Evidence mapPaperPMID 42373434Full record

ReviewZhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology2026

[Application and prospects of artificial intelligence from tool empowerment to paradigm reconstruction for whole-course diagnosis and treatment of liver diseases by ultrasound].

J Y Huang, Q Q Xu, L D Chen, W Wang

Abstract readReviewEnglish Abstract
In one paragraph

Review in Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology, 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

4 authors.

J Y HuangDepartment of Ultrasound, the First Affiliated Hospital of Sun Yat-sen University, Guangzhou 510080, China.
Q Q XuDepartment of Ultrasound, the First Affiliated Hospital of Sun Yat-sen University, Guangzhou 510080, China.
L D ChenDepartment of Ultrasound, the First Affiliated Hospital of Sun Yat-sen University, Guangzhou 510080, China.
W WangDepartment of Ultrasound, the First Affiliated Hospital of Sun Yat-sen University, Guangzhou 510080, China.

Funding

Guangdong Science and Technology Plan-International Science and Technology Cooperation Project 506116939071Guangzhou Science and Technology Plan-key Research and Development Project 2025B03J0155National Natural Science Foundation of China 82371983, 82572323
6 · The paper itself

Abstract

The research system and clinical decision-making pathways have been systematically reshaped as the application of artificial intelligence (AI) in the field of ultrasonically has become increasingly mature recently for liver disease imaging. Research focus has shifted at the clinical level from low-dimensional classification and diagnostic tasks to high-dimensional, task-driven intelligent diagnosis and treatment. The underlying algorithms are undergoing a profound transition at the technological level from single-modality to multi-modal collaboration and from isolated feature extraction to spatiotemporal sequential reasoning. Therefore, to provide a forward-looking analysis and reflection on future development trends and existing challenges, this article systematically reviews the latest research progress of AI in the field of liver disease in terms of four aspects of ultrasonography: early-stage screening, precise diagnosis, personalized treatment, and prognostic monitoring.

Indexed as

Artificial IntelligenceLiver DiseasesAlgorithmsHumansUltrasonography

Identifiers

PMID42373434
PMCPMC13312286

What Socratic holds

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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 Socratic graph.