Evidence map›Paper›PMID 40901593›Full record

ReviewWorld journal of hepatology2025

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

Uday Pratap Singh Parmar, Arvind Kumar Morya, Parul C Gupta, Atul Arora, Nipun Verma

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in World journal of hepatology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Uday Pratap Singh ParmarDepartment of Ophthalmology, Government Medical College and Hospital, Sector 32, Chandigarh 160047, India.
Arvind Kumar MoryaDepartment of Ophthalmology, All India Institute of Medical Sciences, Hyderabad 508126, Telangana, India. bulbul.morya@gmail.com.
Parul C GuptaDepartment of Ophthalmology, Post Graduate Institute of Medical Education and Research, Chandigarh 160012, Punjab, India.
Atul AroraDepartment of Teleophthalmology, Postgraduate Institute of Medical Education and Research, Chandigarh 160012, Punjab, India.
Nipun VermaDepartment of Hepatology, Postgraduate Institute of Medical Education and Research, Chandigarh 160012, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Artificial intelligenceDeep learning and Kayser-Fleischer ringHepatobiliary diseaseOcular biomarkers

Identifiers

PMID40901593
PMCPMC12400317

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.