Machine learning models and AI in predicting diagnosis and prognosis in alcohol-related and metabolic dysfunction-associated steatotic liver disease

Abstract

Steatotic liver disease (SLD) is the most common cause of liver disease globally, with an ever-increasing burden. The two primary components of SLD are metabolic dysfunction-associated steatotic liver disease (MASLD) and Alcohol-Associated Liver Disease (ALD). Both entities have important knowledge gaps in differentiation, diagnosis, risk stratification, and prognosis. Given the enormous burden of both MASLD and ALD and their diverse presentation, they form an ideal ground for the application of artificial intelligence (AI) and machine learning (ML) techniques and algorithms. ML models can aid in disease prediction among large populations and estimate those at the highest risk of disease progression or mortality, while applications with AI technology can aid in better detection and monitored treatment approaches. The use of AI in digital pathology and digital therapeutics are attractive options in moving toward personalized medicine. This review briefly summarizes the knowledge gaps in SLD with emerging literature on the use of ML and AI technologies across domains of disease detection, diagnosis, and prognosis.

Keywords

Steatotic liver disease, machine learning, artificial intelligence, prognosis, metabolic dysfunction / alcohol

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