AIMC Topic: Radiology
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Lessons learned from RadiologyNET foundation models for transfer learning in medical radiology.
Scientific reports
Jul 1, 2025
Deep learning models require large amounts of annotated data, which are hard to obtain in the medical field, as the annotation process is laborious and depends on expert knowledge. This data scarcity hinders a model's ability to generalise effectivel...
Integrating AI into medical imaging curricula: Insights from UK HEIs.
Radiography (London, England : 1995)
Apr 24, 2025
INTRODUCTION: With artificial intelligence (AI) becoming increasingly integrated into medical imaging, the Health and Care Professions Council (HCPC) updated its Standards of Proficiency for Radiographers in Autumn 2023. These changes require clinici...
Workload of diagnostic radiologists in the foreseeable future based on recent (2024) scientific advances: Updated growth expectations.
European journal of radiology
Apr 7, 2025
PURPOSE: To assess the expected impact of the 2024 medical imaging literature on the workload of diagnostic radiologists.
Longitudinal study on the impact of short-term radiological interpretation training on resting-state brain network hubs.
Trends in neuroscience and education
Apr 5, 2025
Radiological expertise develops through extensive experience in specific imaging modalities. While previous research has focused on long-term learning and neural mechanisms of expertise, the effects of short-term radiological training on resting-stat...
DALL-M: Context-aware clinical data augmentation with large language models.
Computers in biology and medicine
Apr 1, 2025
X-ray images are vital in medical diagnostics, but their effectiveness is limited without clinical context. Radiologists often find chest X-rays insufficient for diagnosing underlying diseases, necessitating the integration of structured clinical fea...
A clinically accessible small multimodal radiology model and evaluation metric for chest X-ray findings.
Nature communications
Apr 1, 2025
Large foundation models show promise in biomedicine but face challenges in clinical use due to performance gaps, accessibility, cost, and lack of scalable evaluation. Here we show that open-source small multimodal models can bridge these gaps in radi...
Multimodal generative AI for medical image interpretation.
Nature
Mar 26, 2025
Accurately interpreting medical images and generating insightful narrative reports is indispensable for patient care but places heavy burdens on clinical experts. Advances in artificial intelligence (AI), especially in an area that we refer to as mul...
Using Optimal Feature Selection and Continuous Learning to Implement Efficient Model Arrays for Predicting Daily Clinical Radiology Workload.
Academic radiology
Mar 25, 2025
RATIONALE AND OBJECTIVE: Clinical workload can fluctuate daily in radiology practice. We sought to design, validate, and implement an efficient and sustainable machine learning model to forecast daily clinical image interpretation workload.
American College of Veterinary Radiology and European College of Veterinary Diagnostic Imaging position statement on artificial intelligence.
Journal of the American Veterinary Medical Association
Mar 19, 2025
The American College of Veterinary Radiology (ACVR) and the European College of Veterinary Diagnostic Imaging (ECVDI) recognize the transformative potential of AI in veterinary diagnostic imaging and radiation oncology. This position statement outlin...
LHR-RFL: Linear Hybrid-Reward-Based Reinforced Focal Learning for Automatic Radiology Report Generation.
IEEE transactions on medical imaging
Mar 17, 2025
Radiology report generation that aims to accurately describe medical findings for given images, is pivotal in contemporary computer-aided diagnosis. Recently, despite considerable progress, current radiology report generation models still struggled t...
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