Open Medical Inference (OMI)

Abstract

Outcomes

The Open Medical Inference (OMI) initiative is a BMFTR-funded national research infrastructure aimed at improving reproducibility, transparency, and comparability of machine learning methods in medical imaging and clinical AI. Within OMI, I contributed to the development and evaluation of standardized benchmarking protocols, open evaluation pipelines, and reference datasets for medical AI, with a particular focus on robustness, generalization, and fair comparison across institutions and data distributions. These activities complement my research on federated learning and trustworthy AI by addressing methodological gaps in evaluation practices and supporting community-wide standards for medical machine learning research.

Our role

Contributing researcher (Scientific Lead) with responsibility for methodological input on benchmarking design, evaluation protocols, and reproducibility aspects, in close collaboration with national partners.

Perspective

OMI provides a sustained national framework for open and reproducible medical AI research. The experience and infrastructure developed within OMI inform ongoing and future projects on federated learning, multi-center evaluation, and trustworthy AI, and strengthen national coordination across academic and clinical institutions. These activities align with and complement my work on federated learning benchmarks and multi-center evaluation (see also Sections and ).