Evidence map›Paper›PMID 39309740›Full record

ReviewFrontiers in oncology2024

Advances in artificial intelligence applications in the field of lung cancer.

Di Yang, Yafei Miao, Changjiang Liu, Nan Zhang, Duo Zhang, Qiang Guo, Shuo Gao, Linqian Li, Jianing Wang, Si Liang and 3 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Frontiers in oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 1 pooled it
–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

13 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

13 authors.

Di Yang *Clinical Medical College of Hebei University, Affiliated Hospital of Hebei University, Baoding, China.
Yafei Miao *Clinical Medical College of Hebei University, Affiliated Hospital of Hebei University, Baoding, China.
Changjiang Liu *Thoracic Surgery Department, Fourth Hospital of Hebei Medical University, Shijiazhuang, China.
Nan Zhang *Thoracic Surgery Department, Fourth Hospital of Hebei Medical University, Shijiazhuang, China.
Duo Zhang *Thoracic Surgery Department, Affiliated Hospital of Hebei University, Baoding, China.
Qiang Guo *Thoracic Surgery Department, Affiliated Hospital of Hebei University, Baoding, China.
Shuo Gao *Basic Research Key Laboratory of General Surgery for Digital Medicine, Affiliated Hospital of Hebei University, Baoding, China.
Linqian Li *Basic Research Key Laboratory of General Surgery for Digital Medicine, Affiliated Hospital of Hebei University, Baoding, China.
Jianing Wang *Department of Radiology, Affiliated Hospital of Hebei University, Baoding, China.
Si Liang *Basic Research Key Laboratory of General Surgery for Digital Medicine, Affiliated Hospital of Hebei University, Baoding, China.
Peng Li *Basic Research Key Laboratory of General Surgery for Digital Medicine, Affiliated Hospital of Hebei University, Baoding, China.
Xuan Bai *Basic Research Key Laboratory of General Surgery for Digital Medicine, Affiliated Hospital of Hebei University, Baoding, China.
Ke Zhang *Thoracic Surgery Department, Affiliated Hospital of Hebei University, Baoding, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lung cancer remains a leading cause of cancer-related deaths globally, with its incidence steadily rising each year, representing a significant threat to human health. Early detection, diagnosis, and timely treatment play a crucial role in improving survival rates and reducing mortality. In recent years, significant and rapid advancements in artificial intelligence (AI) technology have found successful applications in various clinical areas, especially in the diagnosis and treatment of lung cancer. AI not only improves the efficiency and accuracy of physician diagnosis but also aids in patient treatment and management. This comprehensive review presents an overview of fundamental AI-related algorithms and highlights their clinical applications in lung nodule detection, lung cancer pathology classification, gene mutation prediction, treatment strategies, and prognosis. Additionally, the rapidly advancing field of AI-based three-dimensional (3D) reconstruction in lung cancer surgical resection is discussed. Lastly, the limitations of AI and future prospects are addressed.

Indexed as

artificial intelligenceconvolutional neural networkdeep learninglung cancermachine learning

Identifiers

PMID39309740
PMCPMC11412794

What OpenQuestion holds

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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.