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Cited by 84AI in cancer detectionLung Cancer Diagnosis and TreatmentRadiomics and Machine Learning in Medical Imaging

The value of artificial intelligence in the diagnosis of lung cancer: A systematic review and meta-analysis

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Liu, Mingsi, Wu, Jinghui, Wang, Nian, Zhang, Xianqin, Bai, Yujiao, Guo, Jinlin, Zhang, Lin, Liu, Shulin +1 more·2023·PLOS ONE
DOI 10.1371/journal.pone.0273445PMC PMC10035910
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Abstract

Lung cancer is a common malignant tumor disease with high clinical disability and death rates. Currently, lung cancer diagnosis mainly relies on manual pathology section analysis, but the low efficiency and subjective nature of manual film reading can lead to certain misdiagnoses and omissions. With the continuous development of science and technology, artificial intelligence (AI) has been gradually applied to imaging diagnosis. Although there are reports on AI-assisted lung cancer diagnosis, there are still problems such as small sample size and untimely data updates. Therefore, in this study, a large amount of recent data was included, and meta-analysis was used to evaluate the value of AI for lung cancer diagnosis. With the help of STATA16.0, the value of AI-assisted lung cancer diagnosis was assessed by specificity, sensitivity, negative likelihood ratio, positive likelihood ratio, diagnostic ratio, and plotting the working characteristic curves of subjects. Meta-regression and subgroup analysis were used to investigate the value of AI-assisted lung cancer diagnosis. The results of the meta-analysis showed that the combined sensitivity of the AI-aided diagnosis system for lung cancer diagnosis was 0.87 [95% CI (0.82, 0.90)], specificity was 0.87 [95% CI (0.82, 0.91)] (CI stands for confidence interval.), the missed diagnosis rate was 13%, the misdiagnosis rate was 13%, the positive likelihood ratio was 6.5 [95% CI (4.6, 9.3)], the negative likelihood ratio was 0.15 [95% CI (0.11, 0.21)], a diagnostic ratio of 43 [95% CI (24, 76)] and a sum of area under the combined subject operating characteristic (SROC) curve of 0.93 [95% CI (0.91, 0.95)]. Based on the results, the AI-assisted diagnostic system for CT (Computerized Tomography), imaging has considerable diagnostic accuracy for lung cancer diagnosis, which is of significant value for lung cancer diagnosis and has greater feasibility of realizing the extension application in the field of clinical diagnosis.

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  • Evidence suggests AI might benefit diagnostic services by supporting decision-making, improving detection accuracy, reducing errors, increasing efficiency, and easing workforce burdens.

    “Evidence suggests AI might benefit diagnostic services by supporting decision-making, improving detection accuracy, reducing errors, increasing efficiency, and easing workforce burdens.13, 14, 15”

    From Procurement and early deployment of artificial intelligence tools for chest diagnostics in NHS services in England: a rapid, mixed method evaluation (2025) · cites this paper as [13]

    The Introduction (§1.2) says AI can help physicians screen for early-stage lung cancer while reducing errors and increasing efficiency. The Discussion (§4) says AI-assisted diagnosis has high diagnostic value and anticipates improved accuracy and reduced physician workload. These passages support the proposed benefits for lung-cancer diagnosis, but do not establish them across diagnostic services generally; the workload benefit is presented as a future prospect. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

  • Integrating CT-image analysis through the Pulmonary Nodules Artificial Intelligence Diagnostic System with tumor marker data yielded the highest reported specificity of 96.1%.

    “Furthermore, integrating CT-image analysis through the Pulmonary Nodules Artificial Intelligence Diagnostic System (PNAIDS) with tumor marker data yielded the highest reported specificity (96.1%), while combining”

    From Artificial Intelligence in Oncologic Thoracic Surgery: Clinical Decision Support and Emerging Applications (2026) · cites this paper as [22]

    The Results (§3.3; page unavailable) report pooled specificity of 0.87 for AI-assisted diagnosis, not 96.1% for the claimed combination. Searches for PNAIDS, 96.1%, and CT-image analysis combined with tumor markers found no passage in our copy addressing that specific result. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Liu, M., Wu, J., Wang, N., Zhang, X., Bai, Y., Guo, J., Zhang, L., Liu, S., & Tao, K.
Affiliations
  • Zhengzhou University (CN)
  • Sichuan University (CN)
  • Chengdu Medical College (CN)
  • Chengdu University of Traditional Chinese Medicine (CN)
  • Shaoxing People's Hospital (CN)
Corresponding author
  • Liu, Mingsi
  • Wu, Jinghui
  • Wang, Nian
  • Zhang, Xianqin
  • Bai, Yujiao
  • Guo, Jinlin
  • Zhang, Lin
  • Tao, Ke
ORCID
Venue
PLOS ONE
Publisher
Public Library of Science (PLoS)
Published
2023-03-23
Volume
18
Issue
3
Pages
e0273445
ISSN
1932-6203 (ISSN-L)
DOI
10.1371/journal.pone.0273445
PMC
PMC10035910
OpenAlex
W4360608880
Cited by
84
References
2
Citations by year
2026: 15 · 2025: 34 · 2024: 29 · 2023: 6
FWCI
1.76
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS ONE (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
National Natural Science Foundation of China (32170119, 31870135)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
  • Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Lung Cancer Diagnosis and Treatment
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
Keywords
lung cancer, artificial intelligence, AI-assisted diagnosis, computer-aided diagnosis, diagnostic accuracy, sensitivity and specificity, computed tomography, radiology, missed diagnosis, misdiagnosis
MeSH terms
Artificial Intelligence, Humans, Lung Neoplasms, Sensitivity and Specificity

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