Real-World Data Research
Medical Data Science (MDS)
The Leuchtturm supports real-world data research from the initial idea through to publication as a fully integrated process.
Researchers submit their clinical research questions—feasibility is assessed in advance via the Feasibility Portal and confirmed during an on-site consultation. Based on this, Leuchtturm works with the researchers to develop the study protocol, coordinates regulatory processes—including UAC and ethics committee submissions—and handles the entire technical implementation: data extraction from participating sites, data preparation, and the actual data analysis.
The results are reviewed together with the researchers, methodologically evaluated, and adjusted as needed—data extraction and analysis are repeated iteratively until scientific quality is ensured. In parallel, the Leuchtturm is responsible for the entire research data management process, thereby ensuring the sustainable usability of the generated data.
The process concludes with a joint publication: Leuchtturm provides methodological support for the preparation of the manuscript in close collaboration with the participating researchers.
- Heterogeneous IT systems at the sites (e.g. tumor documentation or biobank management system)
- Different data formats and data elements
- Central data consolidation of retrospective routine data (RWD) not permitted due to lack of consent
- No sustainable research infrastructure (secondary use)
- Lack of skills for distributed machine learning (AI)
- Lack of large, high-quality database (AI)
- Complexity of processing, storing and analyzing bioinformatic data
- As a result, no cross-site retrospective data use for scientific knowledge gain possible
- Data harmonization
- Development/revision of standardized pipelines
- Data quality and plausibility checks
- Data integration
- Federated evaluations
- Distributed machine learning (AI)
- BZKF-wide further education and training in the fundamentals and methods of AI development and concepts of federated machine learning
- Local IT infrastructures (data extraction and harmonization pipeline, DataSHIELD) for federated analysis and machine learning established at all sites IOS Press Ebooks - Towards a Bavarian Oncology Real World Data Research Platform
- Provision of the pipeline for data transfer, pseudonymization and mapping to bring basic oncological data in FHIR format into the DataSHIELD database
- Establishment of a BZKF ETL task force for task and knowledge sharing in the further development process of the pipeline
- Integration of data quality and plausibility checks into the pipeline
- Joint publications on distributed analyses using our own "onco-analytics-on-fhir" pipeline JMIR Preprints #65681: Bridging Data Silos in Oncology with Modular Software for Federated Analysis on FHIR: A Multisite Implementation Study
- Annual BZKF Summer Schools and regular AI webinars
- Establishment of a BZKF-wide research data repository with clinical data, image data, sample information and molecular data
- Development of innovative bioinformatics pipelines and new AI-based decision support
- Ensuring a governance structure for data use and data sharing
- Data quality analysis and continuous feedback for data improvement
- Support study groups and medical researchers in bioinformatics analysis and AI developments
- Tracking the development and analysis of federated AI models based on the established infrastructure
- Development of new algorithms for federated machine learning on different data types
- Continuous BZKF-wide education and training in federated machine learning concepts
Recording of the Machine Learning webinar series October 2025
To further strengthen machine learning skills at the BZKF locations, the AI and Bioinformatics Lighthouse held a two-week webinar series in October 2025. In the events, participants were taught the basics of machine learning both theoretically and through practical exercises - with the aim of creating an understanding of central concepts and techniques and enabling participants to carry out simple modeling independently.
To the YouTube recording: Day 1: Basics of machine learning and supervised learning
To the YouTube recording: Day 2: Machine Learning project steps and classification algorithms
To the YouTube recording: Day 3: Sources of error and noise
To the YouTube recording: Day 4: Unsupervised learning and application example
Day 5: Practical exercises: Repository with the practical exercise/slides is available at https://github.com/bzkf/ml-webinar-2025-uebung
Recording of the BZKF Lighthouse Lecture 2025
To the Youtube recording from 02.07.2025
To the presentation
Recording of the AI webinar series 2025
To the YouTube recording from 29.10.2025
AI webinar: "AI in colorectal cancer prevention - experiences from 6 years of clinical application"
Speaker: Prof. Alexander Hann (UKW); Professorship for Digital Transformation in Gastroenterology
Specialist in Internal Medicine and Gastroenterology
To the Youtube recording from 10.04.2025
Application of artificial intelligence (AI) in clinical diagnostics and the discovery of new therapeutic approaches in hematology, especially in hematologic neoplasms (blood cancer and related diseases). In detail, an AI-supported clinical framework will be presented that aims to facilitate the diagnostic process and accelerate the discovery of translational therapies.
Speaker: Anke Bergmann (University Hospital Würzburg)
Recording of the AI webinar series 2024
To the Youtube recording from 16.10.2024
Real World Federated Learning in Radiology: Hurdles to overcome and Benefits to gain
What obstacles need to be overcome in order to integrate Federated Learning into real radiology applications?
Speaker: Markus Bujotzek, DKFZ Heidelberg
To the Youtube recording from 13.11.2024
An Experimental Survey of Incremental Transfer Learning for Multicenter Collaboration - How can Incremental Transfer Learning solve data protection problems in multicenter collaboration?
Speaker: Dr. Florian Putz, University Hospital Erlangen
Publication: An Experimental Survey of Incremental Transfer Learning for Multicenter Collaboration
Publication: Multicenter privacy-preserving model training for deep learning BM autosegmentation
To the YouTube recording from 11.12.2024
Evaluation and mitigation of the limitations of large language models in clinical decision-making
Speaker: Paul Hager, TUM
