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蚂蚁AQ应用上线 AI如何让医生们“分身有术”
Huan Qiu Wang· 2025-06-27 02:21
【环球网科技报道 记者 李文瑶】贵州山区一位老人拍下手臂上的红疹照片上传后,屏幕另一端就有皮肤科专家AI分身"接诊";杭州市七医院副院长毛洪京 的"数字分身"单日服务量突破11万人次,是他线下月接诊量的180倍;上海仁济医院的泌尿外科AI助手,正在提升基层医生4%-8%的诊断准确率…… 0:00 / 0:26 这些场景正发生在蚂蚁集团新推出的AI健康应用AQ上。6月26日,这款聚焦全民健康管理的App正式上线,提供超100项AI服务,并连接全国超5000家医 院、近百万医生及近200位名医的AI分身。 蚂蚁集团副总裁张俊杰介绍,去年9月在支付宝上线的"AI健康管家",已服务超7千万用户,历经近10个月持续打磨,此次全新推出的独立应用AQ,具备"问 答更专业、服务更全面、健康更懂你"三大特点。 在医疗资源结构性短缺的背景下,AI能否为全民提供新的医疗健康解决方案? 会追问的 AI 医生,读懂 90% 的医疗报告 值得关注的是,AQ试图用技术重构医疗服务链路。它不仅是咨询工具,更接入了实体医疗资源:为了帮用户选好医院、找好医生,AQ连接了全国超5000家 公立医院、近百万可挂号或线上问诊的医生,用户提需求,AI帮 ...
医渡科技20260626
2025-06-26 15:51
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]
树兰医疗郑杰:计算医学终极目标是“生命仿真”,AI未来医院要朝无界化发展
Tai Mei Ti A P P· 2025-06-25 13:30
Group 1 - The core viewpoint of the articles revolves around the advancements in AI technology within the healthcare sector, particularly through the initiatives of Shulan Medical, which has launched its AI health assistant, Dr. Shu, and is actively pursuing a strategy focused on computational medicine [2][10][19] - Shulan Medical has established strategic partnerships with various data intelligence companies to enhance its smart healthcare initiatives and implement its AI future medical strategy [2][10] - The founder and president of Shulan Medical, Zheng Jie, emphasizes the importance of computational medicine as a systematic and mechanism-driven paradigm for precision healthcare, which integrates multiple disciplines including statistical learning and AI technology [2][4][19] Group 2 - The concept of computational medicine is gaining traction globally, with initiatives like the EU's virtual human twin database and the NIH's Bridge2AI project in the US, which focus on multi-modal data construction to support precision medicine [3][4] - Shulan Medical's roadmap for computational medicine includes steps from data standardization to life modeling, aiming to create a patient-centered digital twin for personalized medical decision-making [3][4][5] - The development of electronic medical records (EMR), electronic health records (EHR), and personal health records (PHR) is crucial for building a comprehensive data foundation for computational medicine [5][6] Group 3 - The Open Medical and Healthcare Alliance (OMAHA), founded by Zheng Jie, aims to promote machine-readable standards for healthcare data, which is essential for achieving data interoperability and supporting the development of computational medicine [6][7] - OMAHA has introduced the HiTA technology stack, which enhances the interoperability of medical data and provides a high-quality standardized data foundation for personal health databases [7][10] - The transition from static data to dynamic data is highlighted through the development of personal digital twins (PDT), which aim to closely replicate human life through comprehensive information modeling [7][10] Group 4 - The rapid development of medical AI has led to its integration into various healthcare applications, with a focus on improving hospital operational efficiency, patient services, and research capabilities [10][11] - Shulan Medical's AI health assistant, Dr. Shu, is