AIMC Topic: Radiology

Clear Filters Showing 531 to 540 of 829 articles

Essential Elements of Natural Language Processing: What the Radiologist Should Know.

Academic radiology
Natural language is ubiquitous in the workflow of medical imaging. Radiologists create and consume free text in their daily work, some of which can be amenable to enhancements through automatic processing. Recent advancements in deep learning and "ar...

A validated natural language processing algorithm for brain imaging phenotypes from radiology reports in UK electronic health records.

BMC medical informatics and decision making
BACKGROUND: Manual coding of phenotypes in brain radiology reports is time consuming. We developed a natural language processing (NLP) algorithm to enable automatic identification of brain imaging in radiology reports performed in routine clinical pr...

Interactive NLP in Clinical Care: Identifying Incidental Findings in Radiology Reports.

Applied clinical informatics
BACKGROUND: Despite advances in natural language processing (NLP), extracting information from clinical text is expensive. Interactive tools that are capable of easing the construction, review, and revision of NLP models can reduce this cost and impr...

Artificial intelligence in radiology: the ecosystem essential to improving patient care.

Clinical imaging
The rapid development of artificial intelligence (AI) has led to its widespread use in multiple industries, including healthcare. AI has the potential to be a transformative technology that will significantly impact patient care. Particularly, AI has...

The use and performance of artificial intelligence applications in dental and maxillofacial radiology: A systematic review.

Dento maxillo facial radiology
OBJECTIVES: To investigate the current clinical applications and diagnostic performance of artificial intelligence (AI) in dental and maxillofacial radiology (DMFR).

Current applications and future directions of deep learning in musculoskeletal radiology.

Skeletal radiology
Deep learning with convolutional neural networks (CNN) is a rapidly advancing subset of artificial intelligence that is ideally suited to solving image-based problems. There are an increasing number of musculoskeletal applications of deep learning, w...

Artificial intelligence for precision education in radiology.

The British journal of radiology
In the era of personalized medicine, the emphasis of health care is shifting from populations to individuals. Artificial intelligence (AI) is capable of learning without explicit instruction and has emerging applications in medicine, particularly rad...

Bending the Artificial Intelligence Curve for Radiology: Informatics Tools From ACR and RSNA.

Journal of the American College of Radiology : JACR
Artificial intelligence (AI) will reshape radiology over the coming years. The radiology community has a strong history of embracing new technology for positive change, and AI is no exception. As with any new technology, rapid, successful implementat...