AI Center for Health Care

The AI Center for Health Care project AI Surgery Tracking aims to help improve surgical care through robust and user-friendly support systems from the field of AI.

Participating institutions:
Fraunhofer MEVIS, Universität Bremen

The Intelligent Digital Guideline Editor project focuses on the development of a methodology to simplify the planning of efficient clinical studies using causal inference and adaptive statistical procedures and to be able to quickly integrate their results into existing guidelines using a digital guideline editor.

Participating institutions:
Fraunhofer MEVIS, Universität Bremen, Leibniz BIPS

The project On the way to AI-supported intelligent magnetic resonance imaging aims to develop an application-oriented language for the development of imaging techniques in magnetic resonance imaging that enables the support of efficient machine learning processes and thus automatically selects the best possible imaging.

Participating institutions:
Universität Bremen, Fraunhofer MEVIS, DFKI

The aim of the AI Center for Health Care project NAKO+ILSE is to combine multimodal data from different studies to improve the prediction of the biological, immunological and cognitive age of individuals and to support the early detection of dementia.

Participating institutions:
Universität Bremen, Fraunhofer MEVIS, Leibniz BIPS, Universität Heidelberg (beratend)

The NAKO-MNA project aims at the AI-based development of a multimodal implicit data model based on combined image data and complex tabular data from the NAKO health study. One application goal is the improved ability to sensitively detect deviations from the norm and previously undetected incidental findings.

Participating institutions:
Leibniz BIPS, Fraunhofer MEVIS, University of Bremen

The PORTAL project is researching AI-based optimization strategies for the laser additive manufacturing of endoprostheses. A forward model is to be developed in order to make statements about properties such as fatigue strength of the manufactured component in accordance with the intended use. A further aim is to develop a backward model in order to parameterize the manufacturing process precisely to the intended use.

Participating institutions:
Leibniz IWT, Universität Bremen

The aim of the AI Center for Health Care project ENABLE is to develop an antibacterial alloy that will help to reduce implant-associated infections.

Participating institutions:
Leibniz IWT, Universität Bremen

The AI Center for Health Care project KIKI focuses on the advantageous structures of diatoms for the development of medical endoprostheses.

Participating institutions:
AWI, Leibniz IWT

The aim of the project MetaN is to improve the efficiency of MR imaging through the use of flexible metamaterials that are dynamically optimized using artificial intelligence methods.

Participating institutions:
Universität Bremen, Fraunhofer IFAM, Fraunhofer MEVIS