Revolutionising biomedical data sharing and AI-driven healthcare solutions: SEARCH launches

Today sees the launch of the Synthetic hEalthcare dAta goveRnanCe Hub (SEARCH); a multi-disciplinary initiative focused on creating synthetic healthcare data and facilitating secure data sharing across the biomedical ecosystem. The project states in a press release that “the launch marks a significant leap in healthcare research, driving advancements in digital health and AI-powered diagnostics through cutting-edge synthetic data generation and federated learning approaches”.

SEARCH will address critical challenges in healthcare data access by creating a platform that enables secure, privacy-preserving searching, sharing and analysis of multimodal healthcare data. Co-ordinated by Trinity College Dublin, – via the Trinity Translational Medicine Institute (TTMI), based at St James’s Hospital – this initiative brings together a consortium of 26 cross-sectoral partners across Europe, including TIC Salut Social Foundation, synthetic data experts, healthcare providers, and solution developers, to unlock new opportunities in data-driven healthcare innovation. Funded under the Innovative Health Initiative Joint Undertaking (IHI JU), SEARCH boasts an initial budget of over €15.2 million.

SEARCH aims to accelerate healthcare innovation by generating FAIRified synthetic data for use in AI/ML models, enabling large-scale data collaborations while preserving privacy and compliance with regulatory standards.

SEARCH will deliver reliable methodologies for synthetic data generation (SDG) that meet the highest standards of accuracy and applicability, significantly increasing the availability of interoperable datasets. These datasets will be used to develop AI-based tools that support diagnostics, personalised treatment, and predictive health outcomes, improving patient care while reducing privacy risks.

Combining data clean rooms and federated learning allows multiple institutions to collaborate, drawing insights from decentralised data sources, while ensuring that patient data remains securely stored at its source. SEARCH’s innovative synthetic data generation techniques will not only democratise access to healthcare data but will also create a foundation for new diagnostic and therapeutic tools powered by AI/ML.

Key Objectives and Innovations

• Next-Generation Synthetic Data: SEARCH leverages deep generative models to create realistic synthetic replicas of healthcare data (EHRs, genomics, medical signals, and radiological imaging), replicating the performance of real-world data while maintaining privacy.

• Federated Learning and Data Clean Rooms for Privacy & Scale: By keeping patient data securely in its original location, SEARCH’s privacy- preserving framework framework enhances collaboration across healthcare sectors while protecting sensitive information. This fosters AI model development and the wider adoption of new healthcare tools.

• Accelerating AI Innovation: SEARCH will enable the development of cutting-edge AI-powered decision-support tools by providing gold-standard synthetic datasets for benchmarking biomedical AI solutions, fuelling faster diagnostic tools, and creating new personalised healthcare approaches.

Groundbreaking Impact

According to the project’s leaders, “SEARCH will play a pivotal role in revolutionising healthcare, contributing to faster innovation, shorter time-to-market for new digital health interventions, and better personalised treatments. Through synthetic data methodologies, SEARCH will uncover insights into cardiovascular, gastrointestinal, and gynaecological diseases, while ensuring the protection of patient privacy”.

SEARCH’s efforts will complement real-world healthcare by validating synthetic datasets through clinical studies. This unique combination of synthetic data and privacy-preserving architecture has the potential to drive AI/ML innovations that enhance patient outcomes, improve diagnostics, and pave the way for new public-private collaborations in healthcare. This project is supported by the Innovative Health Initiative Joint Undertaking (IHI JU) under grant agreement No. 101172997. The JU receives support from the European Union’s Horizon Europe research and innovation programme and COCIR, EFPIA (including Vaccines Europe), EuropaBio and MedTech Europe.