Latest AI and machine learning research in diagnostic radiology for healthcare professionals.
Artificial intelligence (AI) adoption in radiology has accelerated, but operational maturity has not kept pace. Many organisations still deploy AI as isolated point solutions, leading to fragmented workflows, duplicated integration work, inconsistent governance, unclear accountability, and limited ability to assess value over time. For radiologist-facing clinical AI, these problems are less about ...
Radiology learning and development-encompassing both trainee education and lifelong professional learning-are undergoing rapid transformation driven by sustained growth in imaging volumes, persistent workforce constraints, increasing subspecialization, and evolving hybrid practice models that are reshaping radiologists' work. The resulting pressures increasingly and directly compete with protected...
OBJECTIVE: To evaluate adherence to the Minimum Reporting Items for Clear Evaluation of Accuracy Reports of Large Language Models in Healthcare (MI-CL...
While the graphical user interface (GUI) is fundamental to computer-aided diagnosis (CAD) systems, a significant void exists in the technical literatu...
Chronic Obstructive Pulmonary Disease (COPD) is a significant public health challenge globally, with Asia facing unique burdens due to varying demogra...
Artificial intelligence (AI) is rapidly integrating into clinical radiology, creating a continuous emphasis on the necessity of teaching its principle...
Recently, a structured approach to renal mass characterization known as the Kidney Imaging Reporting and Data System (KI-RADS) was proposed. In that p...
INTRODUCTION: The implementation of imaging features in administrative databases and electronic health records is limited by non-standardized free-tex...
Radiology occupational safety has historically centred on radiation protection. Although radiation safety remains essential, the digital transformatio...
Artificial intelligence (AI) is rapidly integrating into clinical radiology. As primary diagnosticians, radiologists increasingly interpret AI-generat...
BACKGROUND: Artificial intelligence (AI) has revolutionized interventional pulmonology (IP) by enhancing diagnostic accuracy, procedural efficiency, a...
PURPOSE: Artificial intelligence (AI) can support and enhance radiologists in musculoskeletal imaging, but evidence of clinical benefit is still lacki...
ABSTRACT: Artificial intelligence (AI) has become firmly established in radiology, with most current applications relying on task-specific convolution...
The United States National Institutes of Health (NIH) serves as the cornerstone of biomedical research funding. Yet, radiology departments receive a d...
PURPOSE: The increasing integration of Artificial Intelligence (AI) into clinical workflows for medical imaging and radiotherapy presents new opportun...
Artificial intelligence (AI) demonstrates potential throughout the cancer care continuum, with evidence supporting its application in medical imaging ...
Artificial intelligence (AI) is rapidly transforming medical imaging, offering unprecedented opportunities to enhance diagnostic accuracy, streamline ...
BACKGROUND: Accurate preoperative distinction between uterine leiomyoma (UM) and uterine leiomyosarcoma (UMS) remains a major clinical challenge. Misc...
The Reporting and Data Systems (RADS) framework has become a key driver of structured reporting and standardization in radiology. This review summariz...
INTRODUCTION AND AIMS: Periapical radiography is widely used in dental practice, but its diagnostic yield is often compromised by motion artefacts, se...