AIMC Topic: Radiology

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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.

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...

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 ...

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...

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.

Collaboration between clinicians and vision-language models in radiology report generation.

Nature medicine
Automated radiology report generation has the potential to improve patient care and reduce the workload of radiologists. However, the path toward real-world adoption has been stymied by the challenge of evaluating the clinical quality of artificial i...

ChatGPT and radiology report: potential applications and limitations.

La Radiologia medica
Large language models like ChatGPT, with their growing accessibility, are attracting increasing interest within the artificial intelligence medical field, particularly in the analysis of radiology reports. These present a valuable opportunity to expl...

Deployment of Artificial Intelligence in Radiology: Strategies for Success.

AJR. American journal of roentgenology
Radiology, as a highly technical and information-rich medical specialty, is well suited for artificial intelligence (AI) product development, and many U.S. FDA-cleared AI medical devices are authorized for uses within the specialty. In this Clinical ...