Search Tag: radiology AI
2025 10 Nov
Artificial intelligence is now embedded across radiology workflows, supporting tasks that affect clinical decision-making and patient outcomes. Within the European Union, new regulatory obligations apply as the EU AI Act enters force for general-purpose and foundation models, with a transition period of up to two years for high-risk systems such...Read more
2025 10 Nov
Artificial intelligence is now embedded across radiology workflows, supporting tasks that affect clinical decision-making and patient outcomes. Within the European Union, new regulatory obligations apply as the EU AI Act enters force for general-purpose and foundation models, with a transition period of up to two years for high-risk systems such...Read more
2025 10 Nov
Artificial intelligence is now embedded across radiology workflows, supporting tasks that affect clinical decision-making and patient outcomes. Within the European Union, new regulatory obligations apply as the EU AI Act enters force for general-purpose and foundation models, with a transition period of up to two years for high-risk systems such...Read more
2025 25 Oct
Artificial intelligence is moving into routine imaging practice, yet adoption depends on disciplined governance. Teams must show that tools perform on local data, integrate cleanly into workflows and deliver value that lasts. A practical pathway spans four phases: local validation, stepwise deployment, ongoing value assessment and post-deployment...Read more
2025 25 Oct
Artificial intelligence is moving into routine imaging practice, yet adoption depends on disciplined governance. Teams must show that tools perform on local data, integrate cleanly into workflows and deliver value that lasts. A practical pathway spans four phases: local validation, stepwise deployment, ongoing value assessment and post-deployment...Read more
2025 25 Oct
Artificial intelligence is moving into routine imaging practice, yet adoption depends on disciplined governance. Teams must show that tools perform on local data, integrate cleanly into workflows and deliver value that lasts. A practical pathway spans four phases: local validation, stepwise deployment, ongoing value assessment and post-deployment...Read more
2025 25 Oct
Artificial intelligence is moving into routine imaging practice, yet adoption depends on disciplined governance. Teams must show that tools perform on local data, integrate cleanly into workflows and deliver value that lasts. A practical pathway spans four phases: local validation, stepwise deployment, ongoing value assessment and post-deployment...Read more
2025 11 Oct
Artificial intelligence promises faster reporting, greater consistency and streamlined workflows in radiology, yet implementation often falls short. Underperformance on real-world data, workflow disruptions, added costs and clinician distrust recur across settings. Problems arise through the AI product lifecycle, within technical infrastructure...Read more
2025 11 Oct
Artificial intelligence promises faster reporting, greater consistency and streamlined workflows in radiology, yet implementation often falls short. Underperformance on real-world data, workflow disruptions, added costs and clinician distrust recur across settings. Problems arise through the AI product lifecycle, within technical infrastructure...Read more
2025 09 Sep
Radiology reports are fundamental to patient management, with the impression section serving as a concise summary of findings and professional interpretation. These impressions are vital for guiding clinical decisions, yet producing them is often demanding and time-consuming for radiologists. The process requires precision, consistency and the...Read more
2025 09 Sep
Radiology reports are fundamental to patient management, with the impression section serving as a concise summary of findings and professional interpretation. These impressions are vital for guiding clinical decisions, yet producing them is often demanding and time-consuming for radiologists. The process requires precision, consistency and the...Read more
2025 09 Sep
Radiology reports are fundamental to patient management, with the impression section serving as a concise summary of findings and professional interpretation. These impressions are vital for guiding clinical decisions, yet producing them is often demanding and time-consuming for radiologists. The process requires precision, consistency and the...Read more
2025 25 Aug
Radiologists face increasing pressure to deliver rapid and accurate interpretations of a growing volume of medical imaging studies. Deep learning offers an opportunity to enhance diagnostic workflows by linking imaging data with text, streamlining reporting and supporting decision-making. Through recent advances in artificial intelligence, models...Read more
2025 25 Aug
Radiologists face increasing pressure to deliver rapid and accurate interpretations of a growing volume of medical imaging studies. Deep learning offers an opportunity to enhance diagnostic workflows by linking imaging data with text, streamlining reporting and supporting decision-making. Through recent advances in artificial intelligence, models...Read more
2025 24 Aug
