Projektbeschreibung
Bessere Integration von KI in die klinische Praxis
Der Diagnoseprozess für komplexe Krankheiten umfasst zahlreiche Tests, darunter auch die medizinische Bildgebung. KI und maschinelles Lernen können dazu beitragen, die Diagnose zu beschleunigen, erfordern aber vertrauenswürdige Lösungen, die speziell für das Gesundheitswesen entwickelt wurden und gut mit der klinischen Praxis verbunden sind. Das EU-finanzierte Projekt BioMedAI TWINNING wird ein Schulungsprogramm für die Verarbeitung sensibler Bilder und klinischer Daten einrichten. Die Partner werden Workshops und virtuelle Schulungen für KI-Forschende zur Entwicklung erklärbarer KI-basierter Methoden planen. Das Projekt soll den Einsatz von KI-Technologien in der klinischen Praxis ermöglichen, was dem Wohlergehen der Patientinnen und Patienten zugute kommen wird.
Ziel
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.
Wissenschaftliches Gebiet
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Aufforderung zur Vorschlagseinreichung
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HORIZON-AG - HORIZON Action Grant Budget-BasedKoordinator
601 77 Brno
Tschechien