Rhino Health provides a federated compute platform that utilizes federated learning and edge computing to enable secure, privacy-preserving access to healthcare data across multiple institutions. This approach significantly reduces project setup times from months to days while ensuring compliance with data privacy regulations, allowing AI developers to efficiently train models without exposing sensitive information.
Funding
$15M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Healthcare data is often siloed across institutions, making it difficult for AI developers to access diverse datasets needed to train robust and generalizable models. Sharing sensitive patient information across institutions poses significant privacy and compliance risks, hindering collaborative research and AI innovation in healthcare. Traditional methods of data sharing are slow, cumbersome, and require extensive data engineering and IT resources.
Solution
Rhino Health offers a federated computing platform that enables secure, privacy-preserving access to healthcare data across multiple institutions. By integrating federated learning and edge computing, the platform allows AI models to be trained on distributed datasets without exposing raw data or transferring it across institutional boundaries. This approach reduces project setup times from months to days, streamlines data harmonization, and ensures compliance with data privacy regulations such as HIPAA and GDPR. The platform provides AI innovators with an environment for data harmonization, image annotation, exploratory analysis, federated training/inference, and application development.
Target Audience
The primary target audience includes AI developers, data scientists, and researchers in healthcare and life sciences who require access to diverse datasets for training AI models while adhering to strict data privacy and compliance regulations, as well as hospitals and research institutions seeking to collaborate on AI projects without compromising data sovereignty.
Features
- Federated computing platform integrating federated learning and edge computing
- Secure, privacy-preserving access to distributed healthcare data
- Data harmonization tools for aligning disparate data sources
- Image annotation capabilities for enhancing imaging data
- Exploratory analysis tools for uncovering novel insights
- Federated training and inference for building robust AI models
- Application development environment for creating custom applications
- LLM-driven Harmonization Copilot for advanced data harmonization
- Federated Datasets for seamless multi-site analytics
- Compliance with ISO 27001, SOC 2, HIPAA, and GDPR