OBJECTIVE: To standardize and objectivize treatment response assessment in oncology, guidelines have been proposed that are driven by radiological measurements, which are typically communicated in free-text reports defying automated processing. We st...
Artificial intelligence (AI) is rapidly transforming radiology, with applications spanning disease detection, lesion segmentation, workflow optimization, and report generation. As these tools become more integrated into clinical practice, new concern...
High-quality segmentation is important for AI-driven radiological research and clinical practice, with the potential to play an even more prominent role in the future. As medical imaging advances, accurately segmenting anatomical and pathological str...
The integration of machine-learning technologies into radiology practice has the potential to significantly enhance diagnostic workflows and patient care. However, the successful deployment and maintenance of medical machine-learning (MedML) systems ...
Journal of magnetic resonance imaging : JMRI
Nov 1, 2025
This narrative review focuses on the integration of large language models (LLMs), such as GPT-4 and Gemini, into breast imaging. LLMs excel in understanding, processing, and generating human-like text, with potential applications ranging widely from ...
RATIONALE AND OBJECTIVES: Chat generative pre-trained transformer (ChatGPT) is a generative artificial intelligence chatbot based on a LLM at the forefront of technological development with promising applications in medical education. This study aims...
The Society of Thoracic Radiology (STR) membership enthusiastically embraced the launch of its mentorship program, with peaks in participation and engagement after annual meetings and during the COVID pandemic. The program provides a valuable resourc...
RATIONALE AND OBJECTIVES: This study evaluates the performance, cost, and processing time of OpenAI's reasoning large language models (LLMs) (o1-preview, o1-mini) and their base models (GPT-4o, GPT-4o-mini) on Japanese radiology board examination que...
BACKGROUND: Digital twins (DTs) represent a transformative advancement in radiology, integrating multimodal imaging, artificial intelligence (AI), and computational modeling to create dynamic, patient-specific virtual representations.
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