthcare market [34][35]