Latest AI and machine learning research in diagnostic radiology for healthcare professionals.
RATIONALE AND OBJECTIVES: Artificial intelligence (AI) has rapidly transformed radiology practice, yet structured and practical AI education remains inconsistently integrated into radiology residency training. We developed and implemented a hands-on AI curriculum designed to integrate core computational principles with clinically relevant imaging applications. This study describes the curriculum d...
OBJECTIVE: Radiology reports remain predominantly text-based, requiring clinicians and patients to mentally reconstruct imaging findings. Reports in Medical Illustration (REMIL) represent an emerging approach in which artificial intelligence (AI) generates simplified visual summaries directly from report text. This study aimed to evaluate the feasibility, anatomical accuracy, and clinical utility ...
OBJECTIVE: To describe the technical workflow enabling scalable automated artificial intelligence (AI) monitoring in the first national imaging AI reg...
RATIONALE AND OBJECTIVES: Radiology residency often fails to account for individual differences between residents or provide sufficient exposure to di...
Removing patient-identifying information from medical images is a prerequisite for sharing image data directly, as in public dataset release and open ...
INTRODUCTION: This study aimed to examine the perceptions of Greek radiographers and radiologists on integrating artificial intelligence (AI) in medic...
BACKGROUND AND AIMS: Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies worldwide because most patients are diagnosed...
BACKGROUND AND AIMS: Differentiating odontogenic keratocyst (OKC) from other radiolucent jaw lesions like ameloblastoma is clinically important but ra...
BACKGROUND: Large language models (LLMs) show promise in automatically detecting errors in radiology reports, but their performance remains insufficie...
INTRODUCTION: Fracture misdiagnosis is a common diagnostic error in emergency departments (EDs) and minor injury units (MIUs), leading to poor patient...
INTRODUCTION: A 3D interactive report is a state-of-the-art artificial intelligence (AI) tool that integrates a patient's imaging history into an intu...
As patients increasingly access radiology reports through electronic portals, imaging reports are no longer private technical communications between c...
RATIONALE AND OBJECTIVES: Hospital-radiology joint ventures (JVs) are forming at an accelerating pace as health systems seek to recapture outpatient i...
Maintaining the quality and consistency of radiology reports has become increasingly challenging with the growing volume of imaging examinations. This...
Large language models (LLMs) have been rapidly adopted in healthcare since 2022; however, field-level trends and specialty differences remain poorly c...
INTRODUCTION: Identify knowledge gaps in applying artificial intelligence in clinical settings, using medical imaging as a primary use case to enhance...
Uveitis encompasses a heterogeneous spectrum of etiologies, including systemic inflammatory diseases, infections, specific ophthalmologic entities, an...
France has seen significant advancements in radiology in recent years, driven by innovations in research and clinical practice. French groups have dev...
Liver metastases represent the most common form of secondary hepatic malignancy and a major determinant of outcomes in oncologic patients. Imaging pla...
Accurate identification and classification of 3D mesh data in medical imaging are crucial for various clinical and research applications, including su...