AIMC Topic: Radiologists

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Closing the loop for AI-ready radiology.

RoFo : Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin
BACKGROUND: In recent years, AI has made significant advancements in medical diagnosis and prognosis. However, the incorporation of AI into clinical practice is still challenging and under-appreciated. We aim to demonstrate a possible vertical integr...

How artificial intelligence is reshaping the autonomy and boundary work of radiologists. A qualitative study.

Sociology of health & illness
The application of artificial intelligence (AI) in medical practice is spreading, especially in technologically dense fields such as radiology, which could consequently undergo profound transformations in the near future. This article aims to qualita...

Utility of machine learning for identifying stapes fixation on ultra-high-resolution CT.

Japanese journal of radiology
PURPOSE: Imaging diagnosis of stapes fixation (SF) is challenging owing to a lack of definite evidence. We developed a comprehensive machine learning (ML) model to identify SF on ultra-high-resolution CT.

Are the Pilots Onboard? Equipping Radiologists for Clinical Implementation of AI.

Journal of digital imaging
The incorporation of artificial intelligence into radiological clinical workflow is on the verge of being realized. To ensure that these tools are effective, measures must be taken to educate radiologists on tool performance and failure modes. Additi...

Deep neural network pulmonary nodule segmentation methods for CT images: Literature review and experimental comparisons.

Computers in biology and medicine
Automatic and accurate segmentation of pulmonary nodules in CT images can help physicians perform more accurate quantitative analysis, diagnose diseases, and improve patient survival. In recent years, with the development of deep learning technology,...

FSTIF-UNet: A Deep Learning-Based Method Towards Automatic Segmentation of Intracranial Aneurysms in Un-Reconstructed 3D-RA.

IEEE journal of biomedical and health informatics
Segmentation of intracranial aneurysms (IAs) is an important step for the diagnosis and treatment of IAs. However, the process by which clinicians manually recognize and localize IAs is overly labor intensive. This study aims to develop a deep-learni...

Fairness of artificial intelligence in healthcare: review and recommendations.

Japanese journal of radiology
In this review, we address the issue of fairness in the clinical integration of artificial intelligence (AI) in the medical field. As the clinical adoption of deep learning algorithms, a subfield of AI, progresses, concerns have arisen regarding the ...

Clinical use of artificial intelligence products for radiology in the Netherlands between 2020 and 2022.

European radiology
OBJECTIVES: To map the clinical use of CE-marked artificial intelligence (AI)-based software in radiology departments in the Netherlands (n = 69) between 2020 and 2022.

Investigating the impact of cognitive biases in radiologists' image interpretation: A scoping review.

European journal of radiology
RATIONALE AND OBJECTIVE: Image interpretation is a fundamental aspect of radiology. The treatment and management of patients relies on accurate and timely imaging diagnosis. However, errors in radiological reports can negatively impact on patient hea...

Deploying Artificial Intelligence for Thoracic Imaging Around the World.

Journal of the American College of Radiology : JACR
PURPOSE: Artificial intelligence (AI) thoracic imaging applications are increasingly being deployed in low- and middle-income countries (LMICs). Radiologists have a critical gatekeeping role to ensure the effective and ethical implementation of AI so...