Parmar UPS, Morya AK, Gupta PC, Arora A, Verma N. Role of artificial intelligence-based ocular biomarkers in hepatobiliary diseases: A scoping review. World J Hepatol 2025; 17(8): 109801 [DOI: 10.4254/wjh.v17.i8.109801]
Corresponding Author of This Article
Arvind Kumar Morya, MD, Professor, Senior Researcher, Department of Ophthalmology, All India Institute of Medical Sciences, Bibi Nagar, Hyderabad 508126, Telangana, India. bulbul.morya@gmail.com
Research Domain of This Article
Ophthalmology
Article-Type of This Article
Minireviews
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Uday Pratap Singh Parmar, Department of Ophthalmology, Government Medical College and Hospital, Sector 32, Chandigarh 160047, India
Arvind Kumar Morya, Department of Ophthalmology, All India Institute of Medical Sciences, Hyderabad 508126, Telangana, India
Parul C Gupta, Department of Ophthalmology, Post Graduate Institute of Medical Education and Research, Chandigarh 160012, Punjab, India
Atul Arora, Department of Teleophthalmology, Postgraduate Institute of Medical Education and Research, Chandigarh 160012, Punjab, India
Nipun Verma, Department of Hepatology, Postgraduate Institute of Medical Education and Research, Chandigarh 160012, India
Author contributions: Morya AK and Gupta PC conceptualized the research topic; Parmar UPS, Arora A and Morya AK wrote the manuscript; Gupta PC and Verma N edited the manuscript; final submission by Morya AK.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Arvind Kumar Morya, MD, Professor, Senior Researcher, Department of Ophthalmology, All India Institute of Medical Sciences, Bibi Nagar, Hyderabad 508126, Telangana, India. bulbul.morya@gmail.com
Received: May 22, 2025 Revised: June 7, 2025 Accepted: July 24, 2025 Published online: August 27, 2025 Processing time: 97 Days and 20.2 Hours
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.
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.
