Large Language Models for Diagnosis and Prognosis of Chronic Liver Diseases: A Systematic Review
Abstract
BACKGROUND AND AIMS: Chronic liver disease (CLD) affects more than 800 million people worldwide and remains a leading cause of morbidity and mortality. Artificial intelligence (AI), particularly machine learning, has been applied to hepatology for diagnostic and prognostic purposes. Large language models (LLMs) represent a new generation of AI with unique capabilities for processing unstructured clinical text, integrating multimodal inputs, and facilitating patient communication. Their role in CLD, however, has not been systematically reviewed.
METHODS: This systematic review was conducted in accordance with PRISMA guidelines and registered with PROSPERO (CRD420250650268). A literature search of five databases was performed using predefined keywords related to LLMs and CLD. Eligible studies included articles reporting diagnostic, prognostic, clinical decision support, or patient education applications of LLMs in CLD.
RESULTS: A total of 18 studies published between 2023 and 2025 met the inclusion criteria. Studies spanned multiple regions, including the USA, Europe, China, South Asia, and Australia, and employed diverse designs. Evaluated models included ChatGPT-3.5/4, GPT-4o, Bard, Gemini, vision-enabled GPT, and retrieval-augmented frameworks. Applications clustered into four thematic domains: (1) diagnostics, including HCC detection from CT/MRI, CEUS LI-RADS classification, fibrosis staging from pathology text and histology, and MASLD identification from clinical/lab…
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Evidence Hub reads this as a systematic review, done in people. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid. Reported size: 800 participants or records.
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