Core Insights - The 2025 World Artificial Intelligence Conference (WAIC) highlighted AI's transformative role in healthcare, focusing on four key areas: auxiliary diagnosis, clinical practice, model innovation, and drug discovery [1] - Huawei has established a healthcare division to integrate AI technology deeply into the medical sector, exemplified by the launch of the RuiPath pathology model in collaboration with Shanghai Ruijin Hospital [1][2] Group 1: AI in Healthcare - The RuiPath model is China's first clinical-grade multimodal pathology model, covering 90% of the annual incidence of 19 common cancer types in China [1] - RuiPath achieved state-of-the-art (SOTA) performance in 7 out of 14 auxiliary diagnostic tasks across 12 mainstream public datasets, indicating a significant advancement from laboratory to clinical application [1] Group 2: Collaboration and Data Utilization - Since 2021, Huawei and Ruijin Hospital have collaborated on digital pathology, accumulating over 1.03 million digital pathology slices to train the RuiPath model [2] - The ModelEngine toolchain developed by Huawei enables streamlined data engineering and model training, reducing the deployment time for single cancer diagnosis applications from 10 days to 2 days [2][3] Group 3: Market Potential and Strategy - The domestic AI smart diagnosis market is projected to reach nearly 20 billion yuan from 2025 to 2029 for B-end and G-end products, with the C-end market potentially exceeding 70 billion yuan annually [3] - Huawei's "military-style" approach has led to deep collaborations with 62 leading hospitals, focusing on high-level AI medical products based on clinical data and expert knowledge [4] Group 4: Innovative Applications - The ChatZOC ophthalmology model, developed in partnership with Sun Yat-sen University, has provided eye disease screening services to over 3,000 patients in remote areas, addressing the shortage of ophthalmic resources [4] - The "Dingbei Health" proactive health model, launched with Guangdong Second People's Hospital, uses health check data to predict chronic disease risks, shifting health management from passive treatment to proactive intervention [5]

Core Insights - The article emphasizes the critical role of data storage capacity, referred to as "存力" (storage power), in the context of the exponential growth of data driven by advancements in artificial intelligence (AI) technology [1][8] - It highlights the dual importance of computing power ("算力") and storage power as essential components for unlocking the value of data and fostering new infrastructure development [1][8] Industry Overview - The global data volume is expected to grow at an annual rate of 36%, reaching Yottabyte (YB) scale by 2030, necessitating efficient and secure data storage solutions [1] - The Chinese government has recognized the significance of storage power, with initiatives like the "算力基础设施高质量发展行动计划" aimed at enhancing the quality of computing and storage infrastructure [1][8] Company Insights - Guangzhou Huayin Kang Medical Group (华银康集团) is a leading independent medical testing and diagnostic service provider in China, focusing on pathology services [2] - The company has developed a comprehensive "AI + system + equipment + resources" service model, integrating advanced AI technologies to enhance diagnostic capabilities [5][7] Challenges and Solutions - Huayin Kang faces significant challenges in data storage, with an annual testing volume of approximately 40 million samples, each pathology image requiring nearly 1GB of storage [7] - The company has implemented a distributed storage system and advanced compression techniques to manage storage demands, although it still requires annual capacity expansions in the Petabyte (PB) range [7][8] Future Trends - The article discusses the evolving landscape of data storage, where traditional storage methods are becoming inadequate due to the increasing complexity and volume of data generated by AI applications [9][11] - It suggests that the establishment of storage centers will be crucial for integrating data across various sectors, enhancing data governance, and facilitating the development of new industries and job roles [12][15]