ReviewJournal of thoracic disease2020
Putting artificial intelligence (AI) on the spot: machine learning evaluation of pulmonary nodules.
Review in Journal of thoracic disease, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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
24 citing papers in PubMed, 44 citations in OpenAlex.
- An optimized EfficientNetB0 framework with CLAHE-based preprocessing for accurate multi-class chest X-ray classification.Scientific reports · 2026Article
- Artificial Intelligence in Lung Cancer: A Narrative Review of Recent Advances in Diagnosis, Biomarker Discovery, and Drug Development.Pharmaceutics · 2026Review
- Artificial intelligence in medical education for pulmonary nodule management: a narrative review.Translational lung cancer research · 2025Review
- New Perspectives on Lung Cancer Screening and Artificial Intelligence.Life (Basel, Switzerland) · 2025Review
- A Thorough Review of the Clinical Applications of Artificial Intelligence in Lung Cancer.Cancers · 2025Review
- Diagnostic value of combined CT artificial intelligence (AI) system and lung cancer biomarkers in pulmonary nodule evaluation.AIMS public health · 2025Article
- Challenging the significance of SUV-based parameters in a large-scale retrospective study on lung lesions.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Independent evaluation of the accuracy of 5 artificial intelligence software for detecting lung nodules on chest X-rays.Quantitative imaging in medicine and surgery · 2024Article
- Revolutionizing Pulmonary Diagnostics: A Narrative Review of Artificial Intelligence Applications in Lung Imaging.Cureus · 2024Review
- Article
- Clinical outcomes and actual consequence of lung nodules incidentally detected on chest radiographs by artificial intelligence.Scientific reports · 2023Article
- Research Progress of Respiratory Disease and Idiopathic Pulmonary Fibrosis Based on Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2023Review
- Article
- Diagnostic efficiency of artificial intelligence for pulmonary nodules based on CT scans.American journal of translational research · 2023Article
- Beyond high hopes: A scoping review of the 2019-2021 scientific discourse on machine learning in medical imaging.PLOS digital health · 2023Article
- Incidentally found resectable lung cancer with the usage of artificial intelligence on chest radiographs.PloS one · 2023Article
- Enhancing physicians' radiology diagnostics of COVID-19's effects on lung health by leveraging artificial intelligence.Frontiers in bioengineering and biotechnology · 2023Article
- Successful Implementation of an Artificial Intelligence-Based Computer-Aided Detection System for Chest Radiography in Daily Clinical Practice.Korean journal of radiology · 2022Article
- Diagnostic validation of a deep learning nodule detection algorithm in low-dose chest CT: determination of optimized dose thresholds in a virtual screening scenario.European radiology · 2022Article
- Article
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 at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lung cancer remains the leading cause of cancer related death world-wide despite advances in treatment. This largely relates to the fact that many of these patients already have advanced diseases at the time of initial diagnosis. As most lung cancers present as nodules initially, an accurate classification of pulmonary nodules as early lung cancers is critical to reducing lung cancer morbidity and mortality. There have been significant recent advances in artificial intelligence (AI) for lung nodule evaluation. Deep learning (DL) and convolutional neural networks (CNNs) have shown promising results in pulmonary nodule detection and have also excelled in segmentation and classification of pulmonary nodules. This review aims to provide an overview of progress that has been made in AI recently for pulmonary nodule detection and characterization with the ultimate goal of lung cancer prediction and classification while outlining some of the pitfalls and challenges that remain to bring such advancements to routine clinical use.
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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.