Latest AI and machine learning research in diagnostic radiology for healthcare professionals.
Artificial intelligence (AI) is emerging as a transformative force in radiology, offering the potential to revolutionize the field by enabling sophisticated analysis of complex radiological data and uncovering previously unknown information in medical images.About a decade after the introduction of clinically applicable AI tools, this article explores the current status, opportunities, and limitat...
In the last decade, advanced AI methods were applied to radiology, providing tools for clinical practice. Regulations across countries are a relevant topic, considering that AI tools must be regarded as medical devices. We describe the regulatory scenarios in the EU, USA, and China. For the EU, we considered the 2017 Medical Device Regulation, including AI tools as "active" medical devices, the 20...
Ocular tumors encompass ocular surface tumors, orbital tumors, and intraocular tumors, characterized by high heterogeneity and complex classifications...
Artificial intelligence (AI) is increasingly shaping radiology, though its integration into paediatric radiology has progressed more slowly due to cha...
Spine magnetic resonance imaging is among the most frequently performed examinations in clinical radiology and places substantial demands on workflow ...
OBJECTIVE: To systematically evaluate current Artificial Intelligence (AI) based approaches for the diagnosis of impacted teeth other than third molar...
The Barcelona Clinic Liver Cancer (BCLC) classification has been the mainstay for prognostic assessment and initial treatment selection in hepatocellu...
Identifying completely unknown individuals is a major challenge in forensic and emergency medicine. Radiology offers a promising solution by using uni...
Modern radiology requires sustained attention, rapid decision-making, and emotional resilience amid increasing imaging volumes, diagnostic complexity,...
Radiology has been profoundly transformed by artificial intelligence (AI) over the past decade, enabling automated detection, enhanced diagnostic accu...
INTRODUCTION: Radiographic imaging is the primary imaging tool for assessing the presence of an abnormality with two main objectives: detection and ch...
BACKGROUND: Artificial intelligence (AI) promises to significantly impact daily radiology practices. Numerous studies have already been conducted that...
Pediatric imaging presents distinct and urgent sustainability challenges, in part driven by its unique subspecialty demands: safeguarding the lifetime...
BACKGROUND: Real-world evaluation of large language models (LLMs) as clinical diagnostic aids is limited by the reliance on static vignettes and retro...
BACKGROUND: Hysteroscopy allows direct inspection of the uterine cavity for many conditions. Despite being widely adopted, its diagnostic accuracy lar...
OBJECTIVES: Artificial intelligence (AI) applications in radiology may improve clinical outcomes, but adoption is hindered by limited health economic ...
Computed tomography (CT) is essential to modern clinical practice but contributes substantially to population radiation exposure, particularly in onco...
BACKGROUND: With the rapid advancement of artificial intelligence (AI) in medical imaging, its application to coronary artery disease (CAD) imaging bi...
PURPOSE: This study aims to evaluate the effect of input format and hyperparameter settings on GPT-5 and explore the contribution of GPT-5 assistance ...
OBJECTIVES: To evaluate the publication outcomes of oral presentations delivered at the European Congress of Radiology (ECR) 2019 and examine factors ...