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Radiology Information Systems

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Reshaping free-text radiology notes into structured reports with generative question answering transformers.

Artificial intelligence in medicine
BACKGROUND: Radiology reports are typically written in a free-text format, making clinical information difficult to extract and use. Recently, the adoption of structured reporting (SR) has been recommended by various medical societies thanks to the a...

Integrating and Adopting AI in the Radiology Workflow: A Primer for Standards and Integrating the Healthcare Enterprise (IHE) Profiles.

Radiology
The deployment of artificial intelligence (AI) solutions in radiology practice creates new demands on existing imaging workflow. Accommodating custom integrations creates a substantial operational and maintenance burden. These custom integrations als...

A Responsible Framework for Applying Artificial Intelligence on Medical Images and Signals at the Point of Care: The PACS-AI Platform.

The Canadian journal of cardiology
The potential of artificial intelligence (AI) in medicine lies in its ability to enhance clinicians' capacity to analyse medical images, thereby improving diagnostic precision and accuracy and thus enhancing current tests. However, the integration of...

ChatGPT and assistive AI in structured radiology reporting: A systematic review.

Current problems in diagnostic radiology
INTRODUCTION: The rise of transformer-based large language models (LLMs), such as ChatGPT, has captured global attention with recent advancements in artificial intelligence (AI). ChatGPT demonstrates growing potential in structured radiology reportin...

Advancing Medical Imaging Research Through Standardization: The Path to Rapid Development, Rigorous Validation, and Robust Reproducibility.

Investigative radiology
Artificial intelligence (AI) has made significant advances in radiology. Nonetheless, challenges in AI development, validation, and reproducibility persist, primarily due to the lack of high-quality, large-scale, standardized data across the world. A...

Assessing Laterality Errors in Radiology: Comparing Generative Artificial Intelligence and Natural Language Processing.

Journal of the American College of Radiology : JACR
PURPOSE: We compared the performance of generative artificial intelligence (AI) (Augmented Transformer Assisted Radiology Intelligence [ATARI, Microsoft Nuance, Microsoft Corporation, Redmond, Washington]) and natural language processing (NLP) tools ...

Evaluation of GPT-4 ability to identify and generate patient instructions for actionable incidental radiology findings.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: To evaluate the proficiency of a HIPAA-compliant version of GPT-4 in identifying actionable, incidental findings from unstructured radiology reports of Emergency Department patients. To assess appropriateness of artificial intelligence (A...

PhraseAug: An Augmented Medical Report Generation Model With Phrasebook.

IEEE transactions on medical imaging
Medical report generation is a valuable and challenging task, which automatically generates accurate and fluent diagnostic reports for medical images, reducing workload of radiologists and improving efficiency of disease diagnosis. Fine-grained align...