Evidence map›Paper›PMID 42717391›Full record

ReviewJournal of the Chinese Medical Association : JCMA2026

Artificial intelligence in advanced liver disease: From risk stratification to decision.

Chia-Fu Liu, Chien-Lin Chang, Ching-Chih Chang, Shao-Jung Hsu, Hui-Chun Huang

Abstract readReview
In one paragraph

Review in Journal of the Chinese Medical Association : JCMA, 2026. 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.

Chia-Fu LiuSchool of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC.
Chien-Lin ChangSchool of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC.
Ching-Chih ChangSchool of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC.
Shao-Jung HsuSchool of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC.
Hui-Chun HuangSchool of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The diagnosis and management of liver cirrhosis and portal hypertension have been challenging for hundreds of years, placing a tremendous burden on medical and social resources. In routine practice, clinicians usually depend on various documented scoring and stratification systems to establish the diagnosis and make decisions. These systems have traditionally been constructed through manual review of retrospective data, image interpretation, and statistical analyses. However, the development of artificial intelligence (AI) has allowed for the analysis of large and varied datasets, and consequently the ability to identify novel factors and test the predictive power of different combinations of factors that would not be feasible using traditional means. Considering the promise of using AI for research in this notorious disease entity, this review article discusses recent advances in AI technology regarding various clinical aspects of liver cirrhosis and portal hypertension. Topics covered include the diagnosis of cirrhosis and clinically significant portal hypertension, detection and prediction of major cirrhosis-related complications (gastroesophageal varices, hepatic encephalopathy, infection, acute kidney injury, hepatorenal syndrome, and hepatocellular carcinoma), assessing the prognosis, and evaluating the treatment response. The potential benefits, drawbacks, limitations, and future perspectives of AI are also discussed.

Indexed as

Artificial IntelligenceLiver CirrhosisLiver DiseasesHumansHypertension, PortalPrognosisRisk AssessmentArtificial intelligenceLiver cirrhosisPortal hypertension

Identifiers

PMID42717391
PMCPMC13690445

What OpenQuestion holds

Textmetadata
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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.