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CC BY 4.0 · Semin Liver Dis 2025; 45(03): 315-327
DOI: 10.1055/a-2599-3728
Review Article

Big Data Analytics in Large Cohorts: Opportunities and Challenges for Research in Hepatology

Autoren

  • Helen Ye Rim Huang

    1   Department of Internal Medicine III, Gastroenterology, Metabolic Diseases and Intensive Care, University Hospital RWTH Aachen, Aachen, Germany
    2   Division of Translational Medicine and Human Genetics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania

  • Kai Markus Schneider   *

    3   Department of Medicine I, Department of Gastroenterology and Hepatology, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
    4   Center for Regenerative Therapies Dresden (CRTD), Technische Universität (TU) Dresden, Dresden, Germany
    5   Else Kroener Fresenius Center for Digital Health, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany

  • Carolin Schneider   *

    1   Department of Internal Medicine III, Gastroenterology, Metabolic Diseases and Intensive Care, University Hospital RWTH Aachen, Aachen, Germany
    2   Division of Translational Medicine and Human Genetics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
    6   The Institute for Translational Medicine and Therapeutics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
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Graphical Abstract

Abstract

Advances in big data analytics, precision medicine, and artificial intelligence are transforming hepatology, offering new insights into disease mechanisms, risk stratification, and therapeutic interventions. In this review, we explore how the integration of genetic studies, multi-omics data, and large-scale population cohorts has reshaped our understanding of liver disease, using steatotic liver disease as a prototype for data-driven discoveries in hepatology. We highlight the role of artificial intelligence in identifying patient subgroups, optimizing treatment strategies, and uncovering novel therapeutic targets. Furthermore, we discuss the importance of collaborative networks, open data initiatives, and implementation science in translating these findings into clinical practice. Although data-driven precision medicine holds great promise, its impact depends on structured approaches that ensure real-world adoption.

Keywords

big data - liver disease - cohorts - omics - artificial intelligence

* Joint authorship.




Publikationsverlauf

Artikel online veröffentlicht:
21. Mai 2025

© 2025. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)

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