Evidence map›Paper›PMID 41167791›Full record

ReviewZhonghua shao shang yu chuang mian xiu fu za zhi2025

[Research advances on the application of artificial intelligence technology in the diagnosis and treatment of sepsis patients].

Y W Zhang, Y L Bai, X M Zhang, Y Wen, Z Y Yang, M Y Lu, X L Xu, Q Q Liu

Abstract readReviewEnglish Abstract
PubMed Full text at PMC Publisher
In one paragraph

Review in Zhonghua shao shang yu chuang mian xiu fu za zhi, 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. [Clinical research and medical ethics in the era of artificial intelligence].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2025
    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

8 authors.

Y W ZhangEmergency and Critical Care Center, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing 100010, China.
Y L BaiEmergency and Critical Care Center, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing 100010, China.
X M ZhangCollege of Integrated Traditional Chinese and Western Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China.
Y WenCollege of Integrated Traditional Chinese and Western Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China.
Z Y YangSecond Clinical College, Liaoning University of Traditional Chinese Medicine, Shenyang 116600, China.
M Y LuSecond Clinical College, Liaoning University of Traditional Chinese Medicine, Shenyang 116600, China.
X L XuEmergency and Critical Care Center, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing 100010, China.
Q Q LiuEmergency and Critical Care Center, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing 100010, China.

Funding

General Program of National Natural Science Foundation of China 82474428The Key Discipline Construction Project of High-level Traditional Chinese Medicine of the National Administration of Traditional Chinese Medicine zyyzdxk-2023001
6 · The paper itself

Abstract

In recent years, with the rapid advancement of artificial intelligence technology, this technology has been extensively applied in the field of emergency and critical care. Artificial intelligence demonstrates significant potential in clinical decision support, offering novel tools for the auxiliary diagnosis and treatment of sepsis. The sepsis prediction model developed based on artificial intelligence technology can efficiently capture and integrate information from diverse modalities, enhance the accuracy of early sepsis prediction, and establish a prognostic evaluation system, thereby guiding clinical precision treatment and ultimately improving the survival rates of sepsis patients. This paper reviews the research advances on the application of artificial intelligence technology in the early prediction and subtype identification of sepsis, prognosis assessment, and prediction of related organ dysfunction. It aims to provide a reference and inspiration for the further application of this technology in the field of emergency and critical care.

Indexed as

Artificial IntelligenceSepsisCritical CareDecision Support Systems, ClinicalHumansPrognosis

Identifiers

PMID41167791
PMCPMC12580017

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.