27 de Junio, 2023
16 de Enero, 2024
11:59 PM
Maximising the potential of synthetic data generation in healthcare applications- HORIZON-JU-IHI-2023-05-04
Investigación / Innovación
-Escuela de Medicina y Ciencias de la Salud - Escuela de Ingeniería, Ciencia y Tecnología - Facultad de Ciencias Naturales
Comisión Europea
10 000 000-12 000 000 EUR
Project(s) funded under this topic should aim at Maximizing the potential of synthetic data generation in healthcare applications
ExpectedOutcome:
The proposals should contribute to all of the following expected outcomes:
1,academic and industrial researchers should have access to relevant, robust, and generalisable synthetic data generation methodologies, including open source when relevant, to create and share pools of synthetic patient data in specific use cases;
2,academic and industrial researchers should have access to relevant, high quality synthetic datasets;
3,thanks to better availability of robust synthetic datasets for training data models, healthcare providers and industry should have a wider range of performant AI-based and other data-driven tools to support diagnostics, personalised treatment decision-making and prediction of health outcomes.
Expected Impact:
To exploit the full potential of digitalisation and data exchange in health care, this topic is expected to contribute to the following expected impacts:
•wider availability of interoperable, synthetic data generation methodologies and/or datasets facilitating research and development of integrated products and services that will benefit patients;
•improved insight into real-life behaviour and challenges of patients with complex, chronic diseases and co-morbidities thanks to m-health and e-health technologies;
advanced analytics / artificial intelligence tools supporting health research and innovation resulting in: a) better clinical decision support for increased accuracy of diagnosis and efficacy of treatment; b) faster prototyping and shorter times-to-market of •personalised health interventions; and c) better evidence of the added value from new digital health and AI tools, including reduced risk of bias due to improved methodologies.
Documents: (Check them on the webside).
IHI JU Evaluation form for Research and Innovation Actions (single and two-stage Calls)
IHI JU Proposal template (RIA/SP) - Part B
•Colombia is eligible as a third country, for this it must participate with the role of partner together with a consortium with at least three entities from different European/associated countries).
OBJETIVO/DESCRIPCIÓN
Project(s) funded under this topic should aim at Maximizing the potential of synthetic data generation in healthcare applications
REQUISITOS CLAVE
ExpectedOutcome:
The proposals should contribute to all of the following expected outcomes:
1,academic and industrial researchers should have access to relevant, robust, and generalisable synthetic data generation methodologies, including open source when relevant, to create and share pools of synthetic patient data in specific use cases;
2,academic and industrial researchers should have access to relevant, high quality synthetic datasets;
3,thanks to better availability of robust synthetic datasets for training data models, healthcare providers and industry should have a wider range of performant AI-based and other data-driven tools to support diagnostics, personalised treatment decision-making and prediction of health outcomes.
Expected Impact:
To exploit the full potential of digitalisation and data exchange in health care, this topic is expected to contribute to the following expected impacts:
•wider availability of interoperable, synthetic data generation methodologies and/or datasets facilitating research and development of integrated products and services that will benefit patients;
•improved insight into real-life behaviour and challenges of patients with complex, chronic diseases and co-morbidities thanks to m-health and e-health technologies;
advanced analytics / artificial intelligence tools supporting health research and innovation resulting in: a) better clinical decision support for increased accuracy of diagnosis and efficacy of treatment; b) faster prototyping and shorter times-to-market of •personalised health interventions; and c) better evidence of the added value from new digital health and AI tools, including reduced risk of bias due to improved methodologies.
Documents: (Check them on the webside).
IHI JU Evaluation form for Research and Innovation Actions (single and two-stage Calls)
IHI JU Proposal template (RIA/SP) - Part B
•Colombia is eligible as a third country, for this it must participate with the role of partner together with a consortium with at least three entities from different European/associated countries).