Federated Compute represents the natural evolution of the legacy federated data / analytics paradigm, which in itself is a step up from the outdated worldview of centralizing data. Said simply: we let AI developers work with data without ever taking possession of the data.
We offer an alternative approach to sourcing data versus the traditional paradigm of centralization: accessing the data in real time but in such a way that data remain behind data custodian firewalls to provide maximum security and maximum protection of patient privacy, which in turn actually increases the volume and complexity of data available for analysis. We do this using a combination of edge computing and federated learning technology - techniques that allow for centralized orchestration of data pre-processing, exploratory analysis, visualization, querying, and ultimately training & monitoring the efficacy of AI algorithms.
Rhino Health co-founder & CEO, Ittai Dayan led the EXAM study, which demonstrated the benefit of federated learning for scaling data between institutions and rapidly deploying new AI innovation. This approach could solve for the data shortage in AI development, and increase usability and transparency in the way that AI is deployed. It highlighted the need for a trusted, industry-grade platform to deliver the federated learning technology to developers and users around the world. To form Rhino Health, Ittai teamed up with industry-veteran Yuval Baror, who has a track record of bringing analytics and AI solutions to enterprise scale.
Rhino Health has already installed the Rhino Health Platform (RHP) at >20 leading hospitals around the world and partnered with the American College of Radiology to enable their distributed computing & AI agenda. Innovators at these institutions and partners in industry are using the RHP for manifold applications - from diagnostic algorithm training to clinical trial recruitment to cross-hospital outcomes monitoring. These use cases illustrate the viability of our approach to data collaborations as we have passed rigorous information security reviews at each participating site, and helped unlock or streamline collaborations that would have previously been impossible.
Key features of the Rhino Health Platform include:
- Data validation 'suite' - schema, inferred schema, schema validation, data validation using the schema, cohort analytics
- Generalized Compute - the ability to containerize and then safely run any code on data behind a firewall
- Secure Access - true zero footprint viewer, with built-in image and text annotation
- Analytics Toolbox - perform exploratory data analysis and visualize the results with our built in tools
- User management - flexible privacy and permissions policies
- API/SDK access to the Platform
- NVFLARE integration for federated learning
- Ability to support recyclable ETLs
- DICOMWeb query
About Digital Health Innovation Summit 2023
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