Evidence map›Paper›PMID 40943991›Full record

ReviewJournal of clinical medicine2025

Diagnostic Applications of Artificial Intelligence in Liver Diseases.

Maria Consiglia Bragazzi, Rosanna Venere, Gloria Andriollo, Lorenzo Ridola, Domenico Alvaro

Abstract readReview
In one paragraph

Review in Journal of clinical medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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.

Maria Consiglia BragazziDepartment of Medical and Surgical Sciences and Biotechnologies, Sapienza University of Rome, 04100 Latina, Italy.ORCID 0000-0003-3626-8546
Rosanna VenereDepartment of Medical and Surgical Sciences and Biotechnologies, Sapienza University of Rome, 04100 Latina, Italy.
Gloria AndriolloDepartment of Medical and Surgical Sciences and Biotechnologies, Sapienza University of Rome, 04100 Latina, Italy.
Lorenzo RidolaDepartment of Medical and Surgical Sciences and Biotechnologies, Sapienza University of Rome, 04100 Latina, Italy.ORCID 0000-0002-8596-2609
Domenico AlvaroDepartment of Translational and Precision, Sapienza University of Rome, 00161 Rome, Italy.ORCID 0000-0003-3771-9644

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence encompasses the capacity of machines to emulate human faculties, including reasoning, learning, planning, and creativity. Presently, chronic liver disease, liver cirrhosis, primary liver tumors, and liver metastasis represent significant and escalating causes of mortality worldwide. Consequently, there is a growing demand for more efficient and minimally invasive tools to enhance the diagnostic and therapeutic approaches for these ailments. Over recent years, endeavors have been directed towards employing artificial intelligence within hepatology to advance the diagnosis and treatment of liver diseases. This paper aims to outline the latest developments in the application of artificial intelligence within the realm of liver disease.

Indexed as

artificial intelligencechronic liver diseasecirrhosisdeep learningfocal liver injury

Identifiers

PMID40943991
PMCPMC12429451

What OpenQuestion holds

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LicenceCC BY
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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.