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Liver Disease Diagnosis and Treatment · Review
Artificial Intelligence in Gastroenterology · September 9, 2026
Raises a question worth testing. It does not answer one.
This narrative review surveys the potential role of artificial intelligence and machine learning in diagnosing and managing metabolic dysfunction-associated steatotic liver disease, identifying strongest current evidence in retrospective case identification, imaging classification, and digital pathology, while therapeutic selection and monitoring remain investigational. The authors recommend viewing AI as decision-support technology rather than clinical judgment replacement, reflecting the early and incomplete state of evidence.
Narrative review. Patients with metabolic dysfunction-associated steatotic liver disease (MASLD).
Current evidence is strongest for retrospective case identification, imaging-based classification, and quantitative digital pathology Therapeutic selection and monitoring of AI applications remain largely investigational Natural language processing and deep-learning approaches may support automated quantification of steatosis, inflammation, and fibrosis
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
Clinicians should recognize that AI applications in MASLD remain exploratory outside retrospective case identification and imaging analysis. Current evidence does not support AI-driven therapeutic selection or disease monitoring as standard clinical practice.
This is a narrative review outlining potential AI applications in MASLD diagnosis and prognosis without presenting empirical trial data, effect sizes, or comparative evidence to support clinical implementation.
As stated by the source record.
Clinicians should recognize that AI applications in MASLD remain exploratory outside retrospective case identification and imaging analysis. Current evidence does not support AI-driven therapeutic selection or disease monitoring as standard clinical practice.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
What is missing. This record has no reported figures. That is a gap in the analysis, not a judgement about the study.
Metabolic dysfunction-associated steatotic liver disease is the most prevalent chronic liver disease worldwide and may progress from steatosis to metabolic dysfunctionassociated steatohepatitis, fibrosis, cirrhosis, and hepatocellular carcinoma.Insulin resistance, obesity, mitochondrial dysfunction, inflammation, and impaired autophagy contribute to its pathogenesis.Early diagnosis, monitoring of disease progression, and assessment of therapeutic response remain important clinical challenges.Artificial intelligence (AI) and machine learning may improve disease prediction by integrating laboratory, metabolic, imaging, histological, and molecular data.Natural language processing and deep-learning approaches applied to radiological imaging and digital histopathology may support automated quantification of steatosis, inflammation, and fibrosis, while AI-assisted multi-omics analysis may identify molecular signatures relevant to disease stratification.Current evidence is strongest for retrospective case identification, imaging-based classification, and quantitative digital pathology; therapeutic selection and monitoring remain largely investigational.AI should therefore be viewed as a decision-support technology rather than a replacement for clinical judgment.
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