Latest AI and machine learning research in diagnostic radiology for healthcare professionals.
RATIONALE AND OBJECTIVES: Imaging utilization has significantly increased over the last two decades, and is only recently showing signs of moderating. To help healthcare providers identify patients at risk for high imaging utilization, we developed a prediction model to recognize high imaging utilizers based on their initial imaging reports.
The application of Large Language Models (LLMs) to diagnostic decision-making has garnered growing interest. However, existing benchmarks largely focus on textual reasoning or isolated visual question-answering (VQA) tasks, lacking holistic integration of clinical narratives and medical imaging, and thus failing to assess the multimodal diagnostic synthesis capability central to expert clinical ju...
Reliable spatial understanding is an important prerequisite for future medical vision-language systems that aim to support radiological report generat...
Background. Residual cancer burden (RCB) after neoadjuvant chemotherapy (NAC) offers finer prognostic stratification than binary pathologic complete r...
Image inpainting aims to reconstruct missing or corrupted regions of an image while preserving as much as possible, visual and semantic consistency. I...
Building foundation models for medical imaging requires pooling data across institutions, yet privacy regulations prohibit centralized aggregation. Ex...
Large language model (LLM) agents are beginning to automate machine learning engineering (MLE) by coupling planning, code execution, debugging, and em...
The privacy requirements of medical data and its substantial variations across organs and modalities hinder the clinical implementation of medical AI....
Imaging demand is growing faster than the radiology workforce can expand, and reporting backlogs cannot be resolved through training and recruitment a...
Objectives: To evaluate the diagnostic accuracy of a publicly available DenseNet-121 convolutional neural network (TorchXRayVision) for triaging chest...
Reasoning in multimodal large language models (MLLMs) has shown strong promise in medical imaging. However, this reasoning is usually free-form text j...
We developed a standardized, reproducible preprocessing framework for computed tomography (CT) imaging data from multi-institutional repositories such...
Vision-language pre-training (VLP) holds great promise for general-purpose medical AI by leveraging radiology reports as rich textual supervision, yet...
Real-world clinical decision support requires reasoning over heterogeneous and longitudinal patient information rather than answering isolated medical...
Multi-center studies are crucial for advancing medical and radiological research. Data exploration, collaboration discovery, and study progress monito...
The surge in medical imaging has spurred the development of vision-language models (VLMs) to alleviate radiologist workloads. However, clinical deploy...
Masked autoencoders (MAE) have shown great promise in medical image classification. However, the random masking strategy employed by traditional MAEs ...
Long-tailed class distributions are pervasive in multi-class medical datasets and pose significant challenges for deep learning models which typically...
Lung cancer remains one of the leading causes of cancer-related mortality worldwide. Conventional computed tomography (CT) imaging, while essential fo...
Vision-language models trained with contrastive learning on paired medical images and reports show strong zero-shot diagnostic capabilities, yet the e...