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Towards standardized quality control for AI systems in critical care


The overarching goal of this project is to develop quality assessment standards for machine learning approaches in critical care. As a part of a bigger initiative - redefining the metrology as a science applied to artificial intelligence and machine learning workflows, this project will develop machine learning solutions for selected problems in critical care with special emphasis on quality aspects such as robustness, uncertainty calibration, explainability and fairness. To overcome limitations imposed by privacy concerns, synthetic reference data will be generated from large real data sets as part of the process. 


As machine learning approaches start to be applicable to solving ambitious problems in medicine, a need arises for controlling quality aspects as listed above using standardized evaluation protocols and benchmarks. Machine learning solutions are of particular interest in intensive care units (ICU), where data are continuously recorded with high temporal resolution and where timely decision making is key.  


  • Technical University of Berlin, Faculty IV 
  • Charité - Universitätsmedizin Berlin, Institute of Medical Informatics 


G. Lichtner, F. Balzer, S. Haufe, N. Giesa, F. Schiefenhövel, M. Schmieding, C. Jurth, W. Kopp, A. Akalin, S. J. Schaller, S. Weber-Carstens, C. Spies;F. von Dincklage
Scientific Reports, 11(1),
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