A privacy-preserving solution for using AI in cardiovascular care

The Secur-e-Health project developed a concept that enables AI to be used in the treatment of cardiovascular diseases without compromising the protection of sensitive patient data.

“The study shows that AI tools can be built for healthcare in a way that respects patients’ privacy at every stage of care,” says Mika Hilvo, Research Team Leader at VTT and national coordinator of the project. “Our work brings together the secure use of data, clinical needs and modern AI methods in a way that can support better care in the future.”

“This new approach can help strengthen trust between patients, data controllers, healthcare providers and researchers. The study shows that sensitive health data can be used collaboratively, securely and in a privacy-preserving way without organisations losing control of their data,” says Gaurang Sharma, the lead author of the publication and Research Scientist at VTT.

The project created a privacy-preserving architecture for patient care. It combines secure data processing, careful consent practices, and privacy-preserving AI tools. This can support both early risk assessment and the monitoring of care for patients with cardiovascular diseases. The solution covers the entire care pathway, from the prevention of serious heart problems (primary prevention) to supporting the care of diagnosed patients (secondary prevention).

In the prevention-focused part of the work, the research team tested methods for training AI models using health data stored in different locations, without the data needing to be transferred to a single centralised location. This enables collaboration between organisations while helping to keep patient data better protected. The results showed that models trained using privacy-preserving federated learning can perform as well as models trained using traditional ma
chine learning methods.

For patients requiring continuous care, the researchers created a secure process for obtaining treatment consent, collecting ECG monitoring data, combining data from different systems, and reviewing patient information to support clinicians. The system was designed so that patient data can be combined securely without revealing identity information more widely than necessary.

The solution addresses one of the key challenges in modern healthcare: how to make use of AI when medical data is distributed across several different systems and must be handled with great care. By combining secure data use, patient consent and AI-based support tools, the researchers created a foundation that can help develop the digital health services of the future.

The three-year Secur-e-Health research project ended at the end of 2025 and involved researchers from five countries. VTT coordinated the Finnish research consortium, which included Bittium, CSIT Finland, Mediconsult, Nordic Healthcare Group, Solita and Success Clinic. In Finland, the project was funded by Business Finland.

Juha Pajula, Senior Scientist from VTT, will present the publication on the solution, “End-to-End Architecture for Secure Cardiovascular Disease Risk Assessment and Clinical Care”) at the Nordic Conference on Digital Health and Wireless Solutions in Oulu on Tuesday, 16 June 2026. A scientific article will be published in July 2026 (Conference proceedings: Digital Health and Wireless Solutions Integrating AI, LLMs and Multimodal Health Data for Next-Generation Decision Support).