Samsung has partnered with Web3 healthcare startup Galeon to integrate AI-powered ultrasound devices with Galeon’s decentralized electronic medical record system, focusing on improving women’s health diagnostics while preserving patient privacy. The collaboration connects Samsung’s ultrasound technology to Galeon’s blockchain-based EHR platform currently deployed across 18 European hospitals.
The partnership utilizes Galeon’s Blockchain Swarm Learning technology to enable decentralized AI training on anonymized, encrypted datasets without centralizing sensitive patient records. Only AI algorithm operations are recorded on the blockchain for traceability, while actual imaging data remains off-chain and anonymized.
Galeon will provide a specialized connector for every Samsung ultrasound machine installed in hospitals, ensuring secure data transfer from hardware to software systems. This addresses a significant integration challenge in healthcare, where diagnostic equipment often fails to communicate effectively with patient tracking systems.
“We are incredibly proud of this partnership and ready to disrupt the health market together,” said Benjamin Tarter, Head of Women’s Health at Samsung France.
The system aims to improve obstetric care by digitally storing ultrasound data directly in patient records, eliminating the current practice where exam results are typically provided only as printed reports and images. This integration allows clinicians to access precise fetal measurements, morphological observations, and biometrics from previous exams during follow-up appointments.
“Samsung’s device leadership meets our in production EHR and decentralized AI stack to provide what the industry has been missing,” said Loïc Brotons, CEO of Galeon. “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 partnership includes a research component through Galeon’s Atlantis platform and GALEON token, which enables a community of over 100,000 members to vote on medical research funding priorities. This approach aims to make research funding more democratic and aligned with patient needs rather than purely commercial interests.
The collaboration addresses growing demands for AI-supported diagnostics while maintaining strict patient privacy standards. The decentralized architecture allows hospitals to retain full control of their data while benefiting from improved diagnostic capabilities and streamlined workflows.
