AI-Driven
Medical Innovation
Specializing in AI medical software development and medical AI research services — delivering one-stop intelligent solutions from algorithm R&D to clinical application for medical institutions and research teams
Our Clients
Long-term partnerships with leading universities, research institutes, and medical institutions
Inner Mongolia Medical University
Affiliated Hospital of Inner Mongolia Medical University
Second Affiliated Hospital of Inner Mongolia Medical University
Baotou Central Hospital
Ordos Central Hospital
China Medical University
First Affiliated Hospital of China Medical University
Shengjing Hospital of China Medical University
Northeastern University
Institute of Metal Research, CAS
Chinese Research Academy of Environmental Sciences
Second Affiliated Hospital of Dalian Medical University
Southern Medical University
China Pharmaceutical University
Core Business
AI Medical Software Development + Medical AI Research Services
Intelligent Diagnosis Systems
Build AI-assisted diagnosis, treatment recommendation, and clinical pathway management systems based on medical large models to improve diagnostic efficiency and quality
Medical AI Research Services
Provide medical data analysis, AI modeling, publication support, and intelligent clinical trial services to accelerate medical research output
Medical Imaging AI
Develop medical imaging diagnostic systems covering CT, MRI, pathology and other multimodal imaging analysis and structured report generation
Medical Data Governance
Build disease-specific medical databases and knowledge graphs for cleaning, fusion, and intelligent governance of multi-source heterogeneous medical data
Private Secure Deployment
Support on-premises and private-cloud deployment, compliant with medical data security standards, keeping data within the hospital
Continuous Technical Support
Provide model iteration, performance monitoring, knowledge base updates, and 24/7 technical support
Cooperation Process
A standardized service process, fully controlled from requirements to delivery
Requirements Analysis
Deeply understand clinical or research scenarios and define technical goals and evaluation metrics
Solution Design
Select suitable AI models and architecture, and develop a detailed implementation plan
Data Governance
Clean, annotate, and structure medical data to build high-quality training datasets
R&D Delivery
Model development and training, system integration, performance validation, and joint testing
Deployment & Launch
Private deployment, security audit, user training, and phased rollout
Ongoing Service
Performance monitoring, user-feedback iteration, model upgrades, and technical support
Representative Cases
Validated in multiple medical scenarios
Intelligent Diagnosis System for Tertiary Hospitals
Developed an AI-assisted diagnosis system for a provincial tertiary hospital covering cardiology, neurology, and respiratory medicine, with over 95% diagnostic accuracy
Medical Imaging AI Platform
Built a multimodal imaging AI analysis platform supporting CT, MRI, and X-ray screening, improving report generation efficiency by 70%
AI Data Platform for Drug R&D
Built a target-discovery and drug-screening data analysis platform for a pharma R&D team, boosting knowledge retrieval efficiency 10x
Service Model Comparison
Choose the cooperation model that fits your needs
| Dimension | Project-based | Long-term Partner |
|---|---|---|
| Best for | A single well-defined development need | Multi-scenario, ongoing research cooperation |
| Delivery cycle | 4-12 weeks rapid delivery | Quarterly planning, continuous delivery |
| Data governance | Cleaning and annotation during the project | Continuous governance and knowledge updates |
| Technical support | 3 months free maintenance | Year-round 24/7 support |
| Pricing model | Paid per project milestone | Annual service fee with shared outcomes |
FAQ
Depending on complexity, standard development cycles are 4-12 weeks. Simple single-function modules can be delivered within 3 weeks, while large integrated systems are delivered in milestones.
Fully. We use on-premises or private-cloud deployment to keep medical data within the hospital and meet data-compliance requirements.
For AI model training, de-identified clinical, imaging, or research data is required. Our team assists with data cleaning and annotation.
Yes. We provide standard APIs and data interfaces that integrate seamlessly with HIS, EMR, PACS, LIS, and other mainstream systems.
We offer project-based or long-term research-partner models, supporting joint grant applications, data analysis, and publication, with flexible outcome-sharing.
We provide flexible O&M plans including on-demand support, quarterly inspections, and annual upgrades, with continuous model monitoring and performance reports.
It depends on the scenario and data quality. In mature scenarios such as imaging-assisted diagnosis and record QC, accuracy reaches 90%-98%, close to senior physicians. AI remains a decision-support tool, with final diagnosis confirmed by physicians.