Datascience & AI in personalized medicine
Development of precise models and support for clinical
decisions for improved medical care
Introducing the
research department
The MOLIT Institute’s Datascience & AI department conducts research in the field of personalized medicine. The focus is on model development and clinical decision support. Thanks to a focus on interoperability, it should be possible to integrate the developments into existing systems as easily as possible.
As part of the AI community, the aim is to provide data for AI training and derive model systems so that digital decision support in translational research can become a clinical reality. The use of current data science methods and the link to biology will make it possible to generate evidence and knowledge.
Research Team
Chantal Bachschmid
Manager Lion-App
Felix Edel
Software Developer
Kevin Kaufmes
Software Developer
Georg Mathes
Software Developer
Dr. Stefan Sigle
Head of Department
Valeriya Vishnevskaya
Software Developer
Publications
Current Publications
3757376
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1
apa
3
date
desc
4566
https://www.molit.eu/wp-content/plugins/zotpress/
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Mathes, G., Berger, S., & Sigle, S. (2026). Automated Extraction of Genetic Eligibility Criteria from Clinical Trial Records Using LLMs – A Technical Case Report. In U. Sax, T. Ganslandt, R. Breitschwerdt, K. Jung, J. König, & A. Großhennig (Eds.), Studies in Health Technology and Informatics. IOS Press. https://doi.org/10.3233/SHTI260998
Pelka, O., Eil, J., Manjunatha, K., Singh, N., Omeirat, J., Sigle, S., Mathes, G., Schweizer, S.-T., Stump, S.-R., Girdziunaite, G., & Nensa, F. (2026). Interoperable clinical data integration for distributed medical AI: The Open Medical Inference Gateway. GMS Medizinische Informatik, Biometrie Und Epidemiologie, 22. https://doi.org/10.3205/MIBE000309
Pelka, O., Sigle, S., Werner, P., Schweizer, S. T., Iancu, A., Scherer, L., Kamzol, N. A., Eil, J. H., Apfelbacher, T., Seletkov, D., Susetzky, T., May, M. S., Bucher, A. M., Fegeler, C., Boeker, M., Braren, R., Prokosch, H.-U., & Nensa, F. (2025). Democratizing AI in Healthcare with Open Medical Inference (OMI): Protocols, Data Exchange, and AI Integration. RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren, a-2651-6653. https://doi.org/10.1055/a-2651-6653
Collaborations
IT solutions for scientific projects
We are open to collaborations for the implementation of scientific projects and the provision of research infrastructure. Our expertise helps you to implement projects efficiently and create the necessary IT and data basis.
Current projects from Datascience & AI
Open Medical Interference (OMI)
The OMI platform enables the exchange and use of healthcare data and AI services through open protocols and data formats.
Lion app for better
quality of life
The Lion app improves patients’ quality of life by recording and analyzing well-being and symptoms to support doctor communication.
Study platform CATS
CATS is a platform for the management and semantic filtering of studies that supports personalized medicine through modern data models.
Variant Browser (VB)
The Variant Browser brings together relevant data sources on genetic variants and combines distributed queries in a standardized overview.
Knowledge Artifacts (KA)
Treatment data is anonymized, extracted, structured and used specifically for decision-making in the research context with the help of AI technologies.
AI MTB-Assistant
The AI Assistant enables interaction between clinical users and large language models – with a focus on reliable, evidence-based outputs.
Qualitymanagement in molecular tumorboards
Digital tools create transparency about composition and development and enable data-based, automated QM.
Predictive models in personalized medicine
ML and AI are used to process publicly available data in order to develop predictive models for clinical decision support in the research context.