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Radiology

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Deep learning in generating radiology reports: A survey.

Artificial intelligence in medicine
Substantial progress has been made towards implementing automated radiology reporting models based on deep learning (DL). This is due to the introduction of large medical text/image datasets. Generating radiology coherent paragraphs that do more than...

Attitudes Toward Artificial Intelligence Among Radiologists, IT Specialists, and Industry.

Academic radiology
OBJECTIVES: We investigated the attitudes of radiologists, information technology (IT) specialists, and industry representatives on artificial intelligence (AI) and its future impact on radiological work.

Implementation of eHealth and AI integrated diagnostics with multidisciplinary digitized data: are we ready from an international perspective?

European radiology
Digitization of medicine requires systematic handling of the increasing amount of health data to improve medical diagnosis. In this context, the integration of the versatile diagnostic information, e.g., from anamnesis, imaging, histopathology, and c...

Understanding artificial intelligence based radiology studies: What is overfitting?

Clinical imaging
Artificial intelligence (AI) is a broad umbrella term used to encompass a wide variety of subfields dedicated to creating algorithms to perform tasks that mimic human intelligence. As AI development grows closer to clinical integration, radiologists ...

Exploring the Role of Artificial Intelligence in an Emergency and Trauma Radiology Department.

Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes
Emergency and trauma radiologists, emergency department's physicians and nurses, researchers, departmental leaders, and health policymakers have attempted to discover efficient approaches to enhance the provision of quality patient care. There are in...

Machine Learning for Automation of Radiology Protocols for Quality and Efficiency Improvement.

Journal of the American College of Radiology : JACR
PURPOSE: The aim of this study was to enhance multispecialty CT and MRI protocol assignment quality and efficiency through development, testing, and proposed workflow design of a natural language processing (NLP)-based machine learning classifier.

Deep learning in gastric tissue diseases: a systematic review.

BMJ open gastroenterology
BACKGROUND: In recent years, deep learning has gained remarkable attention in medical image analysis due to its capacity to provide results comparable to specialists and, in some cases, surpass them. Despite the emergence of deep learning research on...

Interpretable Artificial Intelligence: Why and When.

AJR. American journal of roentgenology
The purpose of this article is to discuss the problem of interpretability of artificial intelligence (AI) and highlight the need for continuing scientific discovery using AI algorithms to deal with medical big data. A plethora of AI algorithms are ...