Latest AI and machine learning research in diagnostic radiology for healthcare professionals.
OBJECTIVES: This study aims to evaluate the diagnostic performance of an artificial intelligence (AI) algorithm for detection, segmentation, and volumetric quantification of pneumoperitoneum on abdominal CT scans. MATERIALS AND METHODS: We developed and validated a deep learning-based model for automated pneumoperitoneum detection on CT. Multi-center CT imaging series from 2072 patients were colle...
BACKGROUND: In scoliosis imaging education, the traditional lecture-based learning model can lead to low student engagement and present challenges in developing clinical thinking abilities and diagnostic skills. This study aims to evaluate the effectiveness of integrating three-dimensional (3D) printing and artificial intelligence (AI) technologies with traditional teaching methods in scoliosis ed...
INTRODUCTION: The use of Artificial Intelligence (AI), especially Machine learning (ML) and Deep learning (DL), has led to a major shift in medical di...
BACKGROUND: Clinical decision-making requires integrating history, physical examination, laboratory, and imaging data. In the emergency department (ED...
BACKGROUND: Skin neglected tropical diseases (NTDs) pose significant diagnostic and management challenges in resource-limited settings due to constrai...
Despite remarkable advances in transplant pathology, molecular diagnostics, imaging, and biomarker discovery, uncertainty remains an intrinsic feature...
Radiology is rapidly evolving from a service that produces images into a data-centric clinical platform that supports prevention, early diagnosis, and...
RATIONALE AND OBJECTIVES: This study evaluated the efficacy of a combined artificial intelligence (AI)-assisted and traditional teaching model in enha...
Splenic diseases in dogs and cats present significant diagnostic challenges, particularly in differentiating benign from malignant lesions using conve...
RATIONALE AND OBJECTIVES: Although artificial intelligence (AI) clinical trials in medical imaging have grown rapidly, peer-reviewed publication outco...
OBJECTIVE: Artificial intelligence (AI) demonstrates significant potential in medical imaging diagnosis, yet its real-world clinical value requires va...
OBJECTIVE: Artificial intelligence (AI) is increasingly integrated into radiology, but pediatric imaging remains underrepresented in implementation st...
As radiology AI systems move from predeployment validation to routine radiology practice, attention is shifting toward postdeployment monitoring and p...
Artificial intelligence (AI) is poised to transform diagnostic radiology, yet data on its adoption and the perspectives of radiologists in the Middle ...
Machine learning models, especially vision transformers in the domain of medical images, are highly prone to data poisoning attacks, in which a small ...
BACKGROUND: The rising demand for imaging studies, increasing diagnostic complexity, and limited personnel resources are organizational challenges for...
BACKGROUND: Radiology trainees require efficient, accurate, and accessible resources to master complex imaging techniques and identify findings that g...
PURPOSE: This study aimed to evaluate whether a combination of optical coherence tomography (OCT) and OCT angiography (OCTA) parameters could improve ...
OBJECTIVE: To analyze the adherence of Checklist for Artificial Intelligence (AI) in Medical Imaging (CLAIM) in top medical imaging journals. METHODS:...
The sharp rise in medical tomography examinations has created a demand for automated systems that can reliably extract informative features for downst...