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Radiology

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Ethics of Artificial Intelligence in Radiology: Summary of the Joint European and North American Multisociety Statement.

Journal of the American College of Radiology : JACR
This is a condensed summary of an international multisociety statement on ethics of artificial intelligence (AI) in radiology produced by the ACR, European Society of Radiology, RSNA, Society for Imaging Informatics in Medicine, European Society of M...

Ethics of Artificial Intelligence in Radiology: Summary of the Joint European and North American Multisociety Statement.

Radiology
This is a condensed summary of an international multisociety statement on ethics of artificial intelligence (AI) in radiology produced by the ACR, European Society of Radiology, RSNA, Society for Imaging Informatics in Medicine, European Society of M...

Artificial intelligence in clinical imaging: a health system approach.

Clinical radiology
The development and application of artificial intelligence (AI) to radiology requires an approach that encompasses a health system. The UK government and National Health Service (NHS) are creating an ecosystem to facilitate academic/industrial partne...

Radiologic-Radiomic Machine Learning Models for Differentiation of Benign and Malignant Solid Renal Masses: Comparison With Expert-Level Radiologists.

AJR. American journal of roentgenology
The objective of our study was to compare the performance of radiologicradiomic machine learning (ML) models and expert-level radiologists for differentiation of benign and malignant solid renal masses using contrast-enhanced CT examinations. This ...

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).