Programme Category
Programme Name
Programme Description
IHI JU is based on the idea that interdisciplinary and cross-sector collaboration will enable perspective and breakthrough innovations in healthcare, including the pharmaceutical industry but also new fields such as biopharmaceuticals, medical technologies and biotechnologies.
Identifier Code
Call
Summary
Healthcare research using individual patient data is often constrained due to restrictions in data access because of privacy, security, intellectual property (IP) and other concerns. Synthetic health data, i.e., data that is artificially created to mimic individual patient data, can reduce these concerns, leading to more rapid development of reliable data-driven methods including diagnostic, precision medicine, decision support and patient monitoring tools. However, while many synthetic data generation (SDG) methods are currently available, it is not always clear which method is best for which use case, and SDG methods for some types of data are still immature. Furthermore, it is still unclear whether highly detailed synthetic data, which are often needed for research, can be categorised as anonymous.
This topic is focused on maximising the potential of synthetic data generation in healthcare applications.
Detailed Call Description
To address these challenges and maximise the opportunity offered by synthetic data, projects funded under this topic should address the following objectives:
- assemble a cross-sectoral public-private consortium including synthetic data experts, public and private data owners, and healthcare solution developers;
- using high-quality public and private datasets, develop / further develop and validate reliable SDG methods for relevant healthcare use cases. The use cases to be explored must be described and justified in the proposal, complement work that is already ongoing, and should:
- ensure the broad applicability of the SDG methods developed and include data types that are not currently adequately addressed, such as device data, image data, genomic data etc;
- include methods to generate: a) fully synthetic datasets that do not contain any real data; b) hybrid datasets composed of a combination of data derived from both real and synthetic data; and c) synthetically-augmented datasets.
- pay particular attention to bias, both in source data and in the SDG methods.
- validate the synthetic data generation methods applied in the project using source data. This should include assessing the risk of re-identification;
- demonstrate the quality and applicability of the synthetic data generated in the project through the development of relevant models;
- encourage the uptake of the results of the project through a strong communication and outreach plan.
Call Total Budget
Financing percentage by EU or other bodies / Level of Subsidy or Loan
Expected IHI contribution per project: ≈ €10 000 000
Thematic Categories
- Education and training
- Health
- Industry
- Research, Technological Development and Innovation
Eligibility for Participation
- Educational Institutions
- Large Enterprises
- Other Beneficiaries
- Private Bodies
- Researchers/Research Centers/Institutions
- Semi-governmental organisations
- State-owned Enterprises
- Training Centres
Eligibility For Participation Notes
For all the above topics applicant consortia should ensure that at least 45% of the action’s eligible costs and costs for action-related additional activities are provided by in-kind contributions to operational activities (IKOP), financial contributions (FC), or in-kind contributions to additional activities (IKAA) from private members and/or contributing partners and the constituent or affiliated entities of the private members and/or of the contributing partners. Contributing partners may not contribute IKAA.
Applicants should propose a project duration such that it matches project activities and expected outcomes and impacts.
Call Opening Date
Call Closing Date
EU Contact Point
All questions regarding JU JU invitations should be directed to infodesk@ihi.europa.eu.