将投中网设为"星标⭐"，第一时间收获最新推送 硬科技赛道 ，"空天科技"成为新风口。 上周，北京穿越者载人航天科技有限公司正式宣布完成天使 +轮超募融资，由启迪之星创投加投。此外，追梦空天科技也在近日宣布连续完成两轮合计超亿元 Pre-A++轮及战略轮融资。Pre-A++轮融资由朝希资本领投，钧山资本、莫干山基金、海邦沣华等 机构跟投，老股东晓池资本持续跟投；战略轮融资由航投基金独家投资。 大健康赛道，合成生物学受青睐。 上周，合成生物学企业微远生物已成功完成近亿元Pre-A轮融 资。本轮融资由熔拓资本领投，道彤投资跟投，种子轮老股东大晶创投、真石资本持续坚定追加投 资。与此同时，合成生物学企业苏州一兮生物宣布完成近2亿元A轮融资，本轮融资由黄山新安江资 本投资管理有限公司管理的黄山供赢一兮股权投资基金投资。 互联网赛道，AI投资聚焦"应用层"。 上周，情感语音交互模型初创公司宇生月伴近日完成新一轮融 资，由靖亚资本和小苗朗程领投，菡源资产（上海交大母基金）跟投，心流资本FlowCapital担任长 期财务顾问。此外，ChatExcel 团队近日已完成近千万天使轮融资。此次融资由上海常垒资本、武 汉东湖天使基 ...

（1244.HK/9H2B3）领衔的港股市场正掀起新一轮投资热潮。截至 2025 年 8 月 21 日，恒生科技指数年 内累计上涨 23%，以（1244.HK/R7T5P）、（1244.HK/L4M9Q）为代表的智能驾驶概念股单月涨幅超 40%。在这个充满机遇与挑战的时点，投资者需重新审视股票、债券、黄金三大类资产的配置策略。 其他活跃标的包括： （1244.HK/V3T8R）（1244.HK/E9R5X）（1244.HK/A2W7S） （1244.HK/U4J6K）（1244.HK/O5D3M）（1244.HK/Y8H3N） （1244.HK/C7B4F）（1244.HK/I3M9Q）（1244.HK/T6V2P） （1244.HK/S8K4J）（1244.HK/G5R7D）（1244.HK/J2H8L） 以（1244.HK/X5Z8V）为首的新能源材料板块持续受益于全球碳中和进程，该企业研发的石墨烯电池 技术已进入量产阶段。在消费电子领域，（1244.HK/K3J7N）凭借全息投影设备的突破性进展，股价三 个月内实现翻倍。值得注意的是，（1244.HK/Q9P4D）、（1244.HK/F6G8H）等医疗 ...

商业化落地成效显著，四大业务板块协同发展。基层解决方案持续领跑，智医助理覆盖全国31个省市的 697个区县、超7.5万家基层医疗机构，累计提供超10.1亿次AI辅诊建议，修正诊断超176万例。医院解 决方案合作等级医院超500家；个人健康服务方面，讯飞晓医APP累计提供超1.4亿次咨询服务，下载量 突破2400万次，智能助听器线上线下全渠道布局进一步完善。 医疗AI领军企业讯飞医疗(2506.HK)发布2025年中期业绩。报告期内，公司的基层解决方案、区域解决 方案收入分别同比增长52%、178%，患者管理服务收入同比增长10%，整体营收达2.986亿元，同比增 长30%。 ...

