Role of artificial intelligence-based ocular biomarkers in hepatobiliary diseases: A scoping review

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

Core Tip: Artificial intelligence has taken the world by storm. It has various applications in the screening and diagnosis of many systemic diseases. In this review article, we will discuss AI-based ocular biomarkers in hepatobiliary disorders. As per studies, there are deep learning models to detect numerous categories of hepatobiliary disorders on two common types of ocular images: slit-lamp retinal fundus images and optical coherence tomography images.