Evidence

AI-Assisted Chest X-ray Reading Improves Sensitivity

The growing demand for chest radiography in healthcare, combined with radiologist shortages and increasing workloads, underscores...

Assessing Artificial Intelligence and Radiologist Performance

Fracture detection on musculoskeletal (MSK) radiographs is critical for both emergency and routine care, yet diagnostic errors remain...

Enhancing Efficiency in Low-Risk Chest X-ray Reporting: A Comparative Study

Efficient and accurate chest X-ray (CXR) reporting is essential in radiology, especially for quickly identifying...

Evaluating the Performance of Deep Learning Model and Junior Radiologists

Chest X-ray (CXR) is a fundamental diagnostic tool in detecting thoracic pathologies. However, the interpretation accuracy can vary...

Enhancing Diagnostic Accuracy in Fracture Identification

Fracture detection using radiography is crucial for effective patient management. Despite advances, missed fractures remain a significant...

Enhancing Accuracy in Breast Density Assessment using Deep Learning

Breast cancer is the most common cancer type in women and is the main cause of cancer-related death in women population in the world...

Detecting Pulmonary Lesions in Low-Prevalence Real-World Settings

The rapid advancement of AI in medical imaging has presented an exciting prospect of enhancing diagnostic accuracy...

The Role of Artificial Intelligence in the Early Detection of Pulmonary Lesions

In past years, the healthcare industry has undergone significant changes associated with the exponential growth of technological innovations

Cross-Center Validation of Deep Learning Model for Musculoskeletal Fractures

Fractures, often resulting from trauma, overuse, or osteoporosis, pose diagnostic challenges due to their variable clinical...

From Image to Insight: A Review of Deep Learning Approaches for Cystic Fibrosis

Cystic fibrosis (CF) is a genetic disease caused by mutations in the Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) gene.

Challenges and Consequences of Artificial Intelligence in Radiology

The development of artificial intelligence (AI) in medical imaging presents many challengesand opportunities.

Can Deep Learning Reliably Recognize Abnormality Patterns on Chest X-rays?

In this study, we developed a deep-learning-based automatic detection algorithm (DLAD, Carebot AI CXR) to detect and local- ize seven...

Leveraging Deep Learning Decision-Support System in Specialized Oncology Center

X-ray imaging shows particular limitations when detecting primary and secondary tumors, and are prone to reading errors...

Chest X-Ray Abnormality Detection Using Artificial Intelligence

Chest X-ray (CXR) is one of the most common radiological examination for both non-emergent and emergent clinical indications...

Fully Integrated Decision-Support System for Detection and Segmentation

Breast cancer is one of the most prevalent forms of cancer affecting women...

Synthetic Data as a Tool to Combat Racial Bias in Medical AI

Assistive tools to aid in skin cancer detection are experiencing an unprecedented rise with the accessibility of robust and accurate DL...

Visualizing CoatNet Predictions for Aiding Melanoma Detection

Melanoma is considered the most aggressive form of skin cancer. Currently, the evaluation of malignancy is mainly performed by invasive...

Design and Implementation of CNN-based Assistive Diagnosis System for Covid-19

One of the critical tools for early detection and subsequent evaluation of the incidence of lung diseases is chest radiography.

Assistance System for Real-time Polyp Detection

The use of artificial intelligence as an assistive detection method in endoscopy has attracted increasing interest in recent years...