Descripción del proyecto
Mejora de la integración de la inteligencia artificial en la práctica clínica
El proceso de diagnóstico de las enfermedades complejas conlleva muchas pruebas, incluida la obtención de imágenes médicas. La inteligencia artificial (IA o AI, por sus siglas en inglés) y el aprendizaje automático pueden ayudar a agilizar el diagnóstico, pero requieren técnicas fiables diseñadas especialmente para la atención sanitaria y bien conectadas con la práctica clínica. En el proyecto BioMedAI TWINNING, financiado con fondos europeos, se creará un programa de formación específico para el tratamiento de imágenes y datos clínicos sensibles. Los socios programarán seminarios y formaciones virtuales para investigadores de IA con el fin de crear métodos de IA explicable. Se espera que el proyecto mejore la facilitación del uso de la tecnología de IA en la práctica clínica, con evidentes beneficios para el bienestar de los pacientes.
Objetivo
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.
Ámbito científico
Programa(s)
Convocatoria de propuestas
Consulte otros proyectos de esta convocatoriaRégimen de financiación
HORIZON-AG - HORIZON Action Grant Budget-BasedCoordinador
601 77 Brno
Chequia