designed to enhance patient interaction and streamline the healthcare process by providing pre-diagnosis, diagnosis assistance, and post-treatment follow-up [12][13] - The digital twin concept is being explored to provide remote medical services, allowing for more personalized and efficient healthcare delivery [12][14] Group 5 - Shulan Medical's "All in Technology" strategy aims to reconstruct future healthcare models through computational medicine, integrating AI and wearable technologies to offer comprehensive health services [17][19] - The upcoming AI Future Health Medical Center is set to combine various healthcare technologies to create a seamless health service experience for users [18][19] - The focus on building a feedback loop between AI healthcare capabilities and real-world data is essential for enhancing the accuracy and accessibility of AI-driven medical services [18][19]
不插管、不麻醉、零痛苦!达摩院AI靠一张CT让早期胃癌现形
硬AI· 2025-06-25 11:23
Core Viewpoint - The article discusses the breakthrough of the GRAPE AI model for gastric cancer screening, developed by Zhejiang Provincial Cancer Hospital and Alibaba DAMO Academy, which aims to address the high incidence and mortality rates of gastric cancer in China through non-invasive CT imaging [1][3][4]. Group 1: The Gastric Cancer Dilemma in China - Gastric cancer is a significant public health issue in China, with approximately 358,700 new cases and 260,400 deaths annually, accounting for nearly 40% of global cases [3]. - The five-year survival rate for gastric cancer in China is only 35.9%, significantly lower than Japan (60.3%) and South Korea (68.9%), primarily due to the lack of early screening programs [3][4]. - Early detection is crucial, as the five-year survival rate for early gastric cancer can reach 95-99%, while late-stage patients have a survival rate of less than 30% [4]. Group 2: Limitations of Current Screening Methods - The traditional method of endoscopy faces three main challenges: invasiveness, resource dependency, and inefficiency, leading to low acceptance rates among the population [5]. - Current non-invasive screening methods, such as serological tests, have shown limited effectiveness in improving detection rates, creating a market need for a new, efficient screening tool [6]. Group 3: GRAPE Model Overview - The GRAPE model utilizes a two-stage deep learning framework to analyze CT images, overcoming previous assumptions that CT imaging could not effectively screen for gastric cancer [8][9]. - The model has demonstrated high performance in large-scale validation, achieving an AUC of 0.970 in internal validation and 0.927 in external validation, outperforming human experts [13][14]. Group 4: Ambitious "One Scan, Multiple Checks" Strategy - DAMO Academy aims to expand the GRAPE model's application beyond gastric cancer to include multiple diseases, streamlining the integration of AI tools for hospitals [17]. Group 5: Commercialization Strategies - The commercialization of GRAPE involves multiple approaches, including B2B sales to health checkup organizations, B2B2C models for hospitals, OEM partnerships with imaging device manufacturers, and exploring value-based healthcare models [20][21][22][23].
