ReviewJournal of thoracic disease2021
A narrative review of artificial intelligence-assisted histopathologic diagnosis and decision-making for non-small cell lung cancer: achievements and limitations.
Review in Journal of thoracic disease, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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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.
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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.
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Who cites it
11 citing papers in PubMed.
- AI-Powered TAT-Net Hyperspectral Imaging for Rapid and Accurate Differentiation of Sinonasal Inverted Papilloma and Malignancy.World journal of otorhinolaryngology - head and neck surgery · 2026Article
- Artificial intelligence in non-small cell lung cancer: transforming diagnosis, treatment, and prognostic evaluation.Frontiers in medicine · 2026Review
- A 23-gene multi-omics signature predicts prognosis and treatment response in non-small cell lung cancer.Discover oncology · 2025Article
- A Thorough Review of the Clinical Applications of Artificial Intelligence in Lung Cancer.Cancers · 2025Review
- A Predictive Model Integrating AI Recognition Technology and Biomarkers for Lung Nodule Assessment.The Thoracic and cardiovascular surgeon · 2025Article
- Subtypes detection of papillary thyroid cancer from methylation assay via Deep Neural Network.Computational and structural biotechnology journal · 2025Article
- Artificial intelligence applications in personalizing lung cancer management: state of the art and future perspectives.Journal of thoracic disease · 2024Review
- Transitioning to a Personalized Approach in Molecularly Subtyped Small-Cell Lung Cancer (SCLC).International journal of molecular sciences · 2024Review
- Research Trends of Artificial Intelligence in Lung Cancer: A Combined Approach of Analysis With Latent Dirichlet Allocation and HJ-Biplot Statistical Methods.Pulmonary medicine · 2024Article
- Review
- Artificial intelligence in precision medicine for lung cancer: A bibliometric analysis.Digital healthReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo summarize the current evidence regarding the applications, workflow, and limitations of artificial intelligence (AI) in the management of patients pathologically-diagnosed with lung cancer.
backgroundLung cancer is one of the most common cancers and the leading cause of cancer-related deaths worldwide. AI technologies have been applied to daily medical workflow and have achieved an excellent performance in predicting histopathologic subtypes, analyzing gene mutation profiles, and assisting in clinical decision-making for lung cancer treatment. More advanced deep learning for classifying pathologic images with minimal human interactions has been developed in addition to the conventional machine learning scheme.
methodsStudies were identified by searching databases, including PubMed, EMBASE, Web of Science, and Cochrane Library, up to February 2021 without language restrictions.
conclusionsA number of studies have evaluated AI pipelines and confirmed that AI is robust and efficacious in lung cancer diagnosis and decision-making, demonstrating that AI models are a useful tool for assisting oncologists in health management. Although several limitations that pose an obstacle for the widespread use of AI schemes persist, the unceasing refinement of AI techniques is poised to overcome such problems. Thus, AI technology is a promising tool for use in diagnosing and managing lung cancer.
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Registered trials
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.