ReviewEXCLI journal2020
Artificial Intelligence and its future potential in lung cancer screening.
Review in EXCLI journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
What it found
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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.
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.
Who cites it
36 citing papers in PubMed, 57 citations in OpenAlex.
- Multidisciplinary perspectives on artificial intelligence in aging research and education: evolving uses, ethics, and equity considerations in gerontology.The Gerontologist · 2026Article
- Artificial Intelligence in Lung Cancer: A Narrative Review of Recent Advances in Diagnosis, Biomarker Discovery, and Drug Development.Pharmaceutics · 2026Review
- Machine Learning-Guided Detection of Malignancy of Lung Nodules With Molecular Imaging-Guided Surgery.JAMA network open · 2026Article
- Generative AI-Driven CNN Framework for Enhanced Lung Cancer Detection, Prediction, and Treatment: A Novel Approach to Overcoming AI Limitations.International journal of biomedical imaging · 2026Article
- CNN-RNN framework for lung cancer classification using CT imaging and GAN-based augmentation.Frontiers in artificial intelligence · 2026Article
- Procurement and early deployment of artificial intelligence tools for chest diagnostics in NHS services in England: a rapid, mixed method evaluation.EClinicalMedicine · 2025Article
- Implementation, Experiences, Impact, and Costs of Artificial Intelligence in Chest Diagnostics: Protocol for a Mixed Methods Evaluation.JMIR research protocols · 2025Article
- A Systematic Review of AI Performance in Lung Cancer Detection on CT Thorax.Healthcare (Basel, Switzerland) · 2025Review
- Artificial intelligence utilization in cancer screening program across ASEAN: a scoping review.BMC cancer · 2025Article
- A Thorough Review of the Clinical Applications of Artificial Intelligence in Lung Cancer.Cancers · 2025Review
- Te Oranga Pūkahukahu research programme: intentional steps towards a national equity-focused lung cancer screening programme in Aotearoa New Zealand.Journal of the Royal Society of New Zealand · 2025Article
- Comparison of AI software tools for automated detection, quantification and categorization of pulmonary nodules in the HANSE LCS trial.Scientific reports · 2024Article
- Artificial intelligence applications in personalizing lung cancer management: state of the art and future perspectives.Journal of thoracic disease · 2024Review
- Review
- Artificial intelligence in lung cancer.Annals of translational medicine · 2024Article
- Advances in Non-Small Cell Lung Cancer: Current Insights and Future Directions.Journal of clinical medicine · 2024Review
- Artificial intelligence-based graded training of pulmonary nodules for junior radiology residents and medical imaging students.BMC medical education · 2024Article
- How will lung cancer screening and lung nodule management change the diagnostic and surgical lung cancer landscape?European respiratory review : an official journal of the European Respiratory Society · 2024Review
- Recommended approaches for screening and early detection of lung cancer in the Middle East and Africa (MEA) region: a consensus statement.Journal of thoracic disease · 2024Review
- Developing technologies and areas of interest in lung cancer screening adjuncts.Journal of thoracic disease · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) simulates intelligent behavior as well as critical thinking comparable to a human being and can be used to analyze and interpret complex medical data. The application of AI in imaging diagnostics reduces the burden of radiologists and increases the sensitivity of lung cancer screening so that the morbidity and mortality associated with lung cancer can be decreased. In this article, we have tried to evaluate the role of artificial intelligence in lung cancer screening, as well as the future potential and efficiency of AI in the classification of nodules. The relevant studies between 2010-2020 were selected from the PubMed database after excluding animal studies and were analyzed for the contribution of AI. Techniques such as deep learning and machine learning allow automatic characterization and classification of nodules with high precision and promise an advanced lung cancer screening method in the future. Even though several combination models with high performance have been proposed, an effectively validated model for routine use still needs to be improvised. Combining the performance of artificial intelligence with a radiologist's expertise offers a successful outcome with higher accuracy. Thus, we can conclude that higher sensitivity, specificity, and accuracy of lung cancer screening and classification of nodules is possible through the integration of artificial intelligence and radiology. The validation of models and further research is to be carried out to determine the feasibility of this integration.
Indexed as
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What OpenQuestion holds
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.