Latest AI and machine learning research in diagnostic radiology for healthcare professionals.
Developing 3D vision-language models with robust clinical reasoning remains a challenge due to the inherent complexity of volumetric medical imaging, the tendency of models to overfit superficial report patterns, and the lack of interpretability-aware reward designs. In this paper, we propose Med3D-R1, a reinforcement learning framework with a two-stage training process: Supervised Fine-Tuning (SF...
In biomedical engineering, artificial intelligence has become a pivotal tool for enhancing medical diagnostics, particularly in medical image classification tasks such as detecting pneumonia from chest X-rays and breast cancer screening. However, real-world medical datasets frequently exhibit severe class imbalance, where positive samples substantially outnumber negative samples, leading to biased...
Annotating medical data for training AI models is often costly and limited due to the shortage of specialists with relevant clinical expertise. This c...
Federated learning enables collaborative model training across geographically distributed medical centers while preserving data privacy. However, doma...
Promptable segmentation foundation models such as SAM3 have demonstrated strong generalization capabilities through interactive and concept-based prom...
Recent progress in medical vision-language models (VLMs) has achieved strong performance on image-level text-centric tasks such as report generation a...
BACKGROUND: Digital twins (DTs) represent a transformative advancement in radiology, integrating multimodal imaging, artificial intelligence (AI), and...
PURPOSE: This study investigates the causal mechanisms underlying radiology report generation by analyzing how clinical information and prior imaging ...
The integration of artificial intelligence (AI) in medical imaging raises crucial ethical concerns at every stage of its development, from data coll...
The integration of artificial intelligence (AI) in cancer research has significantly advanced radiology, pathology, and multimodal approaches, offerin...
Recent advancements in deep learning for medical image segmentation are often limited by the scarcity of high-quality training data.While diffusion ...
Although there are relatively few diverse, high-quality medical imaging datasets on which to train computer vision artificial intelligence models, eve...
Explainable AI (XAI) methods are gaining prominence in medical imaging, addressing the critical need for transparency and trust in AI-driven diagnosti...
The Churchill Method evolved as an approach to shooting sporting clays; essentially, successfully shooting the clay as it followed its multi-dimension...
Automated radiology report generation (RRG) aims to produce detailed textual reports from clinical imaging, such as computed tomography (CT) scans, ...
Generating radiology reports from CT scans remains a complex task due to the nuanced nature of medical imaging and the variability in clinical docum...
Vision Language Models (VLMs) hold great promise for streamlining labour-intensive medical imaging workflows, yet systematic security evaluations in...
The objective of this study is to map the global scientific competitive landscape in the field of artificial intelligence (AI) medical devices using s...
In medical visual question answering (Med-VQA), achieving accurate responses relies on three critical steps: precise perception of medical imaging d...
In the era of information explosion, efficiently leveraging large-scale unlabeled data while minimizing the reliance on high-quality pixel-level ann...