Evidence map›Paper›PMID 39763611›Full record

ReviewFrontiers in oncology2024

Artificial intelligence in lung cancer: current applications, future perspectives, and challenges.

Dongdong Huang, Zifang Li, Tao Jiang, Chaojuan Yang, Ning Li

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 28 papers.

0numbers the graph read from it
0cells of the map it votes in
28citing 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

28 citing papers in PubMed.

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  8. Diagnosis of Lung Diseases in the Artificial Intelligence Era.International journal of molecular sciences · 2026
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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

5 authors.

Dongdong HuangDepartment of Respiratory and Critical Care Medicine, Center for Respiratory Medicine, the Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, China.
Zifang LiDepartment of Rehabilitation Medicine, Yiwu Central Hospital, Yiwu, China.
Tao JiangDepartment of Respiratory and Critical Care Medicine, Center for Respiratory Medicine, the Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, China.
Chaojuan YangDepartment of Respiratory and Critical Care Medicine, Center for Respiratory Medicine, the Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, China.
Ning LiDepartment of Respiratory and Critical Care Medicine, Center for Respiratory Medicine, the Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) has significantly impacted various fields, including oncology. This comprehensive review examines the current applications and future prospects of AI in lung cancer research and treatment. We critically analyze the latest AI technologies and their applications across multiple domains, including genomics, transcriptomics, proteomics, metabolomics, immunomics, microbiomics, radiomics, and pathomics in lung cancer research. The review elucidates AI's transformative role in enhancing early detection, personalizing treatment strategies, and accelerating therapeutic innovations. We explore AI's impact on precision medicine in lung cancer, encompassing early diagnosis, treatment planning, monitoring, and drug discovery. The potential of AI in analyzing complex datasets, including genetic profiles, imaging data, and clinical records, is discussed, highlighting its capacity to provide more accurate diagnoses and tailored treatment plans. Additionally, we examine AI's potential in predicting patient responses to immunotherapy and forecasting survival rates, particularly in non-small cell lung cancer (NSCLC). The review addresses technical challenges facing AI implementation in lung cancer care, including data quality and quantity issues, model interpretability, and ethical considerations, while discussing potential solutions and emphasizing the importance of rigorous validation. By providing a comprehensive analysis for researchers and clinicians, this review underscores AI's indispensable role in combating lung cancer and its potential to usher in a new era of medical breakthroughs, ultimately aiming to improve patient outcomes and quality of life.

Indexed as

artificial intelligenceearly diagnosislung cancerpersonalized treatmentprecision medicine

Identifiers

PMID39763611
PMCPMC11700796

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.