ReviewTranslational lung cancer research2021
Artificial intelligence for detection and characterization of pulmonary nodules in lung cancer CT screening: ready for practice?
Review in Translational lung cancer research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 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.
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Who cites it
44 citing papers in PubMed.
- DeepFAN, a transformer-based model for human-artificial intelligence collaborative assessment of incidental pulmonary nodules in CT scans: a multireader, multicase trial.Nature cancer · 2026Article
- Strategies for sustainable lung cancer screening: a multi-faceted perspective to long-term surveillance in SOLACE.Insights into imaging · 2026Article
- Review
- Artificial Intelligence in Lung Cancer: A Narrative Review of Recent Advances in Diagnosis, Biomarker Discovery, and Drug Development.Pharmaceutics · 2026Review
- Factors influencing diagnostic yield and complication risk in computed tomography fluoroscopy-guided lung biopsies: a 10-year single-center study.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Article
- Comparison of three commercial AI tools for detection and malignancy assessment of incidental lung nodules.Frontiers in medicine · 2026Article
- Diagnostic performance of artificial intelligence models for pulmonary nodule classification: a multi-model evaluation.European radiology · 2026Article
- Establishing an AI-based diagnostic framework for pulmonary nodules in computed tomography.BMC pulmonary medicine · 2025Article
- Performance Evaluation of Four Deep Learning-Based CAD Systems and Manual Reading for Pulmonary Nodules Detection, Volume Measurement, and Lung-RADS Classification Under Varying Radiation Doses and Reconstruction Methods.Diagnostics (Basel, Switzerland) · 2025Article
- A Systematic Review of AI Performance in Lung Cancer Detection on CT Thorax.Healthcare (Basel, Switzerland) · 2025Review
- A Thorough Review of the Clinical Applications of Artificial Intelligence in Lung Cancer.Cancers · 2025Review
- Applications, image analysis, and interpretation of computer vision in medical imaging.Frontiers in radiology · 2025Review
- An AI deep learning algorithm for detecting pulmonary nodules on ultra-low-dose CT in an emergency setting: a reader study.European radiology experimental · 2024Article
- 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
- A nomogram for predicting invasiveness of lung adenocarcinoma manifesting as ground-glass nodules based on follow-up CT imaging.Translational lung cancer research · 2024Article
- Artificial intelligence for cancer screening and surveillance.ESMO real world data and digital oncology · 2024Review
- Artificial intelligence in lung cancer.Annals of translational medicine · 2024Article
- Artificial intelligence-driven computer aided diagnosis system provides similar diagnosis value compared with doctors' evaluation in lung cancer screening.BMC medical imaging · 2024Article
- Effect of emphysema on AI software and human reader performance in lung nodule detection from low-dose chest CT.European radiology experimental · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lung cancer computed tomography (CT) screening trials using low-dose CT have repeatedly demonstrated a reduction in the number of lung cancer deaths in the screening group compared to a control group. With various countries currently considering the implementation of lung cancer screening, recurring discussion points are, among others, the potentially high false positive rates, cost-effectiveness, and the availability of radiologists for scan interpretation. Artificial intelligence (AI) has the potential to increase the efficiency of lung cancer screening. We discuss the performance levels of AI algorithms for various tasks related to the interpretation of lung screening CT scans, how they compare to human experts, and how AI and humans may complement each other. We discuss how AI may be used in the lung cancer CT screening workflow according to the current evidence and describe the additional research that will be required before AI can take a more prominent role in the analysis of lung screening CT scans.
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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.