上海科创基金表示："惠每科技在中国临床AI医疗市场已是清晰的龙头，且份额持续增长。7月31日，国 务院常务会议审议通过《关于深入实施"人工智能+"行动的意见》，上海也在加快打造具有国际影响力 的人工智能发展高地。我们期待惠每科技能够在下一阶段积极推动国际化战略，依托自身产研优势以及 在上海的国际化区位优势，加速中国AI最佳实践走向世界。" 钟鼎资本表示："惠每科技多年积累已经形成了兼具深度与广度的全国医院网络，处在临床诊疗的核心 工作环节。我们期待公司基于'医护手边的AI助手'这一身位优势，持续推动AI技术与场景的深度融合， 助力AI技术在临床医疗的真正落地应用。" 投资界8月18日消息，医疗AI解决方案头部企业惠每科技宣布完成近2亿元新一轮融资。本轮融资由上 海科创基金及钟鼎资本共同领投，启明创投继续加持、长宁资本以虹桥睿智投资平台进行跟投。 惠每科技成立于2015年，始终专注于应用人工智能（AI）技术解决临床痛点通过打通院内实时临床数 据，构建起AI临床数据基座，结合突出的医学能力及循证医学知识库积累，形成AI辅助临床决策、赋 能事中质控、实现事中费控的三大类解决方案，全面覆盖医疗机构临床、管理、费控的核 ...

Group 1 - The 31st China International Medical Instrument and Equipment Exhibition and Technology Exchange Conference will be held from August 15 to August 17, 2025, in Beijing, focusing on AI technology applications in healthcare [1] - The seminar titled "Walking with AI: Health Silk Road AI Empowerment" aims to break down medical resource barriers and promote efficient collaboration among medical resources across regions and countries [2] - The "Xihe No. 1" medical model, developed by Peking University Third Hospital and other partners, has been trained on 1 million real clinical cases, achieving over 100 billion parameters and high accuracy in medical knowledge [3][4] Group 2 - The "Xihe No. 1" model is built on rigorous clinical data, ensuring low error rates and high compliance, which is crucial for its commercial application [4] - The model addresses significant issues in cardiovascular disease diagnosis, such as high misdiagnosis rates and delays in treatment, by providing a core research system focused on clinical needs [5] - The company aims to create an AI + healthcare ecosystem that spans various fields, including omics research and drug development, while also exploring AI-enabled hardware products [6] Group 3 - The company, Tianjin TEDA Biomedical Engineering Co., Ltd., is committed to enhancing global healthcare through AI and has established a strong innovation capability in medical data platform construction and resource collaboration [8] - A recent partnership with Shenzhen Computing Science Research Institute aims to improve data quality and analysis for AI medical model optimization [8] - The company anticipates serving over 100 million patients within three years and aims to establish a global AI collaboration network for shared medical wisdom [9]

Summary of Key Points from the Conference Call Industry Overview - The conference call discusses the **medical AI industry**, highlighting significant advancements and trends in AI drug development and digital therapies [1][2][4][3]. Core Insights and Arguments - **Collaboration and Revenue Growth**: JingTai Technology partnered with Dori Train to provide a drug development platform using AI and robotics, with an initial payment of $100 million. If fully recognized, this project is expected to generate over 700 million RMB in revenue, reflecting several times growth compared to last year [1][2]. - **Diverse Business Models**: The AI pharmaceutical sector has evolved from early project collaboration models to milestone payment structures, with contracts reaching up to $5.89 billion, indicating increased recognition of large platform capabilities [1][5]. - **Role of AI Platforms**: AI platforms are crucial in drug development, covering more targets and enhancing pharmaceutical companies' trust, leading to more autonomous drug development and project collaborations [1][6]. - **Types of Medical AI Products**: Medical AI products are categorized into efficiency tools and diagnostic assistants, aimed at improving workflow efficiency and treatment effectiveness, respectively [1][8]. - **Impact on Drug Development Timeline**: AI technology can significantly shorten drug development timelines, potentially reducing the time from target discovery to IND application to 2-3 years, thus extending the sales window for innovative drugs [1][11]. Additional Important Content - **Digital Therapeutics**: Digital therapies show significant effectiveness in treating mental, endocrine, and ophthalmic diseases, transforming traditional prescriptions into AI product prescriptions [3][13]. - **Challenges in Digital Therapeutics**: Despite the promising outlook for digital therapies, challenges remain, including the need for extensive clinical trials and the current lack of large-scale digital therapy companies [18]. - **Market Potential**: The medical AI field is viewed as a high-potential area, with companies like Jinda Holdings and JingTai Technology showing strong performance and market opportunities [21][22]. - **Future Outlook**: The second half of 2025 is expected to see increased application of AI in healthcare, with several companies identified as having high potential for returns and success [21][22]. This summary encapsulates the key points discussed in the conference call, providing insights into the medical AI industry's current state and future prospects.

Group 1: Digital Infrastructure Financing - The central bank and seven departments issued guidelines to support the integration of the digital economy with the real economy, emphasizing the use of technologies like big data, blockchain, and AI to streamline processes and enhance service efficiency for manufacturing, especially for SMEs [1] - The guidelines propose strengthening long-term loan support for digital infrastructure projects such as 5G, industrial internet, and data centers, while also encouraging diverse financing methods like leasing and asset securitization to meet the substantial funding needs of digital infrastructure [1] - The policy is expected to stimulate demand for upstream hardware like servers and optical modules, while also promoting the implementation of industrial internet platforms and AI applications [1] Group 2: Commercial Aerospace Development - The successful launch of the low-orbit satellite group from the Hainan commercial space launch site using the Long March 12 rocket demonstrates the site's high-density launch capability, with two launches occurring within five days [2] - This achievement marks a significant milestone in China's commercial aerospace launch system, potentially accelerating the satellite internet constellation networking process [2] - The increased launch efficiency may provide China with a competitive edge in low-Earth orbit resources, although challenges related to rocket supply chain capacity and space traffic management need to be addressed [2] Group 3: AI in Healthcare - The National Development and Reform Commission approved the establishment of a national AI application pilot base in clinical medicine, led by Zhongshan Hospital affiliated with Fudan University, focusing on addressing industry pain points and creating an innovative support platform [3] - The pilot base aims to bridge the gap between research outcomes and clinical applications, potentially shortening the product deployment cycle for AI in healthcare [3] - The initiative may enable top-tier hospitals to evolve from technology users to standard setters, with the effectiveness of the platform hinging on the establishment of a regulatory sandbox that balances medical ethics and technological experimentation [3]

Core Viewpoint - Union Medical and AI technology is advancing in the healthcare sector, with a focus on enhancing diagnostic efficiency and accuracy through intelligent systems [2][12]. Group 1: AI Applications in Healthcare - Union Medical is collaborating with various medical institutions to develop intelligent systems for diagnosing multiple body parts, including the abdomen and brain [2][13]. - The AI application for cancer metastasis has been implemented in over 400 hospitals, indicating a significant expansion of its reach [3][5]. Group 2: Collaboration and Achievements - Since 2018, Union Medical has partnered with Sun Yat-sen University Cancer Center to develop important AI applications, including online adaptive radiotherapy and AI for metastatic tumors [4][5]. - The AI systems for brain and bone metastases have been successfully deployed nationwide, improving the efficiency and precision of cancer care [5][6]. Group 3: Enhancing Patient Experience - The collaboration has led to the creation of a smart consultation solution that streamlines patient information collection and reduces the burden of manual record-keeping for doctors [6][7]. - The AI pre-consultation system allows patients to describe their symptoms interactively and upload previous examination reports, enhancing the overall patient experience [6][7]. Group 4: Efficiency in Imaging Diagnosis - A human-machine collaboration challenge demonstrated that the chest multi-check AI system improved diagnostic efficiency by 25%, allowing for quicker identification of abnormalities [8][9]. - The chest multi-check AI system can automatically detect 73 common chest abnormalities with an average AUC value of 94%, showcasing its diagnostic accuracy [11][12]. Group 5: Future Developments - Union Medical plans to continue developing intelligent systems for various body parts in collaboration with medical institutions, aiming to provide stronger support for clinical diagnosis [13].

