ReviewKorean journal of radiology2020
Clinical Implementation of Deep Learning in Thoracic Radiology: Potential Applications and Challenges.
Review in Korean journal of radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.
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Who cites it
45 citing papers in PubMed.
- Conventional Versus Artificial Intelligence-Assisted Interpretation of Chest Radiographs in Patients With Acute Respiratory Symptoms in Emergency Department: A Pragmatic Randomized Clinical Trial.Korean journal of radiology · 2023Trial
- Value of a deep learning-based algorithm for detecting Lung-RADS category 4 nodules on chest radiographs in a health checkup population: estimation of the sample size for a randomized controlled trial.European radiology · 2022Trial
- Comparative evaluation of generative AI models for chest radiograph report generation in the emergency department.European radiology · 2026Article
- Deep Learning-Based Iodine Contrast Augmentation for Suboptimally Enhanced CT Pulmonary Angiography: Implications for Pulmonary Embolism Diagnosis.Diagnostics (Basel, Switzerland) · 2025Article
- Artificial Intelligence Analysis of Chest Radiographs for Predicting Major Adverse Events in Patients Visiting the Emergency Department With Acute Cardiopulmonary Symptoms.Korean journal of radiology · 2025Article
- Retrospective Clinical Trial to Evaluate the Effectiveness of a New Tanner-Whitehouse-Based Bone Age Assessment Algorithm Trained with a Deep Neural Network System.Diagnostics (Basel, Switzerland) · 2025Article
- Application of Artificial Intelligence in Thoracic Radiology: A Narrative Review.Tuberculosis and respiratory diseases · 2025Article
- Automated segmentation of brain metastases in T1-weighted contrast-enhanced MR images pre and post stereotactic radiosurgery.BMC medical imaging · 2025Article
- Artificial intelligence system for identification of overlooked lung metastasis in abdominopelvic computed tomography scans of patients with malignancy.Diagnostic and interventional radiology (Ankara, Turkey) · 2025Article
- Deep learning for pediatric chest x-ray diagnosis: Repurposing a commercial tool developed for adults.PloS one · 2025Article
- Classifying abnormalities in chest radiographs from Vietnam using deep learning for early detection of cardiopulmonary diseases.Frontiers in radiology · 2025Article
- Choosing the right artificial intelligence solutions for your radiology department: key factors to considerDiagnostic and interventional radiology (Ankara, Turkey) · 2024Review
- Artificial intelligence applications in personalizing lung cancer management: state of the art and future perspectives.Journal of thoracic disease · 2024Review
- Factors for increasing positive predictive value of pneumothorax detection on chest radiographs using artificial intelligence.Scientific reports · 2024Article
- Evolving and Novel Applications of Artificial Intelligence in Thoracic Imaging.Diagnostics (Basel, Switzerland) · 2024Review
- Deep learning-based prognostication in idiopathic pulmonary fibrosis using chest radiographs.European radiology · 2024Article
- [Clinical Application of Artificial Intelligence-Based Detection Assistance Devices for Chest X-Ray Interpretation: Current Status and Practical Considerations].Journal of the Korean Society of Radiology · 2024Review
- AI-assisted Analysis to Facilitate Detection of Humeral Lesions on Chest Radiographs.Radiology. Artificial intelligence · 2024Article
- The impact of artificial intelligence on the reading times of radiologists for chest radiographs.NPJ digital medicine · 2023Article
- Methods of Visualizing the Results of an Artificial-Intelligence-Based Computer-Aided Detection System for Chest Radiographs: Effect on the Diagnostic Performance of Radiologists.Diagnostics (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Chest X-ray radiography and computed tomography, the two mainstay modalities in thoracic radiology, are under active investigation with deep learning technology, which has shown promising performance in various tasks, including detection, classification, segmentation, and image synthesis, outperforming conventional methods and suggesting its potential for clinical implementation. However, the implementation of deep learning in daily clinical practice is in its infancy and facing several challenges, such as its limited ability to explain the output results, uncertain benefits regarding patient outcomes, and incomplete integration in daily workflow. In this review article, we will introduce the potential clinical applications of deep learning technology in thoracic radiology and discuss several challenges for its implementation in daily clinical practice.
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