Stakeholders

SYNTHIA engages a diverse and multidisciplinary group of stakeholders to fulfil its mission of leveraging synthetic data to advance personalised medicine. By bringing together expertise from healthcare, research, industry, patient communities, policy, and related European and global initiatives, the project fosters collaboration across the entire health innovation ecosystem. This broad engagement ensures that SYNTHIA’s technologies are scientifically robust, ethically grounded, and aligned with real-world needs and regulatory frameworks.

Below is an overview of SYNTHIA’s key stakeholders and target audiences.

I. Clinicians and physicians

Healthcare professionals, including physicians and clinicians at academic institutions, involved in research, innovation, and the practical application of personalized medicine.

II. Researchers and Data Scientists

Experts and institutions focused on data analysis, artificial intelligence (AI), machine learning (ML), and advanced biomedical research.

III. Industry

Pharmaceutical Companies: Professionals involved in drug discovery, development, and clinical trials.

Medical Device Manufacturers: Engineers, designers, and developers of medical technologies.

Start-ups and companies contributing cutting-edge solutions to synthetic data and healthcare innovation.

IV. Patient advocacy groups & patients

Organizations and individuals representing patient interests, ensuring inclusivity and ethical considerations in synthetic data applications.

V. Regulators and Policymakers

Government bodies, regulatory authorities, health technology assessment (HTA) organizations, and policymakers responsible for establishing guidelines, policies, and standards related to synthetic data.

VI. Associations and research groups

Medical associations, industry associations, scientific and technology research and working groups.

VII. Related IHI and Horizon Europe projects as well as related global initiatives

IHI and EU funded projects – currently running and potentially new - focussed on big data, RWD, RWE, AI, machine learning and SD.