Advancing the diagnosis and treatment of metabolic-associated steatotic liver disease: The transformative role of artificial intelligence

Table 1 Artificial intelligence applications in metabolic-associated steatotic liver disease diagnosis and treatment

Application	Description	Ref.
AI-based pathology tools	Improve histological scoring accuracy and reduce reader variability	Pulaski et al[3], Ratziu et al[4], Solomon et al[2]
Non-invasive screening models	Predict MASLD using lab and anthropometric data with high accuracy	Masaebi et al[5], Njei et al[6], Leow et al[10], Fan et al[7], Bosch et al[8]
AI-enhanced ultrasonography	Enhances steatosis and fibrosis diagnosis with improved AUC values	Santoro et al[11], Fujii et al[13], Luetkens et al[12]
Gut microbiota-based diagnosis	Identifies MASLD using ML models trained on gut microbiota data	Park et al[14]
Personalized treatment strategies	Guide targeted interventions using clinical and risk factor data	Dabbah et al[15], Wu et al[16], Malik et al[17]
Treatment response assessment	Evaluates therapeutic efficacy using continuous histological scores	Ratziu et al[4], Pulaski et al[3], Nishida et al[18]

AI: Artificial intelligence; AUC: Area under the curve; MASLD: Metabolic-associated steatotic liver disease; ML: Machine learning.