Medical Machine Learning (MML) is a research group at the Institute for Artificial Intelligence in Medicine (IKIM), University Hospital Essen. We develop and deploy machine learning methods with the goal of making a meaningful difference for patients, physicians and hospital staff.
A common approach to medical research is called “from bench to bedside”: using insights gained in laboratory experiments to inform new ways of treating patients. In an analogous approach — “from code to clinic” — we aim to bring our algorithms to the point of care, and to translate them into clinical practice.
Together with a digital-forward clinic administration, the group continues to build on a SMART hospital information technology structure that provides access to real-world medical data. Strong funding and state-of-the-art equipment support this effort. One focus of our work lies in exploring unsupervised learning paradigms for the recognition of oncologically relevant patterns in large and complex data.
We collaborate with many partners in projects for research and patient care.
KI Translation Essen (KITE) is our platform for accelerating the translation of AI into clinical practice. It is the largest AI GPU cluster within a hospital in Germany, and possibly beyond, allowing us to work with patient data in a secure and protected environment.
We also serve as a data and AI platform provider for many national and international projects. One example is RACOON (Radiological Cooperative Network), a nationwide federated infrastructure for collaborative radiology research and AI development, which is part of the Network of University Medicine (NUM). Others include two projects funded by German Cancer Aid (Deutsche Krebshilfe, DKH): ONCOnnect, which strengthens the links between comprehensive cancer centers and regional care providers to improve cancer care across Germany, and ONCOverse, which is part of the German Pancreatic Cancer Alliance and develops a digital care platform to improve research and treatment of pancreatic cancer.
MML received its starting grant through the Cancer Research Center Cologne Essen (CCCE) and is now part of NCT West. We work closely together with Prof. Nensa, who established the Data Integration Center (DIZ) Essen, which in turn is based on the Smart Hospital Information Platform (SHIP). Together with the German Cancer Research Center (DKFZ), we work on the Joint Imaging Platform (JIP) for distributed, machine-learning-based medical image analysis.
Jens Kleesiek is a principal investigator at the Helmholtz Information & Data Science School for Health (HIDSS4Health) and the German Cancer Consortium (DKTK). He is also a member of ELLIS (European Laboratory for Learning and Intelligent Systems), a pan-European network of excellence in AI research.
MML is involved in several programs and networks supporting early-career researchers. As part of the ELLIS network, we offer networking opportunities and training to PhD students and postdoctoral researchers. We are also involved in two DFG Research Training Groups: WisPerMed (Knowledge- and Data-Based Personalization of Medicine at the Point of Care) and AMTEC-PRO (Advanced Methods and Technologies for Proton Therapy). Moreover, we are part of INSIDE:INSIGHT, an EU Doctoral Network focusing on extended reality in healthcare. Our group participates in the BRIDGE program (Boston-Ruhr Initiative for Undergraduate and Graduate Student Exchange), which facilitates internships in biomedical physics and computer science for students from TU Dortmund University, University Hospital Essen, Boston University, MIT, and Harvard’s Massachusetts General Hospital, among other institutions.
In addition, IKIM is helping to establish the BVB-Gesundheitswelt, a joint venture between University Medicine Essen and the Bundesliga soccer club Borussia Dortmund (BVB). Here, we focus on combining sports medicine and artificial intelligence across prevention, acute care, and rehabilitation.
The Institute for Artificial Intelligence in Medicine (IKIM) is a clinical-theoretical institute of the University of Duisburg-Essen and University Medicine Essen. In interdisciplinary teams spanning medicine, computer science, and data science, its groups develop innovative methods to improve diagnostics, personalize therapies, and make healthcare delivery more efficient. The goal is to integrate artificial intelligence responsibly into clinical care, research, and medical education.
The IKIM brings together medical excellence, computer science, and internationally leading research with the goal of translating artificial intelligence into clinical care. From the earliest scientific draft to safe implementation in everyday medicine, we develop technologies that support diagnosis and improve therapies. Trust, clinical relevance, and patient benefit guide every stage of our work.
To make this vision a reality, scientific innovation must be translated into reliable clinical practice. This is why we also build the technical foundations, computational infrastructure, and clinical processes that enable AI to be deployed safely, monitored continuously, and improved over time. By providing expertise, platforms, and reusable models, we empower clinicians and researchers worldwide to bring trustworthy AI into practice.
We believe in a future in which diagnoses are more precise, therapies more personalized, and high-quality healthcare accessible to everyone. IKIM strives to be a globally recognized center where medicine, computer science, and research come together to shape this future. We measure progress not only by the sophistication of algorithms, but by their impact on people: better decisions, supporting healthcare professionals, and a medicine that remains deeply human.
We treat patients — not data.