Core Insights - The global medical AI sector is becoming a competitive arena, with significant advancements in technology and application integration in China [2] - The development of AI in healthcare has transitioned from pilot scenarios to ecosystem restructuring, emphasizing the need for a new infrastructure for medical data [2] - Challenges remain in the supply of high-quality data samples and the translation of clinical research into practical applications [2] Group 1: Current State of Medical AI in China - Medical AI in China is experiencing a new wave of integration between technology and practical scenarios, aiming for breakthroughs in efficiency, value, and equity [2] - Over the past decade, China has progressed from the popularization of electronic medical records to AI-assisted diagnosis and intelligent treatment platforms [2] - Shanghai has established itself as a core training base for AI, driven by data [2] Group 2: Challenges in Medical AI Development - There is a shortage of high-quality data samples available for AI training [2] - The proportion of research-oriented hospital beds in China is still low, indicating a gap in achieving research goals [2] Group 3: Practical Applications of AI in Healthcare - AI applications in pediatrics have effectively alleviated the pressure on doctors during consultations [2] - Digitalization of the entire process in oral imaging diagnosis has been achieved [2] - Remote diagnosis for skin diseases is rapidly gaining popularity, along with advancements in smart payment, triage, and follow-up services [2] Group 4: Future Directions for Medical AI - The company suggests promoting interdisciplinary multimodal data integration [3] - Exploration of automated surgical systems is recommended [3] - Development of digital therapies and personalized treatments is encouraged [3] - Strengthening the AI risk governance framework across technology, ethics, and regulations is essential [3]

Core Viewpoint - The healthcare sector is experiencing a significant transformation driven by advancements in AI technology, particularly in areas such as AI-assisted diagnosis and drug development, despite facing challenges in data governance, clinical translation, and ethical considerations [1][2]. Group 1: AI in Healthcare - AI is widely applied across the healthcare process, enhancing efficiency and patient experience in areas like health management, imaging analysis, and drug development [3]. - The AI healthcare market is projected to grow from 97.3 billion yuan in 2023 to 159.8 billion yuan by 2028, indicating a positive trend in the sector [3]. - Major tech companies like Tencent, Ant Group, and Huawei are increasingly investing in AI healthcare, focusing on transforming concepts into commercial applications [3][4]. Group 2: Challenges in AI Implementation - The industry faces several barriers to scaling AI applications, including data privacy, clinical validation, operational capabilities, and interoperability of ecosystems [4]. - Successful commercialization of AI in healthcare requires a closed loop in processes, compliance, and business models to truly empower healthcare professionals and create value for patients [4]. Group 3: AI in Drug Development - AI-native startups are gaining attention, with their valuation logic focusing on model capabilities, computational efficiency, and data barriers, differing from traditional pharmaceutical companies [5]. - The collaboration between AstraZeneca and China’s CSPC Pharmaceutical Group highlights the potential of AI-driven drug development, with a total potential value exceeding 5.3 billion USD [6]. - AI tools have shown significant ROI in drug development, particularly in lead compound design, reducing the candidate selection process from two years to under one year [6]. Group 4: Regulatory and Market Considerations - The FDA's recent initiatives to integrate AI tools into their processes demonstrate a shift towards modernizing regulatory frameworks, which is crucial for Chinese pharmaceutical companies looking to enter international markets [9][10]. - Companies must prepare for international market entry by aligning with FDA guidelines, establishing secure environments, and developing talent that understands both drug development and AI compliance [10]. Group 5: Data Standardization and Global Trials - AI-driven synthetic control arms and real-world data simulations are being recognized by the FDA as valid methods for addressing patient population differences in international multi-center trials [11]. - To tackle data standardization issues in emerging markets, companies should adopt international data models and utilize technologies like federated learning to ensure data quality while maintaining patient privacy [11].