Samsung & Galeon Partner To Advance Women’s Health With Blockchain AI

Samsung’s ultrasound imaging will integrate into hospital workflows through this decentralized system, providing real-time structured imaging for women’s health

Samsung has announced a partnership with Web3 healthcare startup Galeon to integrate its AI-enabled ultrasound devices with Galeon’s decentralized electronic health record (EHR) system, which is already deployed in 18 hospitals across Europe. The move aims to enhance women’s health diagnostics while maintaining strong safeguards for patient data.

Under the collaboration, imaging data from Samsung devices will be anonymized and encrypted before use in training AI models. Hospitals will retain full control of their data, with only algorithm operations recorded on the blockchain for traceability. This ensures that sensitive patient records remain off chain and protected.

The system relies on Galeon’s Blockchain Swarm Learning technology, which allows hospitals to train AI models without centralizing data. Samsung’s ultrasound imaging will integrate directly into hospital workflows through this decentralized approach, enabling real-time structured imaging for women’s health applications.

Beyond diagnostics, the partnership includes a community-driven research element. Galeon’s Atlantis platform and GALEON token enable more than 100,000 community members to share anonymized information and vote on which medical research projects should receive funding, creating a more democratic approach to healthcare innovation.

Benjamin Tarter, Head of Women’s Health at Samsung France, said, “We are incredibly proud of this partnership and ready to disrupt the health market together.”

Loïc Brotons, CEO at Galeon, said, “Samsung’s device leadership meets our in-production EHR and decentralized AI stack to provide what the industry has been missing. Starting in women’s health, we are connecting AI imaging, real-time hospital workflows, and community-governed research to accelerate discoveries while protecting privacy.”

The collaboration highlights a convergence of AI-driven diagnostics, patient privacy, and decentralized systems in healthcare. If widely adopted, it could accelerate early detection of conditions, improve research funding transparency, and provide hospitals with greater control over sensitive data.

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