Artificial intelligence-enabled insights into dietary modifications driving the progression from metabolic dysfunction-associated steatotic liver disease to hepatocellular carcinoma

Abstract

Dietary patterns represent a major modifiable risk factor contributing to metabolic dysfunction-associated steatotic liver disease (MASLD), which can lead to insulin resistance, inflammation, gut dysfunction and hepatic steatosis. Diet-induced animal models have become essential preclinical tools for investigating the underlying pathological mechanisms of MASLD and for identifying potential therapeutic strategies. However, the complex and multifactorial interactions among diet, host metabolism, genetic predisposition, and environmental factors have limited the ability of conventional approaches to accurately predict disease progression. In recent years, artificial intelligence (AI) has emerged as a powerful tool for deciphering the complex diet-related factors involved in the progression from MASLD to hepatocellular carcinoma (HCC). Although these technologies have demonstrated potential in promoting risk stratification for personalized nutritional interventions, most applications remain at the research stage and require further clinical validation. To the best of our knowledge, no review articles are available on the role of AI in understanding diet-related mechanisms involved in MASLD-HCC disease progression. In the present review, we highlight the implications of AI-enabled methodologies in elucidating the role of dietary factors in MASLD-to-HCC progression.