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Radiology

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Large Language Models with Vision on Diagnostic Radiology Board Exam Style Questions.

Academic radiology
RATIONALE AND OBJECTIVES: The expansion of large language models to process images offers new avenues for application in radiology. This study aims to assess the multimodal capabilities of contemporary large language models, which allow analysis of i...

Performance of Multimodal Large Language Models in Japanese Diagnostic Radiology Board Examinations (2021-2023).

Academic radiology
RATIONALE AND OBJECTIVES: To evaluate the performance of various multimodal large language models (LLMs) in the Japanese Diagnostic Radiology Board Examinations (JDRBE) both with and without images.

Impact of ChatGPT and Large Language Models on Radiology Education: Association of Academic Radiology-Radiology Research Alliance Task Force White Paper.

Academic radiology
Generative artificial intelligence, including large language models (LLMs), holds immense potential to enhance healthcare, medical education, and health research. Recognizing the transformative opportunities and potential risks afforded by LLMs, the ...

Avoiding missed opportunities in AI for radiology.

International journal of computer assisted radiology and surgery
PURPOSE: In the last decade, the development of Deep Learning and its variants, based on the application of artificial neural networks, has reinvigorated Artificial Intelligence (AI). As a result, many new applications of AI in medicine, especially R...

Burnout crisis in Chinese radiology: will artificial intelligence help?

European radiology
OBJECTIVES: To assess the correlation between the use of artificial intelligence (AI) software and burnout in the radiology departments of hospitals in China.

Manual data labeling, radiology, and artificial intelligence: It is a dirty job, but someone has to do it.

Magnetic resonance imaging
In this letter to the editor, authors highlight the key role of data labeling in training AI models for medical imaging, discussing the complexities, resource demands, costs, and the relevance of quality control in the labeling process including the ...

Artificial intelligence: a primer for pediatric radiologists.

Pediatric radiology
Artificial intelligence (AI) is increasingly recognized for its transformative potential in radiology; yet, its application in pediatric radiology is relatively limited when compared to the whole of radiology. This manuscript introduces pediatric rad...

Visualizing radiological data bias through persistence images.

Oncotarget
Persistence images, derived from topological data analysis, emerge as a powerful tool for visualizing and mitigating biases in radiological data interpretation and AI model development. This technique transforms complex topological features into stab...

Testing process for artificial intelligence applications in radiology practice.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Artificial intelligence (AI) applications are becoming increasingly common in radiology. However, ensuring reliable operation and expected clinical benefits remains a challenge. A systematic testing process aims to facilitate clinical deployment by c...

Multi-modal large language models in radiology: principles, applications, and potential.

Abdominal radiology (New York)
Large language models (LLMs) and multi-modal large language models (MLLMs) represent the cutting-edge in artificial intelligence. This review provides a comprehensive overview of their capabilities and potential impact on radiology. Unlike most exist...