Artificial intelligence in metabolic dysfunction-associated steatotic liver disease: A short narrative review of diagnostic and therapeutic implications

Table 3 Evidence relevant to artificial intelligence-supported therapy and therapeutic development in metabolic dysfunction-associated steatotic liver disease

Ref.	Evidence base	Potential therapeutic contribution	Evidence maturity	Focused limitation
Naoumov et al[53]	Exploratory post hoc analysis of the FLIGHT-FXR trial involving 99 patients with F2-F3 NASH and 198 paired biopsies obtained before and after 48 weeks of placebo or tropifexor treatment	AI-assisted SHG/TPEF digital pathology quantified fibrosis, steatosis, ballooning, collagen architecture, and treatment-related histological changes. It identified reductions in overall and perisinusoidal fibrosis that were not detected by conventional ordinal scoring	Clinically proximate but investigational therapeutic-endpoint evidence	Post hoc analysis of a relatively small trial; evaluated treatment-response measurement rather than AI-guided treatment selection or improved patient outcomes
Harrison et al[42]	Phase 3 randomized trial involving 966 patients with biopsy-confirmed non-cirrhotic NASH and fibrosis who received resmetirom 80 mg, resmetirom 100 mg, or placebo	Demonstrates the expanding therapeutic context in which future decision-support systems may need to consider fibrosis stage, metabolic risk, treatment eligibility, safety, and monitoring requirements	Direct therapeutic evidence, but not an AI study	Does not validate an AI model or demonstrate that AI improves treatment selection, monitoring, or clinical outcomes
Muralidharan et al[30]	Serum lipidomic study of 151 patients with biopsy-paired MASLD data and independent validation in 105 patients; 481 serum lipids were evaluated	Identified lipidomic signatures that may inform future risk stratification, trial enrichment, or biomarker-based therapeutic research	Investigational biomarker evidence; not an AI treatment study	The study assessed disease classification rather than treatment response. The at-risk MASH model achieved an AUROC of 0.912 in development but declined to 0.76 during independent validation
Guo et al[49]	Review of inflammation-mediated crosstalk between diabetes and liver disease, including discussion of AI and systems biology	Provides mechanistic context for using computational approaches to characterize patient-specific inflammatory profiles and identify potential precision-therapy targets	Conceptual and hypothesis-generating evidence	No AI model was developed or validated, and no prospective AI-guided MASLD treatment comparison was conducted
Xia et al[55]	Preclinical drug-discovery study combining pose-filter ensemble learning with structure-based virtual screening of FXR agonists	Supported screening and lead identification, resulting in the discovery of XJ034, which showed FXR activity and metabolic effects in vitro and in high-fat-diet-induced obese mice	Preclinical proof-of-concept for AI-assisted drug discovery	No human MASLD evaluation, clinical treatment comparison, or patient-outcome evidence; the study supports lead discovery rather than established therapy selection
De Chiara et al[56]	Narrative review of the potential integration of AI with liver organ-on-chip platforms for historically defined NAFLD	Describes how AI could analyze complex organ-on-chip data to support target identification, drug screening, and preclinical efficacy or toxicity assessment	Conceptual and preclinical	The publication is a review and does not report direct experimental validation of an AI-integrated organ-on-chip platform in MASLD

AI: Artificial intelligence; AUROC: Area under the receiver operating characteristic curve; MASLD: Metabolic dysfunction-associated steatotic liver disease; MASH: Metabolic dysfunction-associated steatohepatitis; NAFLD: Non-alcoholic fatty liver disease; NASH: Non-alcoholic steatohepatitis; SHG/TPEF: Second-harmonic generation/two-photon excitation fluorescence.