持续深化自研医疗垂域大模型的技术攻坚与场景赋能 医渡科技(02158)发布年度业绩,收入7.15亿元
智通财经网· 2025-06-25 09:41
Core Insights - The company reported a revenue of RMB 715 million for the fiscal year ending March 31, 2025, representing a year-on-year decrease of 11.4% [1] - The loss attributable to shareholders was RMB 118 million, a reduction of 39.58% compared to the previous year, with a loss per share of RMB 0.11 [1] Business Performance - The company has developed the "AI Medical Brain" YiduCore, which has established a comprehensive barrier across data, computing power, algorithms, and scenarios, processing 1.15 billion patient visits and 6 billion authorized medical records [1] - The company has achieved a leading position in technology validation, evidenced by top performance in evaluations organized by the National Health Commission [1] - The company has deployed its AI platform in over 30 top-tier hospitals, demonstrating its capability to transition from technology validation to industrial value [1][2] Client Engagement and Solutions - The company has provided solutions to 110 top hospitals and 44 regulatory bodies, covering over 4,000 hospitals, and has upgraded its AI and data platforms to versions 2.0 and 5.0 respectively [3] - The company serves 132 life sciences clients, with a revenue retention rate of 87.51% among its top 20 clients, including 16 of the top 20 multinational pharmaceutical companies [3] - The company has maintained its position as the main operating platform for "Shenzhen Huimin Insurance" and "Beijing Huimin Insurance," with insured individuals reaching 6.09 million and over 15 million respectively [3] Financial Health - The company’s total revenue for the fiscal year was RMB 715 million, down 11.4% due to external market conditions and product mix changes, but the annual loss was reduced to RMB 1.35 billion, a decrease of 38.9% [4] - The company has improved cash management, resulting in a 23.8% reduction in cash outflow from operating activities year-on-year, with cash reserves totaling RMB 3.309 billion [4]
不插管、不麻醉、零痛苦!达摩院AI靠一张CT让早期胃癌现形
Hua Er Jie Jian Wen· 2025-06-25 09:14
2025年6月24日,国际顶级医学期刊《自然·医学》(Nature Medicine)刊发的一篇论文,在中国乃至全球的医疗AI领域投下了一枚重磅炸弹。 由浙江省肿瘤医院与阿里巴巴达摩院联合团队研发的胃癌筛查AI模型GRAPE,宣告仅通过最常规的腹部平扫CT影像,实现对胃癌,特别是早期 胃癌的规模化筛查。 在胃癌发病率、死亡率双高,而早期诊断率严重不足的中国,这一成果直指一个规模巨大、却始终未能有效解决的公共卫生痛点。 继胰腺癌筛查模型PANDA之后,达摩院的"平扫CT+AI"多癌筛查战略再下一城,其背后的商业逻辑、市场格局与未来想象空间,值得进行一次彻 底的审视与剖析。 一道无解的方程:中国的胃癌困局 胃癌,是中国的一场"沉默的流行病"。根据国家癌症中心的数据,中国每年新增胃癌患者约35.87万,死亡人数高达26.04万,占全球总数的近 40%。与此相伴的,是"两高一低"的严峻现实:发病率高、死亡率高,以及仅为35.9%的五年生存率。 这道生存率鸿沟,在与邻国日本(60.3%)和韩国(68.9%)的对比中显得尤为刺眼。值得注意的是,这一差距并非源于手术技术或创新药物的代 差,其根本原因在于后者自上世纪80、9 ...
这个AI能救命!提前6个月发现胃癌病灶,突破医学影像认知,达摩院做成了
量子位· 2025-06-25 05:00
衡宇 发自 凹非寺 量子位 | 公众号 QbitAI AI新进展,救命的那种。 现在, 只需做一次体检时常规的CT检查,再用AI分析,就有可能在癌症还没有露出明显症状之前——比如提前半年——把它揪出来 。 今天,国内与之相关的一项成果登上国际顶级期刊《自然·医学》 (Nature Medicine) : 咱们普通人,往往都是闻癌色变。 众多癌症中,胃癌不仅是我国最常见的恶性肿瘤之一,且导致的死亡人数还特别庞大——每年约26万例,在所有恶性肿瘤中居于第三。 不过患上胃癌并不一定等于死神降临。 如果在早期发现并切除,5年生存率可达95%~99%,甚至有完全治愈的可能 。 然而,早期胃癌没有区别于普通胃炎的特异性症状,我国胃癌早期发现率长期徘徊在20%-30%之间。 现在医学界主流的胃癌早筛方法是"问卷+胃镜" ,也就是先填标准化风险评估问卷,然后根据问卷结果,让筛选出的高/中危人群做胃镜筛 查。 全球首个利用平扫CT识别早期胃癌的AI模型DAMO GRAPE 。 它首次突破了传统影像学的限制,实现了用非增强的普通CT识别胃癌的可能性。 实际操作中,DAMO GRAPE在全国20个中心近10万人的大规模临床研究证明 ...
