AIMC Topic: Radiology

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Assessment of MRI technologists in acceptance and willingness to integrate artificial intelligence into practice.

Radiography (London, England : 1995)
INTRODUCTION: The integration of AI in medical imaging has tremendous exponential growth, especially in image production, image processing and image interpretation. It is expected that radiographers working across all imaging modalities have adequate...

A Conference-Friendly, Hands-on Introduction to Deep Learning for Radiology Trainees.

Journal of digital imaging
Artificial or augmented intelligence, machine learning, and deep learning will be an increasingly important part of clinical practice for the next generation of radiologists. It is therefore critical that radiology residents develop a practical under...

AI MSK clinical applications: spine imaging.

Skeletal radiology
Recent investigations have focused on the clinical application of artificial intelligence (AI) for tasks specifically addressing the musculoskeletal imaging routine. Several AI applications have been dedicated to optimizing the radiology value chain ...

Explicability of artificial intelligence in radiology: Is a fifth bioethical principle conceptually necessary?

Bioethics
Recent years have witnessed intensive efforts to specify which requirements ethical artificial intelligence (AI) must meet. General guidelines for ethical AI consider a varying number of principles important. A frequent novel element in these guideli...

Development and web deployment of an automated neuroradiology MRI protocoling tool with natural language processing.

BMC medical informatics and decision making
BACKGROUND: A systematic approach to MRI protocol assignment is essential for the efficient delivery of safe patient care. Advances in natural language processing (NLP) allow for the development of accurate automated protocol assignment. We aim to de...

Understanding artificial intelligence based radiology studies: CNN architecture.

Clinical imaging
Artificial intelligence (AI) in radiology has gained wide interest due to the development of neural network architectures with high performance in computer vision related tasks. As AI based software programs become more integrated into the clinical w...

Artificial intelligence reporting guidelines: what the pediatric radiologist needs to know.

Pediatric radiology
There has been an exponential rise in artificial intelligence (AI) research in imaging in recent years. While the dissemination of study data that has the potential to improve clinical practice is welcomed, the level of detail included in early AI re...

Semi-supervised classification of radiology images with NoTeacher: A teacher that is not mean.

Medical image analysis
Deep learning models achieve strong performance for radiology image classification, but their practical application is bottlenecked by the need for large labeled training datasets. Semi-supervised learning (SSL) approaches leverage small labeled data...

Deciphering musculoskeletal artificial intelligence for clinical applications: how do I get started?

Skeletal radiology
Artificial intelligence (AI) represents a broad category of algorithms for which deep learning is currently the most impactful. When electing to begin the process of building an adequate fundamental knowledge base allowing them to decipher machine le...

Brain MRI Deep Learning and Bayesian Inference System Augments Radiology Resident Performance.

Journal of digital imaging
Automated quantitative and probabilistic medical image analysis has the potential to improve the accuracy and efficiency of the radiology workflow. We sought to determine whether AI systems for brain MRI diagnosis could be used as a clinical decision...