Evidence (Publications & White Papers)

AI Improves Lung Nodule Detection in Multi-Center Study

Normal and Abnormal Chest X-Ray Classification at Visa Screening Centers in UAE

Detecting tuberculosis with AI-powered active case finding in Vietnam

Enhancing Stroke Care with AI at Baptist Christian Hospital

Evidence at 2024 World Conference on Lung Cancer cites chest X-ray AI’s potential to detect lung nodules early

The utility of artificial intelligence in identifying radiological evidence of lung cancer and pulmonary tuberculosis in a high-burden tuberculosis setting

Deploying artificial intelligence software in an NHS trust: a how-to guide for clinicians

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

Implementing a chest X-ray artificial intelligence tool to enhance tuberculosis screening in India: Lessons learned

Using Artificial Intelligence to Stratify Normal versus Abnormal Chest X-rays: External Validation of a Deep Learning Algorithm at East Kent Hospitals University NHS Foundation Trust

Comparing tuberculosis symptom screening to chest X-ray with artificial intelligence in an active case finding campaign in Northeast Nigeria

Comparative Assessment of Chest X-ray Interpretations by AI Model and Radiologist Vs Pulmonologist in Predicting the Clinical Status of Covid-19 Pneumonia Patients

Qure.ai receives 510(k) FDA clearance for its Chest X-ray solution for Pneumothorax and Pleural Effusion, Elevating ICU and ER Care Pathways

The diagnostic value of chest X-ray scanning by the help of Artificial Intelligence in Heart Failure (ART-IN-HF)

FDA Clears Qure.ai's qXR-CTR Chest X-ray Solution as a Potential Indicator for Identifying Heart Failure Risk

Performance of a Chest Radiography AI Algorithm for Detection of Missed or Mislabeled Findings: A Multicenter Study

Deep-ASPECTS: A Segmentation-Assisted Model for Stroke Severity Measurement

Identifying malignant nodules on chest X-rays: A validation study of radiologist versus artificial intelligence diagnostic accuracy

The Early diagnostic value of chest X-ray scanning by the help of Artificial Intelligence in Heart Failure (ART-IN-HF): The First Outcomes

Using artificial intelligence to risk stratify COVID19 patients based on chest X-ray findings

Evaluation of chest X-Ray with automated interpretation algorithms for mass tuberculosis screening in prisons

Independent evaluation of 12 artificial intelligence solutions for the detection of tuberculosis

Early Evaluation of an Ultra-Portable X-ray System for Tuberculosis Active Case Finding

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

Pattern of abnormalities amongst chest X-rays of adults undergoing computer-assisted digital chest X-ray screening for tuberculosis in Peri-Urban Blantyre, Malawi: A cross-sectional study

Deep Learning-Based Software Tools for Tuberculosis Detection in Chest X-Ray Images

Application of artificial intelligence in digital chest radiography reading for pulmonary tuberculosis screening

Artificial intelligence matches subjective severity assessment of pneumonia for prediction of patient outcome and need for mechanical ventilation – a cohort study

Automated Lateral Ventricular and Cranial Vault Volume Measurements in 13,851 Subjects Utilizing Deep Learning Algorithms

Deep Learning-Based A.I. Software for Chest X-Ray Analysis to Detect Microbiologically-Confirmed Tuberculosis: A Prospective Study of Diagnostic Accuracy

Chest x-ray analysis with deep learning-based software as a triage test for pulmonary tuberculosis – a prospective study of diagnostic accuracy for culture-confirmed disease

Initial chest radiographs and artificial intelligence (AI) predict clinical outcomes in COVID-19 patients – analysis of 697 Italian patients

Can artificial intelligence (AI) be used to accurately detect tuberculosis (TB) from chest x-ray? A multiplatform evaluation of five AI products used for TB screening in a high TB-burden setting

Performance of Qure.ai automatic classifiers against a large annotated database of patients with diverse forms of tuberculosis

Deep learning, computer-aided radiography reading for tuberculosis – A diagnostic accuracy study from a tertiary hospital in India

Clinical Context Improves the Performance of AI Models for Cranial Fracture Detection

Segmentation and Measurement of Ventricular and Cranial Vault Volumes in 15,223 Subjects Using Artificial Intelligence

AUC and Enriched Datasets are Not Good Enough Anymore: Presenting an Alternative Metric to Evaluate Radiology AI Models

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

Automated classification of X-rays as normal/Abnormal using a high sensitivity deep learning algorithm

Automated Detection and Localization of Pneumocephalus in Head CT scan

Deep Learning for Infarct Detection and Localization from Head CT Scans

Validation of Deep Learning Algorithms for Detection of Critical Findings in Head CT Scans

Prospective evaluation of a deep learning algorithm deployed in an urban imaging centre to notify clinicians of head CT scans with critical abnormalities

Automated Detection of Midline Shift and Mass Effect from Head CT Scans using Deep Learning

Deep learning algorithms for detection of critical findings in head CT scans – A retrospective study

Deep learning in chest radiography: Detection of findings and presence of change

Can Artificial Intelligence Reliably Report Chest X-Rays? Radiologist Validation of an Algorithm trained on 1.2 Million X-Rays

Machine Learning Methods Improve Prognostication, Identify Clinically Distinct Phenotypes, and Detect Heterogeneity in Response to Therapy in a Large Cohort of Heart Failure Patients

Development and Validation of Deep Learning Algorithms for Detection of Critical Findings in Head CT Scans

Automated detection of intra- and extra-axial haemorrhages on CT brain images using deep neural networks

Efficacy of deep learning for screening pulmonary tuberculosis

Identifying pulmonary consolidation in Chest X Rays using deep learning

Clinical validation of a deep learning algorithm for quantification of the idiopathic pulmonary fibrosis pattern

Automated detection and localisation of skull fractures from CT scans using deep learning

Variation in practice patterns and outcomes across United Network for Organ Sharing allocation regions

Deep Neural Networks to Identify and Localize Intracerebral Hemorrhage and Midline Shift in CT Scans of Brain

Generating Heatmaps to Visualize the Evidence of Deep Learning Based Diagnosis of Chest X-Rays

2D-3D Fully Convolutional Neural Networks for Cardiac MR Segmentation

Improving Boundary Classification for Brain Tumor Segmentation and Longitudinal Disease Progression

A Deep-Learning Based Approach for Ischemic Stroke Lesion Outcome Prediction