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    Carsten U. Niemann, MD, PhD

    Associate Professor
    Rigshospitalet, Copenhagen University Hospital
    Copenhagen, Hovedstaden, Denmark

    Carsten Utoft Niemann, MD, PhD is a hematologist, chief physician, and associate professor at University of Copenhagen, Rigshospitalet - Copenhagen University Hospital and at the Danish Cancer Society with +10 years hands-on experience in hematology research laboratories across Europe and US. Developing the first fully data-driven, machine-learning based algorithm in Chronic Lymphocytic Leukemia (CLL); deploying the algorithm as open source into a commercial electronic health record system and into a clinical trial. Chairing the Nordic CLL Study Group, heading the CLL laboratory and clinical research program at Rigshospitalet, Denmark. As a clinical hematologist with laboratory background, my research combines routine health data, genetic and functional characterization of lymphoid malignant cells and microenvironmental cells, MRD testing and diagnostic work up for lymphoid malignancies into designing clinical trials for precision hematology. Impacting current practice in CLL, co-authoring ESMO guidelines, TP53 guidelines, flowcytometry- and MRD-guidelines for CLL. Founding member for +5 investigator-initiated clinical trials testing targeted therapy in CLL. Founder and sponsor of the PreVent-ACaLL phase 2-3 trial, the first Machine Learning based clinical trial in CLL. Developing data-driven translational research based on the Danish Lymphoid-lineage Cancer Research (DALY-CARE) resource, covering +3,000 routine data variables for +65,000 patients combined with genetics for +10,000 patients. Developing extended decision matrices for lymphoid malignancies based on data-driven medicine. Based on DALY-CARE, extending collaboration for national and international standardization of health data across different medical specialties to improve access to machine learning ready health data. Thus, applying a uniquely strong background across all the disciplines needed for translational research in lymphoid malignancies feeding into extended decision matrices based on medical Artificial Intelligence for precision medicine.

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