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 bits to bedside” — 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 are part of the Cancer Research Center Cologne Essen (CCCE). Jens Kleesiek is a principal investigator in the German Cancer Consortium (DKTK) and at the Helmholtz Information & Data Science School for Health. We work closely with the German Cancer Research Center (DKFZ) on the Joint Imaging Platform (JIP) for distributed data analysis and federated learning.
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.
