Evidence map›Paper›PMID 41428992›Full record

ArticleInternational journal of surgery (London, England)2026

The intersection of artificial intelligence and lung nodule research: current applications and future prospects.

Linfeng Wang, JunHao Yu, Yue Luo, JiaYi Nie, XinYue Ge, Yue Li, BaoJin Hua, Rui Liu

Abstract read
In one paragraph

Article in International journal of surgery (London, England), 2026. 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

8 authors.

Linfeng WangDepartment of Oncology, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China.
JunHao Yu
Yue Luo
JiaYi Nie
XinYue Ge
Yue Li
BaoJin Hua
Rui Liu

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lung cancer represents a primary global cause of cancer-related mortality, imposing substantial healthcare burdens on both patients and public health systems. Pulmonary nodules, as early-stage manifestations of lung cancer, exhibit considerable morphological heterogeneity. Consequently, precise identification and clinical management of these nodules are critical for effective lung cancer prevention. In recent years, artificial intelligence (AI) has emerged as a transformative component in modern oncology, providing advanced tools for end-to-end pulmonary nodule management. This review systematically analyzes existing literature through bibliometric assessment to synthesize AI applications across the pulmonary nodule care continuum. AI-powered clinical decision support systems and personalized treatment planning are reshaping precision oncology paradigms. Current research advancements and prevailing challenges are critically examined to identify potential future breakthroughs. The comprehensive synthesis presented herein aims to establish a foundational conceptual framework for researchers and clinicians, while facilitating efficient translation of AI technologies into clinical practice for pulmonary nodule diagnosis and therapy.

Indexed as

artificial intelligencebibliometric analysisdeep learninglung nodulesmachine learning

Identifiers

PMID41428992
PMCPMC13105547

What OpenQuestion holds

Textmetadata
LicenceCC BY-SA
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.