Latest AI and machine learning research in diagnostic radiology for healthcare professionals.
Artificial intelligence (AI) is rapidly transforming healthcare, but its benefits are not reaching all patients equally. Children remain overlooked with only 17% of FDA-approved medical AI devices labeled for pediatric use. In this work, we demonstrate that this exclusion may stem from a fundamental data gap. Our systematic review of 181 public medical imaging datasets reveals that children repres...
Recent advances in Artificial Intelligence (AI) have started disrupting the healthcare industry, especially medical imaging, and AI devices are increasingly being deployed into clinical practice. Such classifiers have previously demonstrated the ability to discern a range of protected demographic attributes (like race, age, sex) from medical images with unexpectedly high performance, a sensitive t...
Pancreatic cystic lesions (PCLs) are often discovered incidentally on imaging and may progress to pancreatic ductal adenocarcinoma (PDAC). PCLs have a...
Advancements in imaging technology, alongside increasing longevity and co-morbidities, have led to heightened demand for diagnostic radiology services...
Standard clinical LLM benchmarks use multiple-choice vignettes that present all information up front, unlike real encounters where clinicians iterativ...
Large Language Models (LLMs) represent an ever-emerging and rapidly evolving generative artificial intelligence (AI) modality with promising developme...
Echocardiography serves as a cornerstone of cardiovascular diagnostics through multiple standardized imaging views. While recent AI foundation models ...
Radiology residents require timely, personalized feedback to develop accurate image analysis and reporting skills. Increasing clinical workload often ...
Artificial intelligence (AI) is rapidly transforming healthcare practice, with growing evidence supporting its use in diagnosis, prognosis, treatment ...
Medical imaging has been crucial in the diagnostics of pulmonary diseases and the use of chest CT scans is a fundamental diagnostic tool in lung cance...
In the field of medical imaging equipment, fault diagnosis plays a vital role in guaranteeing stable operation and prolonging service life. Traditiona...
The complexity of stacked imaging and the massive number of radiographs make writing radiology reports complex and inefficient. Even highly experien...
Accurate identification and localisation of brain tumours from medical images remain challenging due to tumour variability and structural complexity...
Current transferability estimation methods designed for natural image datasets are often suboptimal in medical image classification. These methods p...
Multimodal large language models (MLLMs) hold significant potential in the medical field, but their capabilities are often limited by insufficient d...
Self-supervised learning (SSL) has emerged as a promising paradigm in medical imaging, addressing the chronic challenge of limited labeled data in h...
Medical Large Language Models (MLLMs) have demonstrated potential in healthcare applications, yet their propensity for hallucinations -- generating ...
Background: Recent advances in large language models highlight the need for high-quality multilingual medical datasets. While Japan leads globally i...
Advances in imaging technologies have revolutionised the medical imaging and healthcare sectors, leading to the widespread adoption of PACS for the ...
We introduce RadA-BenchPlat, an evaluation platform that benchmarks the performance of large language models (LLMs) act as agent cores in radiology ...