RPS 2105 - Artificial intelligence in chest imaging

SPAIDNet: a Deep Learning-Based Imaging classification Framework for Interstitial Lung Disease Developed and Validated in A Multicenter Prospective Cohort Study

Quantitative imaging markers on HRCT predict rapid progression and adverse events of patients with idiopathic inflammatory myopathies-related interstitial lung disease

Standardized platform to evaluate, compare, and analyze AI-based software for detection and classification of lung nodules for the purpose of CT lung cancer screening implementation

Benchmarking of Artificial Intelligence and Radiologists for Lung Cancer Screening in CT: The LUNA25 Challenge

Systematic prioritisation of ai-detected chest x-ray abnormalities for optimised lung cancer detection: a multicentre study

Beyond Nodules: A Deep Learning Approach for Comprehensive Lung Tumour Segmentation on CT

Foundation Model-based Unsupervised CT Kernel Conversion for Standardizing Emphysema Quantification

Scientific Evidence of AI in Lung Nodule Evaluation on CT-examinations: A Systematic Review

On the effect of lesion number on the FROC performance in AI-based lung nodule detection

Optimizing Healthcare Sustainability through AI-Assisted Lung Cancer Detection at the time of initial CXR

An artificial intelligence software for the detection of benign and non-typically benign pulmonary nodules on chest CT scans

Improving the generalisation of radiographic AI using automated data curation to mitigate shortcut learning