Language

English

Publication Date

8-27-2025

Journal

World Journal of Hepatology

DOI

10.4254/wjh.v17.i8.109801

PMID

40901593

PMCID

PMC12400317

PubMedCentral® Posted Date

8-27-2025

PubMedCentral® Full Text Version

Post-print

Abstract

Artificial intelligence (AI) has become an indispensable tool in modern health care, offering transformative potential across clinical workflows and diagnostic innovations. This review explores the sation of AI technologies in synthesizing and analyzing multimodal data to enhance efficiency and accuracy in health care delivery. Specifically, deep learning models have demonstrated remarkable capabilities in identifying seven categories of hepatobiliary disorders using ocular imaging datasets, including slit-lamp, retinal fundus, and optical coherence tomography images. Leveraging ResNet-101 neural networks, researchers have developed screening models and independent diagnostic tools, showcasing how AI can redefine diagnostic practices and improve accessibility, particularly in resource-limited settings. By examining advancements in AI-driven health care solutions, this article sheds light on both the challenges and opportunities that lie ahead in integrating such technologies into routine clinical practice.

Keywords

Artificial intelligence, Ocular biomarkers, Hepatobiliary disease, Deep learning and Kayser-Fleischer ring

Published Open-Access

yes

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