Evidence map›Paper›PMID 40547139›Full record

ReviewWorld journal of gastrointestinal oncology2025

Artificial intelligence to predict hepatocellular carcinoma risk in cirrhosis.

Imen Akkari, Hanen Akkari, Raida Harbi

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in World journal of gastrointestinal oncology, 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

3 authors.

Imen AkkariDepartment of Gastroenterology, University Hospital of Hached, University of Sousse, Faculty of Medicine of Sousse, Sousse 4000, Tunisia. imenakkaribm@gmail.com.
Hanen AkkariLATIS-Laboratory of Advanced Technology and Intelligent Systems, University of Sousse, National School of Engineering of Sousse, Sousse 4023, Tunisia.
Raida HarbiDepartment of Gastroenterology, University Hospital of Sahloul, University of Sousse, Faculty of Medicine of Sousse, Sousse 4054, Tunisia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Hepatocellular carcinoma (HCC) is the second leading cause of cancer-related deaths worldwide. The primary risk factor for HCC is cirrhosis. Identifying individuals who are at high risk of developing HCC will have numerous benefits for patient outcomes, patient quality of life, and the global healthcare system. Artificial intelligence (AI) has the capability to develop systems that emulate human intelligence. Recent studies have highlighted the potential of AI in the management of HCC, and the application of AI appears promising for identifying high-risk groups among patients with cirrhosis who require closer monitoring. Ultimately, the aim of AI in the field of HCC clinical care is to enable earlier diagnosis and consequently improve prognosis.

Indexed as

Artificial intelligenceCirrhosisHepatocellular carcinomaMachine learningPredictionPrognosis

Identifiers

PMID40547139
PMCPMC12179862

What OpenQuestion 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 OpenQuestion graph.