Evidence map›Paper›PMID 40312600›Full record

ReviewHepatology international2025

Trends in the applications of artificial intelligence in fatty liver diseases.

Tian-Ao Xie, Li-Li Liufu, Hui-Jin Chen, Hao-Lin Chen, Xin-Ting Hou, Xuan-Rui Wang, Meng-Yi Han, Yu-Kai Shan, Rui-Jing Shen, Zhong-Yu Wu and 3 more

Erratum issuedAbstract readReview
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In one paragraph

Review in Hepatology international, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. 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

5 · Who and what money

Authors and funding

13 authors.

Tian-Ao XieDepartment of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou, 310016, China.
Li-Li Liufu *Department of Clinical Medicine, The Third Clinical School of Guangzhou Medical University, Guangzhou, 511436, China.
Hui-Jin Chen *Guangzhou Institute of Cancer Research, The Affiliated Cancer Hospital, Guangzhou Medical University, Guangzhou, 510182, China.
Hao-Lin ChenDepartment of Clinical Medicine, The Third Clinical School of Guangzhou Medical University, Guangzhou, 511436, China.
Xin-Ting HouDepartment of Clinical Medicine, The Third Clinical School of Guangzhou Medical University, Guangzhou, 511436, China.
Xuan-Rui WangDepartment of Clinical Medicine, The Third Clinical School of Guangzhou Medical University, Guangzhou, 511436, China.
Meng-Yi HanDepartment of Gastroenterology, Chongqing Key Laboratory of Digestive Malignancies, Daping Hospital, Army Medical University, Third Military Medical University, Chongqing, 400042, China.
Yu-Kai ShanDepartment of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou, 310016, China.
Rui-Jing ShenDepartment of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou, 310016, China.
Zhong-Yu WuDepartment of Surgical Oncology, School of Medicine, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou, 310000, China. wuzhongyu@zju.edu.cn.
Shi-Jie LiDepartment of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou, 310016, China. mylee@zju.edu.cn.
Sarun JuengpanichDepartment of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou, 310016, China. 21718716@zju.edu.cn.
Win Topatana *Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou, 310016, China. win.topatana@zju.edu.cn.

Funding

Fundamental Research Funds for the Central Universities No. 2021FZZX005-21National Natural Science Foundation of China No. W2433188The Youth Development Fund Program of Sir Run Run Shaw Hospital, Zhejiang University School of Medicine QNPY23041Zhejiang Provincial Natural Science Foundation of China No.LQ22H030003
6 · The paper itself

Abstract

introductionArtificial intelligence (AI) has rapidly advanced and shows great potential in the prediction, diagnosis, treatment, and prognosis of fatty liver disease (FLD). This study aims to summarize AI's applications and emerging trends in FLD to inspire future research directions.

methodWe analyzed 270 articles sourced from the Web of Science Core Collection published between 2006 and 2024. The study focuses on the medical application of AI in FLD, examining the contributions of authors, institutions, countries, keywords, and cited references.

resultsAI is predominantly applied in FLD diagnosis, with progression from simple diagnostic tools to advanced methods for classifying FLD and assessing liver fat content. Moreover, the types of data used in AI development have evolved, incorporating a variety of new image and clinical data sources. AI is also being integrated into drug development and personalized nutritional therapies for FLD. Additionally, researchers are becoming increasingly interested in the application of AI to study FLD genes.

conclusionWe found that the applications of AI in FLD are mainly reflected in the prediction, diagnosis, therapy, and prognosis of FLD. In contrast to traditional medicine, AI has the potential to advance the fields of precision medicine and telemedicine, as well as to conserve additional social resources. Moreover, AI may help medical personnel from the perspective of traditional Chinese medicine, FLD prognosis, and the use of AI to analyze gene prediction and natural language processing (NLP).

Indexed as

Artificial IntelligenceFatty LiverHumansPrecision MedicinePrognosisArtificial intelligenceComputer-assistedData visualizationDiagnosisDigital healthFatty liverFatty liver, alcoholicImage processingMedical informatics applicationsNon-alcoholic fatty liver diseasePrecision medicine

Identifiers

PMID40312600

What Socratic holds

Textmetadata
Read underepoch 390

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.