Business Challenges

  1. Medical Resource Pressure: Staff shortage and heavy clinical documentation leading to low efficiency.
  2. Insufficient Data Processing: Healthcare institutions generate massive data but lack effective analysis and insight capabilities.
  3. Diagnostic Accuracy and Speed Requirements: Increasing GAI and AI training/inference demands require high-performance infrastructure to support comprehensive medical services.
  4. Cross-system Integration: Database processing protection, privacy security at endpoints, and lack of unified AI infrastructure resource scheduling platform.
  5. Resource and Cost Constraints: Medical AI applications require high-performance hardware but face deployment challenges due to high costs and limited resources.
  6. Telemedicine Implementation Difficulties: Real-time medical services lack effective diagnostic tools beyond video consultations.

Solution Overview

Smart Healthcare GAI + AI solutions are designed to provide end-to-end support, from data collection and analysis to application. By leveraging powerful AI models (such as deep learning and generative AI), these solutions can process medical imaging, and genetic data, and assist in clinical decision-making. In recent years, breakthroughs in smart healthcare development have been remarkable, such as using AI to rapidly analyze CT and MRI images to improve diagnostic accuracy. AlphaFold, for example, utilizes genetic data to predict protein structures, offering critical support for disease research and drug development. By integrating the unique features of the ‘Infinite AI-Stack’ one-stop AI infrastructure management platform, AI-powered smart healthcare applications can accelerate their implementation:

  1. Data Privacy and Security: Tenant data is isolated and encrypted, ensuring confidentiality through HTTPS encryption for data transmission. Role-Based Access Control (RBAC) restricts data access, and all development data is stored securely using AES-256 encryption. Physical and logical isolation is implemented between development and production environments, with containerization technologies like Docker ensuring comprehensive data security and system stability.
  2. Efficient GPU Resource Management: With AI-Stack’s GPU slicing and aggregation technology, a single GPU can support multiple tasks simultaneously, significantly increasing resource utilization from 30% to 90%.
  3. Distributed High-Performance Computing: Supports distributed training by distributing training data across multiple nodes for parallel processing, reducing AI model training time.
  4. Rapid Deployment and Management: Provides a developer-friendly no-code interface, reducing the time to set up development environments from weeks to just one minute.
  5. Multi-Cloud and Edge Support: Integrates multi-cloud platform management and supports AI application scenarios from cloud to edge, making it especially suitable for remote healthcare and real-time diagnostics.
  6. Auditing and Compliance: Regularly undergoes third-party compliance assessments and penetration testing to ensure security and regulatory adherence.

Solution Value

  1. Enhancing Diagnostic Efficiency and Accuracy:
    • Leverage AI-Stack’s high-performance computing platform to enable real-time medical image analysis, improving diagnostic accuracy.
    • Support the computational needs of large-scale deep learning applications like AlphaFold for gene analysis, aiding personalized drug development.
  2. Reducing Resource Waste and Costs:
    • GPU slicing technology enables more efficient multi-tasking, saving compute costs and boosting resource utilization by over 10 times.
    • Improve computational efficiency by 90%, significantly lowering hardware and energy costs.
  3. Accelerating AI Application Deployment:
    • Rapidly configure AI models and application environments, shortening the time to AI adoption in healthcare institutions.
    • Provide MLOps functionality, offering full support from model training to inference, speeding up AI deployment in medical scenarios.
  4. Efficient Collaboration and Unified Management:
    • Integrate data centers and multi-cloud platforms, enabling seamless collaboration and sharing of medical data.
    • Optimize operator management efficiency, including resource allocation, health monitoring, and billing analysis.

Solution Architecture

LayerDescriptionExamples
ApplicationDeploy applications in telemedicine, medical image diagnostics, genetic analysis, personalized medicine, and data flows.Virtual assistants, pathology diagnostics, medical search, medical imaging, drug development, medical robots, intelligent health management, telemedicine
TechnicalAI-Stack platform automates model training scheduling and application deployment.Speech or semantic recognition, image recognition, human-computer interaction, medical big data, deep learning
DevelopmentAI-Stack platform provides developers with development tools and AI ecosystem resources to streamline software development collaboration, and is compatible with various images and development container environments.Open source software, large language models (LLM), AI ecosystem
InfrastructureAI-Stack platform offers GPU management control and compute power scheduling, along with HPC distributed computing scheduling technology, supporting multi-node operations for efficient training; it also provides resource monitoring, access control, and other features.In-hospital systems, smart medical devices, medical databases, on-premise servers, AI chips, GPUs