Applying Machine Learning in Liver Disease and Transplantation: A Comprehensive Review

Hepatology. 2020 Mar;71(3):1093-1105. doi: 10.1002/hep.31103. Epub 2020 Mar 6.


Machine learning (ML) utilizes artificial intelligence to generate predictive models efficiently and more effectively than conventional methods through detection of hidden patterns within large data sets. With this in mind, there are several areas within hepatology where these methods can be applied. In this review, we examine the literature pertaining to machine learning in hepatology and liver transplant medicine. We provide an overview of the strengths and limitations of ML tools and their potential applications to both clinical and molecular data in hepatology. ML has been applied to various types of data in liver disease research, including clinical, demographic, molecular, radiological, and pathological data. We anticipate that use of ML tools to generate predictive algorithms will change the face of clinical practice in hepatology and transplantation. This review will provide readers with the opportunity to learn about the ML tools available and potential applications to questions of interest in hepatology.

Publication types

  • Review

MeSH terms

  • Algorithms
  • Artificial Intelligence
  • Humans
  • Liver Diseases / diagnosis
  • Liver Diseases / therapy*
  • Liver Transplantation* / adverse effects
  • Liver Transplantation* / mortality
  • Machine Learning*
  • Neural Networks, Computer
  • Patient Selection