ReviewCancer medicine2024
Artificial intelligence in lung cancer screening: Detection, classification, prediction, and prognosis.
Review in Cancer medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers, 1 of them a synthesis that pooled it.
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
53 citing papers in PubMed, 1 synthesis or guideline pooled it, 70 citations in OpenAlex.
- Machine learning approaches for EGFR mutation status prediction in NSCLC: an updated systematic review.Frontiers in oncology · 2025Pooled it
- Application of artificial intelligence in head and neck squamous cell carcinoma.Annals of medicine · 2026Review
- Characterization of Patients with Non-Small Cell Lung Cancer Using Machine Learning Tools: Relationship with Prognostic Biomarkers and Overall Survival-A Pilot Study.Journal of clinical medicine · 2026Article
- Article
- Real-World Imaging Data: Opportunities and Challenges.JMIR medical informatics · 2026Article
- TikTok, Xiaohongshu, and Bilibili as sources of medical information on lung cancer: a multi-platform content evaluation.Journal of thoracic disease · 2026Article
- Artificial intelligence-assisted early screening of lung cancer and accurate diagnosis of pulmonary nodules: research progress and clinical prospects from radiomics to multi-omics integration: a narrative review.Journal of thoracic disease · 2026Review
- Advances in Brain Tumor Biomarkers: From Molecular Profiling to Liquid Biopsy and AI-Driven Detection.Cancers · 2026Review
- Artificial intelligence-powered liquid biopsy in cancer: a paradigm shift in cancer detection and personalized care.Cancer cell international · 2026Review
- Review
- Automated grading and diagnosis of sacroiliitis on CT images using a 3D convolutional neural network: a multicenter retrospective study.Scientific reports · 2026Article
- Budget Impact of the LungFlag™ Predictive Risk Model for Lung Cancer Screening.PharmacoEconomics - open · 2026Article
- Artificial Intelligence in Oncologic Thoracic Surgery: Clinical Decision Support and Emerging Applications.Cancers · 2026Review
- Development and validation of a nomogram for preoperative prediction of the invasiveness of stage T1 lung adenocarcinoma utilizing AI-driven radiomics.BMC cancer · 2026Article
- Interpretable multi-center machine learning model driven by facial image features for non-invasive early risk assessment of lung cancer.Frontiers in physiology · 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
- From Variant to Biomarker in NSCLC Immunotherapy Resistance: Multiomics Evidence Chains and Accountable AI Integration.Human mutation · 2026Review
- Application of an improved YOLOv5s-based deep learning model for automated detection of pulmonary adenocarcinomaFrontiers in medicine · 2026Article
- CNN-RNN framework for lung cancer classification using CT imaging and GAN-based augmentation.Frontiers in artificial intelligence · 2026Article
- Diagnostic performance of artificial intelligence models for pulmonary nodule classification: a multi-model evaluation.European radiology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 2 institutions in 2 countries.
Funding
Abstract
backgroundThe exceptional capabilities of artificial intelligence (AI) in extracting image information and processing complex models have led to its recognition across various medical fields. With the continuous evolution of AI technologies based on deep learning, particularly the advent of convolutional neural networks (CNNs), AI presents an expanded horizon of applications in lung cancer screening, including lung segmentation, nodule detection, false-positive reduction, nodule classification, and prognosis. METHODOLOGY: This review initially analyzes the current status of AI technologies. It then explores the applications of AI in lung cancer screening, including lung segmentation, nodule detection, and classification, and assesses the potential of AI in enhancing the sensitivity of nodule detection and reducing false-positive rates. Finally, it addresses the challenges and future directions of AI in lung cancer screening.
resultsAI holds substantial prospects in lung cancer screening. It demonstrates significant potential in improving nodule detection sensitivity, reducing false-positive rates, and classifying nodules, while also showing value in predicting nodule growth and pathological/genetic typing.
conclusionsAI offers a promising supportive approach to lung cancer screening, presenting considerable potential in enhancing nodule detection sensitivity, reducing false-positive rates, and classifying nodules. However, the universality and interpretability of AI results need further enhancement. Future research should focus on the large-scale validation of new deep learning-based algorithms and multi-center studies to improve the efficacy of AI in lung cancer screening.
Indexed as
Identifiers
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.