ReviewDiagnostics (Basel, Switzerland)2022
The Potential Role of Artificial Intelligence in Lung Cancer Screening Using Low-Dose Computed Tomography.
Review in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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
19 citing papers in PubMed, 32 citations in OpenAlex.
- Post-Tuberculosis Sequelae and Active Tuberculosis in Lung Cancer: Imaging Patterns and Clinical Associations-A Retrospective Single-Center Cohort Study.Journal of clinical medicine · 2026Article
- Lung Cancer Screening and Prevention: Beyond Conventional Strategies in a Rapidly Changing Global Milieu.American Society of Clinical Oncology educational book. American Society of Clinical Oncology. Annual Meeting · 2026Review
- Identifying a Critical Blind Spot: How Commercial AI (CAD) Systems Fail to Detect Faint Ground-Glass Opacities at -730 HU on Low-Dose CT.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial Intelligence in Lung Cancer: A Narrative Review of Recent Advances in Diagnosis, Biomarker Discovery, and Drug Development.Pharmaceutics · 2026Review
- Diagnostic performance of artificial intelligence models for pulmonary nodule classification: a multi-model evaluation.European radiology · 2026Article
- Low dose CT for lung cancer screening.Journal of thoracic disease · 2025Article
- ViT-DCNN: Vision Transformer with Deformable CNN Model for Lung and Colon Cancer Detection.Cancers · 2025Article
- Repeatability of AI-based, automatic measurement of vertebral and cardiovascular imaging biomarkers in low-dose chest CT: the ImaLife cohort.European radiology · 2025Article
- Article
- A Thorough Review of the Clinical Applications of Artificial Intelligence in Lung Cancer.Cancers · 2025Review
- Artificial Intelligence-Empowered Radiology-Current Status and Critical Review.Diagnostics (Basel, Switzerland) · 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
- AI-Enhanced CAD in Low-Dose CT: Balancing Accuracy, Efficiency, and Overdiagnosis in Lung Cancer Screening.Thoracic cancer · 2025Article
- Precision lung cancer screening from CT scans using a VGG16-based convolutional neural network.Frontiers in oncology · 2024Article
- Lung Cancer Screening with Low-Dose CT: What We Have Learned in Two Decades of ITALUNG and What Is Yet to Be Addressed.Diagnostics (Basel, Switzerland) · 2023Review
- Human-Machine Collaboration in Diagnostics: Exploring the Synergy in Clinical Imaging with Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2023Article
- Assessment of Barriers and Challenges to Screening, Diagnosis, and Biomarker Testing in Early-Stage Lung Cancer.Cancers · 2023Review
- Cost of illness in a super-aged society-comparison of breast, lung, and prostate cancer in Japan.BMC geriatrics · 2022Article
- Computer-Aided Diagnosis of Pulmonary Nodules in Rheumatoid Arthritis.Life (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Two large randomized controlled trials of low-dose CT (LDCT)-based lung cancer screening (LCS) in high-risk smoker populations have shown a reduction in the number of lung cancer deaths in the screening group compared to a control group. Even if various countries are currently considering the implementation of LCS programs, recurring doubts and fears persist about the potentially high false positive rates, cost-effectiveness, and the availability of radiologists for scan interpretation. Artificial intelligence (AI) can potentially increase the efficiency of LCS. The objective of this article is to review the performances of AI algorithms developed for different tasks that make up the interpretation of LCS CT scans, and to estimate how these AI algorithms may be used as a second reader. Despite the reduction in lung cancer mortality due to LCS with LDCT, many smokers die of comorbid smoking-related diseases. The identification of CT features associated with these comorbidities could increase the value of screening with minimal impact on LCS programs. Because these smoking-related conditions are not systematically assessed in current LCS programs, AI can identify individuals with evidence of previously undiagnosed cardiovascular disease, emphysema or osteoporosis and offer an opportunity for treatment and prevention.
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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.