Empowering Patient-Centered Care with Synthetic Data

SYNTHIA’s commitment to patients is based on the development of synthetic data-driven analytic tools, with the goal of advancing new and more personalised treatments. By actively engaging patients, caregivers, and patient advocates, SYNTHIA aims to ensure that innovations in synthetic data are tailored to address patient needs, foster trust, and enhance the fairness and effectiveness of healthcare. The resulting solutions are meant to benefit patients directly, while build a more inclusive and effective healthcare landscape. 

Advancing Patient Care with Synthetic Data Innovation in Healthcare 

Synthetic data refers to artificially generated data that mimics the statistical properties and structure of real-world data without exposing any individual’s personal information. In healthcare, synthetic data enables the creation of datasets that resemble clinical, genomic, imaging, or behavioural information, without the risk of patient re-identification. These datasets are produced using advanced statistical modelling, machine learning, or generative algorithms that learn from real-world data to produce new, anonymized versions. Synthetic data offers a transformative solution to the challenge of accessing high-quality, representative datasets for AI model development and validation in healthcare. It supports data privacy by eliminating direct links to real individuals, thus facilitating data sharing and collaborative research across institutions and borders. By providing balanced and scalable datasets, synthetic data reduces biases, enhances model generalizability, and supports regulatory-compliant innovation. 

SYNTHIA is a European project - funded by the Innovative Health Initiative (IHI) - that brings together 39 academic and industry partners from medicine, technology, law, policy, and industry to explore how synthetic data can safely and effectively be used in healthcare. Launched in September 2024, SYNTHIA is the first IHI project dedicated to synthetic data. The 5-year project focuses on six major diseases to show how synthetic data can support personalized medicine. 

SYNTHIA's disease focus

SYNTHIA's goals

• Build tools and methods for generating high-quality synthetic data across different types (e.g. lab results, genomics, imaging). 
• Create clear rules and standards for ensuring data privacy, quality, and fairness. 
• Validate the usefulness of synthetic data in real clinical and research settings. 
• Share results and resources with the wider community through an open platform. 
• Ultimately, SYNTHIA aims to speed up research, improve patient care, and build trust in the responsible use of synthetic data. 

Expected impact for patients 

While synthetic data offers numerous benefits, it is essential to address concerns about its use in healthcare. SYNTHIA is committed to perform research with patient involvement. Therefore, working with patient representatives throughout the project SYNTHIA ensured to include the patients experience, needs, and concerns up from the very beginning.  

Patients often face barriers in participating in clinical research, such as time and privacy concerns. Synthetic data can help reduce these barriers by providing a safe and efficient way to participate in research without compromising personal information. This approach can lead to more inclusive and diverse clinical trials, ultimately benefiting all patients. These benefits are not only applicable to clinical trials, as Synthetic data can also support HCP with an accurate and in-time diagnosis, predictions and selection of most suitable therapies for the patients.  

Through the six clinical use cases as show above, SYNTHIA works with academic, industry, and patient partners to develop and test how synthetic datasets could improve diagnosis and monitoring, personalise care and treatment planning, to reduce the burden of trial participation, and enable safe and privacy-respecting AI development.

Examples how synthetic data can transform patient care

• Lung Cancer: Synthetic PET/CT imaging helps predict treatment response without requiring all patient images to be shared, protecting privacy while advancing cancer care. 
• Breast Cancer: 3D synthetic breast anatomy models improve detection and optimise devices, reducing unnecessary biopsies and supporting safer innovation. 
• Multiple Myeloma: Synthetic “control arms” make trials more inclusive for patients with rare profiles, reducing delays in accessing new treatments. 
• Diffuse Large B-Cell Lymphoma: Synthetic data supports personalised relapse prediction, helping guide treatment choices and follow-up care. 
• Alzheimer’s Disease: Synthetic patient populations allow research into early detection and treatment strategies without placing extra demands on vulnerable patients and families. 
• Type 2 Diabetes: Synthetic wearable data helps develop digital biomarkers and tools for real-time risk prediction, supporting better day-to-day management of the condition.