The Department for Clinical AI and Translational Medicine (MedKIT) was established in early 2025 to develop and operate clinical AI applications at the University of Leipzig Medical Center. MedKIT serves as the central unit for coordinating all AI-related activities within patient care at the hospital. To this end, we collaborate closely with the Faculty of Medicine of the University Leipzig and with the other two divisions of the Medical Informatics Center, the Department of Medical Data Science (MDS) and the Data Integration Center (DIZ).
Evidence as a Guiding Principle
We apply the framework of evidence-based medicine to clinical AI applications. Our goal is to ensure that research findings are incorporated into patient care in a transparent and scientifically sound manner.
AMPEL – Detecting Complications Earlier
The core project of the MedKIT department is AMPEL, an open-source initiative in which we have successfully established real-time clinical decision support in patient care over the past years. This Clinical Decision Support System (CDSS) integrates a broad spectrum of digitally collected clinical data and facilitates communication among healthcare professionals. Through multiple channels—digital notifications, telephone alerts, or specialized intervention teams—potential complications are reported in real time. Further information on AMPEL is available here.
Networking as a Driver for Project Development
As the central contact point for AI in clinical care, the Department for Clinical AI and Translational Medicine connects a wide range of stakeholders across the University of Leipzig Medical Center. This network approach enables new AI projects and collaborations to transition rapidly into clinical application. Beyond our hospital, we actively promote collaboration and infrastructure development in this domain. One such example is the Clinical Decision Support Network (CDSN), founded in 2025,
which brings together numerous clinical and non-clinical partners and promotes expertise exchange, collaborative projects and the establishment of shared infrastructures for AI-supported healthcare.
