Artificial Intelligence Applications in Hepatology.

Journal: Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association
Published Date:

Abstract

Over the past 2 decades, the field of hepatology has witnessed major developments in diagnostic tools, prognostic models, and treatment options making it one of the most complex medical subspecialties. Through artificial intelligence (AI) and machine learning, computers are now able to learn from complex and diverse clinical datasets to solve real-world medical problems with performance that surpasses that of physicians in certain areas. AI algorithms are currently being implemented in liver imaging, interpretation of liver histopathology, noninvasive tests, prediction models, and more. In this review, we provide a summary of the state of AI in hepatology and discuss current challenges for large-scale implementation including some ethical aspects. We emphasize to the readers that most AI-based algorithms that are discussed in this review are still considered in early development and their utility and impact on patient outcomes still need to be assessed in future large-scale and inclusive studies. Our vision is that the use of AI in hepatology will enhance physician performance, decrease the burden and time spent on documentation, and reestablish the personalized patient-physician relationship that is of utmost importance for obtaining good outcomes.

Authors

  • Jörn M Schattenberg
    Metabolic Liver Research Program, I. Department of Medicine, University Medical Center, Mainz, Germany.
  • Naga Chalasani
    Division of Gastroenterology and Hepatology, Indiana University School of Medicine, Indianapolis, IN, United States of America.
  • Naim Alkhouri
    Arizona Liver Health and University of Arizona, Tucson, Arizona. Electronic address: nalkhouri@azliver.com.