The daily volume of imaging data such as CT, MRI, and pathology slides is growing exponentially. Traditional general-purpose computing cannot handle the millisecond-level inference demands of deep learning models, resulting in high latency for AI-assisted diagnosis and preventing seamless integration into real-time clinical workflows.
SOLUTION INTRODUCTION
Solution introduction
Provides end-to-end computing power support—from massive data storage and high-concurrency inference to large-scale model training—for medical imaging-assisted diagnosis, clinical decision support, genomics analysis, and healthcare big data governance. It drives precision diagnosis and treatment, intelligent hospital operations, and accelerated clinical research.HIS, PACS, EMR, and other core systems are isolated and use inconsistent standards, making it difficult to aggregate full-dimensional patient data. The lack of a powerful data fusion engine and an AI annotation and analysis platform hinders the construction of disease-specific databases and real-world studies.
Virtual drug screening, protein structure prediction, and genome-wide association analysis require parallel computing at the scale of thousands of accelerators. Fragmented and outdated GPU infrastructure leads to model training cycles lasting weeks, seriously blocking the rapid translation of cutting-edge research into clinical diagnosis and treatment.
SOLUTION ADVANTAGES
Solution advantages
The following points are replaceable demonstration content based on the current solution structure.Ultimate AI performance for instant response in diagnosis and research
AI computing servers equipped with the latest GPUs/NPUs are deeply optimized for models such as Transformers and CNNs. Imaging inference latency is reduced to milliseconds, and whole-genome analysis is shortened from tens of hours to just hours, making AI capabilities instantly available while significantly improving model training efficiency.
Deep software-hardware synergy for out-of-the-box deployment and high availability
Factory pre-integrated with a medical-application-ready AI development environment and computing platform, fully tuned and stress-tested. Supports redundant power supplies and fault self-healing design to meet 24/7 high availability requirements of up to 99.999% for healthcare environments, significantly reducing overall O&M complexity and TCO.
Open ecosystem safeguards data security and drives compliant innovation
Fully compatible with mainstream deep learning frameworks and healthcare ISV applications, with built-in hardware-level data encryption and TEE trusted execution environments. Supports federated learning and secure multi-party computation to ensure data is usable but not visible, meeting healthcare data security and privacy regulations across the entire chain and fully unleashing the value of data assets.
SOLUTION ARCHITECTURE
Solution architecture
This staged architecture visualizes the current planning path and is not a delivery commitment.- 01
Heterogeneous computing foundation: deploy a highly efficient hybrid computing cluster
Use high-density general-purpose servers for core production systems such as HIS/EMR, introduce AI computing servers to build elastic inference and training resource pools, and pair them with distributed storage servers to create a unified imaging and medical record data lake. Achieve storage-compute separation and on-demand scaling through a high-speed lossless network.
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Intelligent data governance: build a high-quality, standardized healthcare data foundation
Use AI-powered automatic de-identification and natural language processing to perform deep quality control, standardized mapping, and enterprise master patient index (EMPI) construction on multi-source heterogeneous data. Break down information silos and accumulate compliant, usable research-grade data assets to provide high-quality input for AI models.
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AI capability platform: deliver agile full-lifecycle model services
Build a healthcare AI platform to manage model assets such as imaging segmentation and detection and knowledge graph construction. Provide a visual drag-and-drop training platform and a model marketplace, enabling clinical teams to fine-tune specialty models with zero coding and achieve agile integrated delivery of AI capabilities from development and validation to deployment.
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Deep scenario integration: embed AI into core clinical and research workflows
Inject AI-assisted diagnosis capabilities into PACS and outpatient physician workstations to achieve second-level detection of pulmonary nodules, fractures, and other conditions, along with structured report generation. Build a high-throughput bioinformatics analysis platform to accelerate gene alignment workflows. Create a research big data analytics portal to drive independent clinical innovation.
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