overview
Challenges
- Complexities in integrating diverse AI applications with existing imaging systems
- Inconsistent workflows due to proprietary AI integration methods
- Limited scalability and adaptability of current AI solutions
- High overhead costs of individual AI integrations with PACS and RIS
- Difficulty in accessing a wide range of FDA-approved AI solutions
Objectives
Create a vendor-neutral platform for seamless AI integration
Standardize AI/ML workflows across radiology departments
Enhance clinical decision-making through streamlined access to AI tools
Improve scalability, adaptability, and cost-efficiency
Solution Approach
- Cloud-Based AI Integration: Enabled “bring your own AI” capabilities, allowing seamless onboarding of third-party AI apps.
- Standardized Protocol Support: Supported DIMSE, DICOM Web, WADO, DICOM Proxies, and non-DICOM AI applications.
- Scalable Architecture: Utilized microservice-based design hosted on Kubernetes for flexible and self-hosting capabilities.
- Subscription Module: Allowed radiologists to subscribe to AI applications specific to their modality.
- Extensive App Ecosystem: Onboarded 50+ AI applications on the cloud platform.
- Cloud-Native Services: Integrated service mesh, edge device support, observability, certificate management, and licensing.
- Multi-Tenant Cloud Environment: Provided medical inferencing and insights to 20+ hospitals and labs.
Results & Impact
Reduced Integration Overhead: Minimized costs and complexity of individual AI integrations with PACS and RIS.
Standardized Workflows: Streamlined AI/ML processes for consistent insights, reporting, and worklist prioritization.
Expanded Access to AI Solutions: Faster access to a broader range of FDA-approved AI tools.
Enhanced Clinical Decision-Making: Improved radiologist efficiency and diagnostic accuracy through seamless AI integration.
Revenue Growth Opportunities: Facilitated new revenue streams through integration with external PACS systems.