“AI4Health” is an initiative funded by the State of Bremen as part of the Lead Project on Artificial Intelligence of the U Bremen Research Alliance. The initiative aims to promote the sustainable advancement of AI-enabled health research in Bremen and the broader northwestern region of Germany.
Four Research Areas form the scientific core of AI4Health: “AI4Prevention,” “Biosignal Interpretation 4 Health,” “Effective Digital Prevention,” and “Small Data.” These areas emerged from the AI Center for Health Care, the Artificial Intelligence Peer-to-Peer Network, and the 2025 scoping workshops, and they consolidate the region’s expertise in artificial intelligence and health research.
AI4Prevention
AI4Prevention seeks to develop a comprehensive technological framework that leverages artificial intelligence (AI) to optimize health-related preventive interventions across all stages of the prevention continuum. Rather than deploying isolated AI models for each stage of prevention, the project aims to establish an integrated system that harnesses and optimizes synergies among the different stages. The seamless exchange of data across these stages, combined with the flexible application of AI throughout all phases, enables the dynamic adaptation and optimization of prevention strategies. The project builds on promising AI applications that have previously been used in specific areas, such as early detection and targeted outreach to at-risk populations. It expands these approaches into a comprehensive system designed to improve preventive interventions across the entire continuum. In the long term, this system could contribute to improving population health, increasing the efficiency of resource utilization, and reducing health care costs.
Contact: Dr. Annika Gerken
Project Leads:
Biosignal Interpretation 4 Health
The objective is to capture and analyze individual biosignals using artificial intelligence to generate medically relevant insights and adapt cognitive systems to individual needs. To this end, unobtrusive wearable devices record biosignals as well as social, lifestyle, and contextual information generated by individuals in their daily lives. The project utilizes the “Biosignals-HUB – Biosignal Sensors for Human-Centered AI at the University of Bremen,” which is currently being established with funding from the European Regional Development Fund (ERDF). These data and their interpretations may support the early prediction of disease and enable personalized interventions or therapies, among other potential applications. A particular focus of this Research Area is on safeguarding privacy and data protection and ensuring the ethical collection of data. Relevant preliminary work was conducted within projects of the AI Center for Health Care (AICHC).
Contact: Dr. Lena Wollschläger
Project Leads:
Effective Digital Prevention
The “Effective Digital Prevention” project focuses on population-based prevention. Its central research question is: “Which preventive interventions work, for whom, and under what conditions?” Current evidence remains fragmented. The project therefore aims to improve the characterization, evaluation, and modeling of the effectiveness of preventive interventions. Approximately 30 researchers, primarily based in Bremen and Oldenburg, collaborate within the project, contributing complementary expertise in public health and computer science. The project’s conceptual and methodological foundations were developed within the LSC Campus Digital Public Health and the AI Center for Health Care (AICHC).
Contact Dr. Maren Emde
Project Leads:
Small Data
In the health care sector and in the processing of neural data, machine learning and artificial intelligence offer considerable potential for supporting diagnostic decision-making and informing treatment recommendations. A major challenge, however, is that health data are often limited in volume and incomplete, insufficiently diverse, or inadequately characterized using standard features. This project focuses on the development of learnable data representations that promote robustness, explainability, fairness, and generalizability. The conceptual and methodological foundations of the project were developed within the “Small Data” working group of the Artificial Intelligence Peer-to-Peer Network.
Contact: Dr. Lena Wollschläger
Project Leads:
Dr. Maren Emde
Coordinator for Research and Transfer Focus in Health Sciences and Scientific Managing Director of the Health Sciences Research Priority Area, University of Bremen
Universität Bremen
UNICOM – Haus Turin
Mary-Somerville-Straße 3
28359 Bremen
emde@uni-bremen.de
Phone: +49 421 218 58519
Dr. Annika Gerken
Senior Scientist Medical Image Analysis, Artificial Intelligence
Fraunhofer Institute for Digital Medicine MEVIS
Max-von-Laue-Str. 2
28359 Bremen
annika.gerken@mevis.fraunhofer.de
Phone: +49 421 17879 2139
Dr. Lena Wollschläger
Research Area Managerin "Small Data" & "Biosignal Interpretation 4 Health"
U Bremen Research Alliance
c/o Universität Bremen
UNICOM 2 – Haus Oxford
Mary-Somerville-Straße 2
28359 Bremen
lena.wollschlaeger@vw.uni-bremen.de
Phone: +49 421 218 60023
Prof. Dr.-Ing. Horst Hahn
Institute director Fraunhofer Institute for Digital Medicine
horst.hahn@mevis.fraunhofer.de
More information:
https://www.mevis.fraunhofer.de/en/employees/horst-hahn.html
Prof. Dr. Frank Kirchner
Executive Director DFKI Bremen Robotics Innovation Center
More information:
https://www.dfki.de/web/ueber-uns/mitarbeiter/person/frki01
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Prof. Dr.-Ing. Tanja Schultz
Director Cognitive Systems Lab, University Bremen
More information:
https://www.uni-bremen.de/csl/institut/direktorin
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Dr.-Ing. Monika Michaelis
Deputy Director / Artificial Intelligence
c/o Universität Bremen
UNICOM – Haus Oxford
Mary-Somerville-Straße 2
28359 Bremen
monika.michaelis@vw.uni-bremen.de
Phone: +49 421 218 60045
Cooperating in U Bremen Research Alliance:
