Evidence map›Paper›PMID 41025073›Full record

ReviewWorld journal of gastroenterology2025

Translational artificial intelligence in gastrointestinal and hepatic disorders: Advancing intelligent clinical decision-making for diagnosis, treatment, and prognosis.

Shu-Qi Ren, Jin-Man Chen, Chuang Cai

Abstract readReview
In one paragraph

Review in World journal of gastroenterology, 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. Article
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.

Shu-Qi RenDepartment of Laboratory Medicine, Zhongshan City Hospital of Integration of TCM & Western Medicine, Zhongshan 528467, Guangdong Province, China.
Jin-Man ChenSchool of Pharmaceutical Sciences, Guangzhou University of Chinese Medicine, Guangzhou 510006, Guangdong Province, China.
Chuang CaiCancer Research Institute of Zhongshan City, Zhongshan City People's Hospital, Zhongshan 528445, Guangdong Province, China. caich6@foxmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Gastrointestinal and hepatic disorders exhibit significant heterogeneity, characterized by complex and diverse clinical phenotypes. Most lesions present without typical symptoms in their early stages, which poses substantial challenges for early clinical identification and intervention. As an interdisciplinary field at the forefront of technology, artificial intelligence (AI) integrates theoretical innovation, algorithm development, and engineering applications, triggering paradigm shifts within the medical field. Current research trends indicate that AI technology is progressively permeating the entire diagnostic and therapeutic process for gastrointestinal and hepatic disorders, facilitating intelligent transformations in precise lesion detection, optimization of treatment decisions, and prognosis evaluation through the integration of different modal data, construction of intelligent algorithms, and establishment of clinical verification systems. This article systematically reviews the latest advancements in AI technology concerning the diagnosis and treatment of gastrointestinal diseases (such as inflammatory bowel disease and digestive system tumors) and hepatic diseases (including hepato-cirrhosis and liver cancer), emphasizing its application value and transformative potential in critical areas such as imaging omics analysis, endoscopic intelligent identification, and personalized treatment prediction.

Indexed as

Artificial IntelligenceClinical Decision-MakingGastrointestinal DiseasesLiver DiseasesAlgorithmsHumansPrecision MedicinePrognosisTranslational Research, BiomedicalArtificial intelligenceDiagnosisGastrointestinal disordersHepatic diseasesPrognosisTreatment decision

Identifiers

PMID41025073
PMCPMC12476653

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.