AI Medical Compendium Topic

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Radiology

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Generative pretrained transformer-4, an artificial intelligence text predictive model, has a high capability for passing novel written radiology exam questions.

International journal of computer assisted radiology and surgery
PURPOSE: AI-image interpretation, through convolutional neural networks, shows increasing capability within radiology. These models have achieved impressive performance in specific tasks within controlled settings, but possess inherent limitations, s...

Automatic generation of conclusions from neuroradiology MRI reports through natural language processing.

Neuroradiology
PURPOSE: The conclusion section of a radiology report is crucial for summarizing the primary radiological findings in natural language and essential for communicating results to clinicians. However, creating these summaries is time-consuming, repetit...

Meta-research on reporting guidelines for artificial intelligence: are authors and reviewers encouraged enough in radiology, nuclear medicine, and medical imaging journals?

Diagnostic and interventional radiology (Ankara, Turkey)
PURPOSE: To determine how radiology, nuclear medicine, and medical imaging journals encourage and mandate the use of reporting guidelines for artificial intelligence (AI) in their author and reviewer instructions.

Automated image label extraction from radiology reports - A review.

Artificial intelligence in medicine
Machine Learning models need large amounts of annotated data for training. In the field of medical imaging, labeled data is especially difficult to obtain because the annotations have to be performed by qualified physicians. Natural Language Processi...

A deep learning approach for projection and body-side classification in musculoskeletal radiographs.

European radiology experimental
BACKGROUND: The growing prevalence of musculoskeletal diseases increases radiologic workload, highlighting the need for optimized workflow management and automated metadata classification systems. We developed a large-scale, well-characterized datase...

Proceedings From the 2022 ACR-RSNA Workshop on Safety, Effectiveness, Reliability, and Transparency in AI.

Journal of the American College of Radiology : JACR
Despite the surge in artificial intelligence (AI) development for health care applications, particularly for medical imaging applications, there has been limited adoption of such AI tools into clinical practice. During a 1-day workshop in November 20...

Beyond regulatory compliance: evaluating radiology artificial intelligence applications in deployment.

Clinical radiology
The implementation of artificial intelligence (AI) applications in routine practice, following regulatory approval, is currently limited by practical concerns around reliability, accountability, trust, safety, and governance, in addition to factors s...

Empowering PET: harnessing deep learning for improved clinical insight.

European radiology experimental
This review aims to take a journey into the transformative impact of artificial intelligence (AI) on positron emission tomography (PET) imaging. To this scope, a broad overview of AI applications in the field of nuclear medicine and a thorough explor...

Performance of AI chatbots on controversial topics in oral medicine, pathology, and radiology.

Oral surgery, oral medicine, oral pathology and oral radiology
OBJECTIVES: In this study, we assessed 6 different artificial intelligence (AI) chatbots (Bing, GPT-3.5, GPT-4, Google Bard, Claude, Sage) responses to controversial and difficult questions in oral pathology, oral medicine, and oral radiology.

Image annotation and curation in radiology: an overview for machine learning practitioners.

European radiology experimental
"Garbage in, garbage out" summarises well the importance of high-quality data in machine learning and artificial intelligence. All data used to train and validate models should indeed be consistent, standardised, traceable, correctly annotated, and d...