Large language models are being integrated into radiology workflows with growing interest, thanks to their ability to perform advanced natural language tasks. These tools support report generation, summarisation, data labelling and clinical decision support. However, their tendency to generate inaccurate or misleading information, often referred...Read more
2025 01 Aug
Radiology report generation aims to automate the conversion of medical images into clinically relevant textual descriptions. This task carries unique challenges compared to general image captioning due to the complexity and specificity of medical language, the need for clinical accuracy and the imbalance in data distribution. In clinical datasets,...Read more
2025 01 Aug
Radiology report generation aims to automate the conversion of medical images into clinically relevant textual descriptions. This task carries unique challenges compared to general image captioning due to the complexity and specificity of medical language, the need for clinical accuracy and the imbalance in data distribution. In clinical datasets,...Read more
2025 06 Jul
Breast cancer is the most frequently diagnosed cancer among women worldwide. Mammographic screening remains a key strategy in early detection, helping reduce mortality and morbidity. However, limitations such as false-positive and false-negative results, overdiagnosis and radiologist workload continue to challenge screening programmes. Furthermore,...Read more
2025 30 May
Radiology plays a critical role in diagnosing thoracic conditions, but its effectiveness depends heavily on the clarity and accuracy of reports. Unfortunately, various sources of error—ranging from speech recognition software limitations to human interpretation biases—pose risks to patient safety and care quality. Traditional solutions, such as...Read more
2025 22 May
Chronic pancreatitis (CP) is a long-standing inflammatory condition that can lead to irreversible damage to the pancreas. This condition is often accompanied by complications such as persistent abdominal pain, pancreatic insufficiency, diabetes and a heightened risk of cancer. Accurate imaging is essential for diagnosis, monitoring and treatment...Read more
2025 04 May
Magnetic resonance imaging (MRI) is central to modern diagnostic radiology due to its non-invasive nature and detailed imaging capabilities. Automating the segmentation of multiple abdominal organs and structures in MRI can significantly enhance diagnostic accuracy and reduce the workload for radiologists. Yet, most existing segmentation tools are...Read more
2025 24 Apr
Accurate labelling of medical imaging data remains one of the most demanding aspects of training artificial intelligence models for diagnostic purposes. Traditionally, this task relies on expert annotations, a process that is both time-consuming and resource-intensive. Radiology reports often contain rich diagnostic insights that could be repurposed...Read more
2025 09 Apr
The Ovarian-Adnexal Reporting and Data System (O-RADS) for MRI provides a structured method for stratifying malignancy risk in adnexal masses. Endorsed by the American College of Radiology, it categorises findings into five levels of risk, aiming to enhance decision-making and communication between radiologists and referring clinicians. Despite its...Read more
2025 09 Apr
The Ovarian-Adnexal Reporting and Data System (O-RADS) for MRI provides a structured method for stratifying malignancy risk in adnexal masses. Endorsed by the American College of Radiology, it categorises findings into five levels of risk, aiming to enhance decision-making and communication between radiologists and referring clinicians. Despite its...Read more
2025 10 Mar
The accuracy of radiology reports is critical to ensuring effective patient care, but the process of proofreading these reports is both time-intensive and prone to human error. Large language models (LLMs) offer a potential solution by automating the detection of common errors. While closed-source LLMs such as GPT-4 have demonstrated strong performance,...Read more
2025 28 Feb
AI-Powered Ultrasound: Transforming Imaging with Practical Innovation Ultrasound technology is advancing rapidly, and the latest systems are integrating practical AI to enhance efficiency, accuracy, and usability. With workforce shortages and growing patient demand, these innovations are reshaping medical imaging for professionals worldwide....Read more
2024 24 Sep
Mammography screening is essential for early detection of breast cancer, significantly improving treatment outcomes and survival rates. Traditionally, double reading—where two radiologists independently review mammograms—is considered the gold standard to enhance diagnostic accuracy. However, with increasing workloads and radiologist shortages,...Read more
2024 24 Sep
Mammography screening is essential for early detection of breast cancer, significantly improving treatment outcomes and survival rates. Traditionally, double reading—where two radiologists independently review mammograms—is considered the gold standard to enhance diagnostic accuracy. However, with increasing workloads and radiologist shortages,...Read more
2024 01 Sep
The healthcare landscape is rapidly transforming with the advent of artificial intelligence (AI) and machine learning (ML) technologies. The U.S. Food and Drug Administration (FDA) has significantly increased the number of AI-enabled medical devices approved for clinical use, recently crossing the milestone of 950 cleared products. Radiology leads...Read more