ReviewCancers2023
Artificial Intelligence and Lung Cancer: Impact on Improving Patient Outcomes.
Review in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
66 citing papers in PubMed, 2 syntheses or guidelines pooled it, 110 citations in OpenAlex.
- Artificial intelligence for lung cancer: a systematic review of head‑to‑head CT, FDG PET/CT, and multimodal models across screening, staging, and prognosis.BMC medical imaging · 2026Pooled it
- Artificial intelligence-assisted endobronchial ultrasound for differentiating between benign and malignant thoracic lymph nodes: a meta-analysis.BMC pulmonary medicine · 2025Pooled it
- Applying clinical natural language processing to lung cancer in Spain: a terminology-based panel approach.ESMO real world data and digital oncology · 2026Article
- The Cutting Edge: A Systematic Review of Artificial Intelligence and Machine Learning in Predicting Esophagectomy Outcomes.Annals of thoracic surgery short reports · 2026Article
- Artificial intelligence for lung cancer classification in cytology specimens: A systematic review and diagnostic test accuracy meta-analysis of benign-malignant diagnosis and ADC/SCC/SCLC subtyping.Journal of pathology informatics · 2026Review
- APASA: adaptive selection of informative peritumoral regions for improved automated cancer lesion analysis.Scientific reports · 2026Article
- Effect of Irreversible Compression on the Pulmonary Nodule Detection Rate in Chest Radiographs Using AI Software.Diagnostics (Basel, Switzerland) · 2026Article
- cGAS-STING pathway regulated by spatiotemporal heterogeneity of tumor microenvironment and precision therapy strategies in lung cancer.Journal of experimental & clinical cancer research : CR · 2026Review
- Artificial intelligence construction: a review of the bridge between CT imaging features of lung ground-glass nodules adenocarcinoma and carcinogenic driver genes.Journal of cancer research and clinical oncology · 2026Review
- Determinants of the Public's Behavioral Intention to Adopt AI-Assisted Lung Cancer Screening: An Extended UTAUT Model Integrating Trust and Risk.Healthcare (Basel, Switzerland) · 2026Article
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- Review
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- Accuracy of AI Tools in the Diagnosis of Benign, Potentially Malignant and Malignant Oral Lesions: A Pilot Study.Journal of clinical medicine · 2026Article
- AI-driven multimodal imaging fusion using swin transformer and optimized tensor fusion networks for pneumonia detection.Scientific reports · 2026Article
- Gene-based lung cancer detection system through omix data and optimized convolutional neural network.Journal of computer-aided molecular design · 2026Article
- Machine learning-assisted classification of lung cancer: the role of sarcopenia, inflammatory biomarkers, and PET/CT anatomical-metabolic parameters.Physical and engineering sciences in medicine · 2026Article
- Performance validation of a closed loop fully automated AI model for lung nodule stratification in screening cases.Respiratory investigation · 2026Article
- Artificial Intelligence in Lung Cancer: A Narrative Review of Recent Advances in Diagnosis, Biomarker Discovery, and Drug Development.Pharmaceutics · 2026Review
- Development and validation of a transformer-based deep learning model for predicting distant metastasis in non-small cell lung cancer usingClinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Article
6 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 8 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lung cancer remains one of the leading causes of cancer-related deaths worldwide, emphasizing the need for improved diagnostic and treatment approaches. In recent years, the emergence of artificial intelligence (AI) has sparked considerable interest in its potential role in lung cancer. This review aims to provide an overview of the current state of AI applications in lung cancer screening, diagnosis, and treatment. AI algorithms like machine learning, deep learning, and radiomics have shown remarkable capabilities in the detection and characterization of lung nodules, thereby aiding in accurate lung cancer screening and diagnosis. These systems can analyze various imaging modalities, such as low-dose CT scans, PET-CT imaging, and even chest radiographs, accurately identifying suspicious nodules and facilitating timely intervention. AI models have exhibited promise in utilizing biomarkers and tumor markers as supplementary screening tools, effectively enhancing the specificity and accuracy of early detection. These models can accurately distinguish between benign and malignant lung nodules, assisting radiologists in making more accurate and informed diagnostic decisions. Additionally, AI algorithms hold the potential to integrate multiple imaging modalities and clinical data, providing a more comprehensive diagnostic assessment. By utilizing high-quality data, including patient demographics, clinical history, and genetic profiles, AI models can predict treatment responses and guide the selection of optimal therapies. Notably, these models have shown considerable success in predicting the likelihood of response and recurrence following targeted therapies and optimizing radiation therapy for lung cancer patients. Implementing these AI tools in clinical practice can aid in the early diagnosis and timely management of lung cancer and potentially improve outcomes, including the mortality and morbidity of the patients.
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.