Artificial intelligence in liver disease: Current status and future direction

Vollständiger Abstract

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Chronic liver disease is a leading cause of death globally, primarily owing to liver cirrhosis and hepatocellular carcinoma. Early diagnosis and effective treatment are critical for curative therapy. By integrating imaging data, multiomics data, clinical test results, and electronic health records, artificial intelligence (AI) and machine learning algorithms are increasingly being developed to improve the diagnosis, prognosis, and treatment-related decision-making of liver disease. Notable AI models include CatBoost, ALADDIN (mAchine Learning ADvanceD fibrosis and rIsk MASH Novel predictor), GigaTIME, and METABOLISM. Additionally, AI supports donor organ quality assessment, outcome prediction, and the optimization of drug delivery and therapeutic efficacy. This mini-review summarizes the evidence of AI applications in enhancing liver disease diagnosis, prognosis, treatment, and research. However, many AI models remain difficult to interpret or explain and face challenges such as data bias and limited generalizability across regions. Furthermore, clinical integration of AI requires robust multicenter validation, data interpretability, ethical compliance, and adequate healthcare infrastructure.