展位有限!第二届全球医疗科技大会招商进行中
思宇MedTech· 2025-06-20 11:17
Core Viewpoint - The article highlights the upcoming Second Global Medical Technology Conference organized by Suyu MedTech, scheduled for July 17, 2025, in Beijing, focusing on "Cutting-edge Technology: From R&D to Clinical Application" [1][6]. Conference Overview - The conference will take place at the Zhongguancun Exhibition Center in Haidian District, Beijing [6]. - The event is expected to attract approximately 500 participants from various sectors, including government, hospitals, leading enterprises, startups, investment institutions, and research institutes [8]. - A significant awards ceremony will showcase and honor global medical technology innovations on the main stage [8]. Key Topics of Discussion - The conference will address several critical topics, including: - AI and intelligent systems [7] - Challenges in the implementation of medical AI and large models [9] - Upgrades in imaging equipment and platforms [10] - Innovations in high-value consumables and interventional techniques [11] - Energy platforms and intraoperative devices [12] - Innovations in materials and structural optimization [13] Roundtable Discussions - A roundtable discussion will focus on how innovative products can effectively enter clinical settings and be utilized [14]. Registration Information - Interested parties can register for the conference by copying the provided link or scanning the QR code [15].
10亿融资!联影加码医疗AI赌局
思宇MedTech· 2025-06-20 06:36
Core Viewpoint - The article discusses the strategic advancements and challenges faced by Shanghai United Imaging Healthcare Co., Ltd. (United Imaging) in the field of AI-driven medical technology, emphasizing its recent A-round financing and the broader implications for the medical AI industry [1][3]. Financing Insights - United Imaging successfully completed an A-round financing of 1 billion RMB, marking a significant investment in AI technology despite the current market's conservative sentiment [1][3]. - This financing reflects a long-term strategic commitment from United Imaging and its investors, recognizing AI capabilities as a core competitive advantage [3]. AI Development Strategy - Founded in late 2017, United Imaging has focused on embedding AI into clinical settings, starting with diagnostic tools and expanding to over 100 products across various medical fields [4][7]. - The company has achieved significant regulatory milestones, including 22 NMPA Class II certifications and 12 Class III certifications, along with FDA and CE approvals for several applications [4]. Business Model Challenges - Despite rapid product development, United Imaging reported revenues of 254 million RMB and a loss of 136 million RMB in 2023, highlighting the ongoing struggle to establish a clear commercial model in the medical AI sector [11][12]. - Key challenges include hospitals' reluctance to pay for software, especially in a cost-controlled environment, and the need for comprehensive solutions that address multiple disease types rather than single-function products [12][13]. Technological Innovations - United Imaging is developing a multi-modal AI platform, uAI, which integrates various data types to enhance clinical workflows and improve diagnostic accuracy [14][17]. - The company aims to create a "smart ecosystem" where AI acts as a self-evolving system, enhancing efficiency across all hospital processes [18][20]. Future Outlook - The medical AI industry is shifting from a focus on model parameters to ecosystem strength, with United Imaging positioning itself as a platform company rather than a tool provider [22][23]. - The company faces high costs associated with continuous model iteration and the integration of AI into clinical settings, but its comprehensive approach may provide a sustainable competitive edge in the long term [24][25].
展位有限!第二届全球医疗科技大会招商进行中
思宇MedTech· 2025-06-19 10:19
Core Viewpoint - The second Global Medical Technology Conference organized by Suyu MedTech will take place on July 17, 2025, in Beijing, focusing on "Cutting-edge Technology: From R&D to Clinical Application" [1][6]. Group 1: Conference Overview - The conference will be held at the Zhongguancun Exhibition Center in Haidian District, Beijing [6]. - The expected attendance is approximately 500 participants, including representatives from government, hospitals, leading enterprises, startups, investment institutions, and research institutes [8]. - The agenda will feature discussions on product innovation, technology implementation, and collaboration between medicine and engineering [6][8]. Group 2: Key Topics of Discussion - The conference will emphasize the challenges of implementing medical AI and large models, including multi-modal data integration and embedding solutions into doctors' workflows [9]. - Topics will also cover advancements in imaging equipment, high-value consumables, energy platforms, and material innovations [10][11][12][13]. - A roundtable discussion will focus on how innovative products can effectively enter clinical settings and be utilized [14]. Group 3: Participation and Opportunities - Companies interested in participating can secure exhibition space, which offers branding exposure and business collaboration opportunities [1]. - Registration methods include a link for online registration and a QR code for easy access [15].