Project description
Improving AI integration into clinical practice
The diagnostic process for complex diseases involves many tests, including medical imaging. AI and machine learning may help expedite diagnosis but require trustworthy solutions designed especially for healthcare and well connected with clinical practice. The EU-funded BioMedAI TWINNING project will set up a training scheme dedicated to the processing of sensitive images and clinical data. Partners will schedule workshops and virtual trainings for AI researchers towards developing explainable AI-based methods. The project is expected to improve the facilitation of AI technology into clinical practice with obvious benefits for the wellbeing of patients.
Objective
Increasing demand for sophisticated clinical diagnostics makes current diagnostic capacities insufficient. A potential solution lies in semi-automatic systems speeding up the diagnosis process. Artificial intelligence (AI) and machine learning seem to be very promising approaches to the automation of diagnostic systems. However, most academic AI systems are opaque black boxes that cannot be easily understood, tested and certified. Also, academic AI solutions are often hard to reproduce, and their evaluation is insufficiently connected with clinical practice. This motivates MU and MMCI to team with two advanced partners (AP), MUG and TUB, and establish a BioMedAI infrastructure allowing close cooperation of computer science and clinical experts to develop explainable trustworthy AI solutions. Both AP possess rich experience with AI solutions for healthcare. Namely, processing large amounts of sensitive image and clinical data, interactive machine learning methods with a human-in-the-loop, and validating AI methods for healthcare. The main body of the BioMedAI project concentrates on training computer science researchers at MU and clinical experts at MMCI in the development of explainable AI methods based on high-quality medical data and validated in a clinical setting. Concretely, we propose organizing thematic workshops, virtual training with hands-on experience in developing explainable AI tools, and two summer schools. One will be oriented towards basic research in explainable AI methods for image and clinical data processing, and the other one towards the FAIR management of sensitive medical data. Furthermore, the BioMedAI project will also increase the visibility and presence of the explainable AI research in healthcare at MU and MMCI by training a PR manager responsible for presenting the research to various stakeholders, and by training the existing project management staff at MU and MMCI in writing grant applications for projects in EU and elsewhere.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.4.1 - Widening participation and spreading excellence
MAIN PROGRAMME
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HORIZON.4.1.2 - Twinning
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-CSA - HORIZON Coordination and Support Actions
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) HORIZON-WIDERA-2021-ACCESS-03
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
601 77 Brno
Czechia
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.