Lunit INSIGHT CXR

Accuracy and operational performance of computer-aided detection software use cases for TB screening: a retrospective comparative evaluation in Ho Chi Minh City, Viet Nam

Artificial Intelligence-Based Automated Detection of Chest X-ray Abnormalities as Support for Young Radiologists

PET–CT benchmarked detection and 5-year progression of asymptomatic tuberculosis: a longitudinal, prospective cohort study

How threshold customisation affects the performance of a multiclass X-ray AI model for primary care triage: a retrospective study

A multi-country head-to-head accuracy comparison of automated chest x-ray algorithms for tuberculosis

Artificial intelligence–assisted shared decision-making training for medical students transitioning to residency

Artificial intelligence-assisted chest radiograph interpretation in Role 2 military field hospital settings: a controlled experimental study

Artificial Intelligence Analysis of Chest Radiographs for Predicting Major Adverse Events in Patients Visiting the Emergency Department With Acute Cardiopulmonary Symptoms

Deep learning for pediatric chest x-ray diagnosis: Repurposing a commercial tool developed for adults

Diagnostic Accuracy of Chest X-ray Computer-Aided Detection Software for Detection of Prevalent and Incident Tuberculosis in Household Contacts

Detection of other pathologies when utilising computer-assisted digital solutions for TB screening

Impact of AI-assisted CXR analysis in detecting incidental lung nodules and lung cancers in non-respiratory outpatient clinics

Independent evaluation of the accuracy of 5 artificial intelligence software for detecting lung nodules on chest X-rays

Computer-aided detection of tuberculosis from chest radiographs in a tuberculosis prevalence survey in South Africa: external validation and modelled impacts of commercially available artificial intelligence software

Comparison of Commercial AI Software Performance for Radiograph Lung Nodule Detection and Bone Age Prediction

Effects of Implementing Artificial Intelligence-Based Computer-Aided Detection for Chest Radiographs in Daily Practice on the Rate of Referral to Chest Computed Tomography in Pulmonology Outpatient Clinic

The impact of artificial intelligence on the reading times of radiologists for chest radiographs

Incidentally found resectable lung cancer with the usage of artificial intelligence on chest radiographs

AI Improves Nodule Detection on Chest Radiographs in a Health Screening Population: A Randomized Controlled Trial

Multicentre external validation of a commercial artificial intelligence software to analyse chest radiographs in health screening environments with low disease prevalence

Validation study of machine-learning chest radiograph software in primary and emergency medicine

Association of Artificial Intelligence–Aided Chest Radiograph Interpretation With Reader Performance and Efficiency

Successful Implementation of an Artificial Intelligence-Based Computer-Aided Detection System for Chest Radiography in Daily Clinical Practice

Diagnostic performance of artificial intelligence approved for adults for the interpretation of pediatric chest radiographs

Deep Learning for Detecting Pneumothorax on Chest Radiographs after Needle Biopsy: Clinical Implementation

Effect of deep learning-based assistive technology use on chest radiograph interpretation by emergency department physicians: a prospective interventional simulation-based study

Tuberculosis detection from chest x-rays for triaging in a high tuberculosis-burden setting: an evaluation of five artificial intelligence algorithms

Test-retest reproducibility of a deep learning–based automatic detection algorithm for the chest radiograph

Deep Learning for Chest Radiograph Diagnosis in the Emergency Department

Using artificial intelligence to read chest radiographs for tuberculosis detection: A multi-site evaluation of the diagnostic accuracy of three deep learning systems

Development and Validation of a Deep Learning–based Automatic Detection Algorithm for Active Pulmonary Tuberculosis on Chest Radiographs

Development and Validation of Deep Learning–based Automatic Detection Algorithm for Malignant Pulmonary Nodules on Chest Radiographs

Early user perspectives on using computer-aided detection software for interpreting chest X-ray images to enhance access and quality of care for persons with tuberculosis

Artificial Intelligence for Assessment of Endotracheal Tube Position on Chest Radiographs: Validation in Patients From Two Institutions

Hospital-wide survey of clinical experience with artificial intelligence applied to daily chest radiographs