BMFTR: Innovative data analysis for women’s health and gender-sensitive medicine

Translation of the German Call

Funding is available for interdisciplinary and transdisciplinary research projects that address specific scientific and clinically relevant issues relating to women’s health or gender-sensitive medicine using innovative data-driven methods and IT-based approaches (such as modelling and AI methods).

This involves, firstly, examining and optimising existing datasets with regard to their inclusion and representativeness—particularly of women—so that any biases are clearly identified and/or reduced. For example, quality-assured training, validation and test datasets can be optimised and established; these are based on real-world medical data and, once suitably processed and annotated, are made permanently available to the scientific community.

Secondly, methods are to be developed and tested to examine algorithms, AI models and AI-based applications for gender-specific biases and to evaluate them transparently. This also includes the optimisation of AI models that currently contain gender-specific biases or exacerbate existing biases in the data.

Funding will be provided exclusively for interdisciplinary and transdisciplinary individual projects that mandatorily combine technical expertise from data sciences (e.g. data science, medical informatics, AI) with clinical expertise in women’s health or gender-sensitive medicine, as well as the clinical disciplines relevant to the project.

Researchers from the University Medical Centre are expected to work closely with the Data Integration Centre based there. Project leads from other institutions are expected to collaborate with similar local organisations.

The research projects are based on existing datasets, in particular routine data from the MII or high-quality health datasets from existing national GFDI initiatives such as the NAKO Health Study and the NUM. It is a prerequisite that a solid data foundation for the research work is already in place at the start of the project, for example in the form of a local test dataset. This must be suitable for the development and verification of the analytical methods until any necessary complete datasets become available later in the course of the project. The relevant accessibility and usability (including, amongst other things, sufficient data quality and case numbers, as well as the suitability of the available data types) of the necessary datasets must be demonstrated in the project description. To this end, consultation with the data-providing GFDI should take place in advance; this consultation should also confirm that the data will be available in good time to avoid premature termination of the project and to enable its timely completion. The outcome of this consultation and the data access arrangements must be set out in the project description. Furthermore, the data selection and the (evaluation) methods must be described, including the necessary statistics.

The application procedure is a single-stage process. The deadline for submitting funding applications is 9 August 2026.

Further information on this call for proposals can be found here.