Current status of research on artificial intelligence in prognostic prediction models for acute-on-chronic liver failure

Acute-on-chronic liver failure （ACLF） is a complex clinical syndrome of acute liver function deterioration on the basis of chronic liver diseases， characterized by hepatic and/or extra-hepatic organ failure and a high short-term mortality rate. At present， there is still a lack of effective treatment methods， and the mortality rate of ACLF reaches 50% ‍—‍ 90% after comprehensive medical treatment. A simple， rapid， and accurate prognostic prediction model for ACLF can help clinicians accurately judge the prognosis of ACLF patients in the early stage， identify the patients with poor prognosis， and provide early interventions， which can improve patient prognosis to some extent and help to reduce mortality rates. With the continuous development of computer science and increasingly powerful data processing capabilities， artificial intelligence is gaining more attention and has been applied in various aspects of liver diseases including diagnosis， treatment， and prognostic prediction. With reference to the current status of research in China and globally， this article reviews the common prognostic models for ACLF and machine learning-based prognostic prediction models and summarizes the latest research advances， in order to provide new perspectives for the future development of prognostic prediction models for ACLF.