BioMedAI TWINNING (BioMedAI TWINNING)
- Kód projektu
- Období řešení
- 11/2022 - 10/2025
- Investor / Programový rámec / typ projektu
- Horizont Evropa
- Rozšiřování účasti a posílení ERA
- Fakulta / Pracoviště MU
- Ústav výpočetní techniky
- Další fakulta/pracoviště MU
- Fakulta informatiky
- Klíčová slova
- oncology, human computer interaction, artificial intelligence, machine learning, data management and data interoperability
- Spolupracující organizace
Masarykův onkologický ústav Brno
- Odpovědná osoba MUDr. Rudolf Nenutil, CSc.
- Odpovědná osoba Dr. Heimo Mueller
- Odpovědná osoba Christian Geissler
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 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 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.
Cíle udržitelného rozvoje
Masarykova univerzita se hlásí k cílům udržitelného rozvoje OSN, jejichž záměrem je do roku 2030 zlepšit podmínky a kvalitu života na naší planetě.