Artificial intelligence and generative models in hepatology: from large language models to digital pathology in liver disease diagnosis and treatment
4 affiliations
- 1Medical Data Analytics Centre, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
- 2State Key Laboratory of Digestive Disease, Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong, China
- 3Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China
- 4Ajmera Transplant Centre, University Health Network, University of Toronto, Toronto, ON, Canada
Abstract
Artificial intelligence (AI), particularly foundation and generative models, is reshaping the practice of hepatology through enhanced knowledge synthesis, quantitative and reproducible analysis of multimodal data, and personalized clinical decision support. This narrative review examines the transition from task-specific discrimination AI to large language models (LLMs), multimodal foundation models, and agentic AI. We synthesize evidence from original and validation studies, clinical evaluations, and benchmark studies, as well as expert reviews and regulatory frameworks across metabolic dysfunction-associated steatotic liver disease, chronic hepatitis B, cirrhosis and portal hypertension, hepatocellular carcinoma, and liver transplantation. LLMs can convert free-text notes into structured data, summarize longitudinal electronic health records, support patient education, and retrieve guideline-based information. Retrieval-augmented generation and agentic AI may improve traceability and workflow support, but current evidence is largely retrospective or proof-of-concept. In digital pathology and imaging, discriminative AI has enabled more quantitative and reproducible histologic scoring and biomarker analysis. Pathology and multimodal foundation models offer transferable representations, report generation, and cross-modal reasoning, but hepatology-specific validation remains limited. Key risks include hallucination, automation bias, domain shift across centers and devices, and inequities due to under-representation of patient subgroups. We outline the future directions for safe AI model deployment based on multimodal foundation models, prospective and federated evaluation, lifecycle governance, and continuous monitoring for performance, calibration, and equity. Most generative AI applications in hepatology remain at the proof-of-concept stage, and rigorous prospective validation with human-in-the-loop oversight is required before clinical integration.
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BibTeX
@article{peng2026artificial,
title = {{Artificial intelligence and generative models in hepatology: from large language models to digital pathology in liver disease diagnosis and treatment}},
author = {Nana Peng and Mary Yue Wang and Sherlot Juan Song and Jimmy Che-To Lai and Vicki Wing-Ki Hui and Vincent Wai-Sun Wong and Grace Lai-Hung Wong and Mamatha Bhat and Terry Cheuk-Fung Yip},
journal = {Clinical and Molecular Hepatology},
year = {2026},
month = jul,
doi = {10.3350/cmh.2026.0258},
url = {https://doi.org/10.3350/cmh.2026.0258},
note = {CC BY-NC 3.0},
}
RIS
TY - JOUR
TI - Artificial intelligence and generative models in hepatology: from large language models to digital pathology in liver disease diagnosis and treatment
AU - Nana Peng
AU - Mary Yue Wang
AU - Sherlot Juan Song
AU - Jimmy Che-To Lai
AU - Vicki Wing-Ki Hui
AU - Vincent Wai-Sun Wong
AU - Grace Lai-Hung Wong
AU - Mamatha Bhat
AU - Terry Cheuk-Fung Yip
PY - 2026
DA - 2026/07/23
JO - Clinical and Molecular Hepatology
DO - 10.3350/cmh.2026.0258
UR - https://doi.org/10.3350/cmh.2026.0258
AB - Artificial intelligence (AI), particularly foundation and generative models, is reshaping the practice of hepatology through enhanced knowledge synthesis, quantitative and reproducible analysis of multimodal data, and personalized clinical decision support. This narrative review examines the transition from task-specific discrimination AI to large language models (LLMs), multimodal foundation models, and agentic AI. We synthesize evidence from original and validation studies, clinical evaluations, and benchmark studies, as well as expert reviews and regulatory frameworks across metabolic dysfunction-associated steatotic liver disease, chronic hepatitis B, cirrhosis and portal hypertension, hepatocellular carcinoma, and liver transplantation. LLMs can convert free-text notes into structured data, summarize longitudinal electronic health records, support patient education, and retrieve guideline-based information. Retrieval-augmented generation and agentic AI may improve traceability and workflow support, but current evidence is largely retrospective or proof-of-concept. In digital pathology and imaging, discriminative AI has enabled more quantitative and reproducible histologic scoring and biomarker analysis. Pathology and multimodal foundation models offer transferable representations, report generation, and cross-modal reasoning, but hepatology-specific validation remains limited. Key risks include hallucination, automation bias, domain shift across centers and devices, and inequities due to under-representation of patient subgroups. We outline the future directions for safe AI model deployment based on multimodal foundation models, prospective and federated evaluation, lifecycle governance, and continuous monitoring for performance, calibration, and equity. Most generative AI applications in hepatology remain at the proof-of-concept stage, and rigorous prospective validation with human-in-the-loop oversight is required before clinical integration.
SN - 2287-2728
SN - 2287-285X
ER -