Evidence map›Paper›PMID 38352145›Full record

ReviewFrontiers in medicine2024

Research in the application of artificial intelligence to lung cancer diagnosis.

Wenjuan Liu, Nan Shen, Limin Zhang, Xiaoxi Wang, Bainan Chen, Zhuo Liu, Chao Yang

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Frontiers in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. 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

7 authors.

Wenjuan LiuDepartment of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, China.
Nan Shen *Department of Nephrology, The First Affiliated Hospital of Dalian Medical University, Dalian, China.
Limin ZhangDepartment of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, China.
Xiaoxi WangDepartment of Clinical Laboratory, The First Affiliated Hospital of Dalian Medical University, Dalian, China.
Bainan ChenDepartment of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, China.
Zhuo LiuDepartment of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, China.
Chao Yang *Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The morbidity and mortality rates in lung cancer are high worldwide. Early diagnosis and personalized treatment are important to manage this public health issue. In recent years, artificial intelligence (AI) has played increasingly important roles in early screening, auxiliary diagnosis, and prognostic assessment. AI uses algorithms to extract quantitative feature information from high-volume and high-latitude data and learn existing data to predict disease outcomes. In this review, we describe the current uses of AI in lung cancer-focused pathomics, imageomics, and genomics applications.

Indexed as

artificial intelligencediagnosisgenomics applicationsimageomicslung cancerpathomics

Identifiers

PMID38352145
PMCPMC10861801

What OpenQuestion holds

Textmetadata
LicenceCC BY
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 OpenQuestion graph.