Rhino Health

Rhino Health operates at the intersection of healthcare and artificial intelligence, providing a platform built on the principle of federated computing. The company enables medical researchers, academic medical centers, and developers of AI-based medical technologies to collaborate and train AI models without the need to move or centralize sensitive patient data. This approach directly addresses the critical challenges of patient privacy, data security, and regulatory compliance that often hinder the development of effective AI solutions in healthcare. The business model centers on providing this secure, distributed computation platform, allowing users to access and work with diverse datasets that remain within their original institutional firewalls.

The company was co-founded by Dr. Ittai Dayan, who serves as CEO, and Yuval Baror, the CTO. Dr. Dayan's journey to establishing Rhino Health is rooted in his multifaceted career as a physician, a consultant for Boston Consulting Group's healthcare practice, and the former head of the Center for Clinical Data Science at Mass General Brigham. A pivotal experience for him was co-leading the EXAM study, a large-scale federated learning project with NVIDIA to predict COVID-19 progression, which highlighted the immense difficulty of using siloed data for AI development. This firsthand understanding of the data access bottleneck in healthcare AI was the catalyst for creating Rhino Health, aiming to unlock data collaboration securely.

The Rhino Health Platform utilizes federated learning, allowing an AI developer's model to be applied to patient data where it resides. It provides an end-to-end solution that supports the full lifecycle of AI development, from data pre-processing and harmonization to model training, validation, and analysis, all performed centrally without any data ever leaving the source institution. This method allows AI models to continuously improve by learning from a wide array of patient data across different geographies and demographics, which enhances their accuracy and generalizability. The platform is being applied across various medical fields, including oncology, radiology, and neurology, to predict disease outcomes, improve diagnostics, and advance personalized medicine. Since its inception, the company has secured significant funding, including a $5 million seed round in February 2021 and a Series A of $15 million announced in May 2025, to expand its operations and platform capabilities.

Keywords: federated learning, healthcare AI, medical data collaboration, patient privacy, distributed computing, clinical data science, AI model training, real-world data, secure data sharing, medical research platform, edge computing, AI diagnostics, personalized medicine, data harmonization, clinical decision support, health-tech, data security, AI validation, regulatory compliance, multi-site collaboration, medical imaging analysis