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Radiology

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Large language models as an academic resource for radiologists stepping into artificial intelligence research.

Current problems in diagnostic radiology
BACKGROUND: Radiologists increasingly use artificial intelligence (AI) to enhance diagnostic accuracy and optimize workflows. However, many lack the technical skills to effectively apply machine learning (ML) and deep learning (DL) algorithms, limiti...

Inclusive AI for radiology: Optimising ChatGPT-4 with advanced prompt engineering.

Clinical imaging
This letter responds to the article "Encouragement vs. liability: How prompt engineering influences ChatGPT-4's radiology exam performance," offering additional perspectives on optimising ChatGPT-4 for Radiology applications. While the study highligh...

Artificial intelligence in emergency and trauma radiology: ASER AI/ML expert panel Delphi consensus statement on research guidelines, practices, and priorities.

Emergency radiology
BACKGROUND: Emergency/trauma radiology artificial intelligence (AI) is maturing along all stages of technology readiness, with research and development (R&D) ranging from data curation and algorithm development to post-market monitoring and retrainin...

AI in Dental Radiology-Improving the Efficiency of Reporting With ChatGPT: Comparative Study.

Journal of medical Internet research
BACKGROUND: Structured and standardized documentation is critical for accurately recording diagnostic findings, treatment plans, and patient progress in health care. Manual documentation can be labor-intensive and error-prone, especially under time c...

Large Language Model Ability to Translate CT and MRI Free-Text Radiology Reports Into Multiple Languages.

Radiology
Background High-quality translations of radiology reports are essential for optimal patient care. Because of limited availability of human translators with medical expertise, large language models (LLMs) are a promising solution, but their ability to...

Assessing Large Language Models for Oncology Data Inference From Radiology Reports.

JCO clinical cancer informatics
PURPOSE: We examined the effectiveness of proprietary and open large language models (LLMs) in detecting disease presence, location, and treatment response in pancreatic cancer from radiology reports.

RADHawk-an AI-based knowledge recommender to support precision education, improve reporting productivity, and reduce cognitive load.

Pediatric radiology
BACKGROUND: Using artificial intelligence (AI) to augment knowledge is key to establishing precision education in modern radiology training. Our department has developed a novel AI-derived knowledge recommender, the first reported precision education...

Accuracy of radiologists and radiology residents in detection of paediatric appendicular fractures with and without artificial intelligence.

BMJ health & care informatics
OBJECTIVES: We aim to evaluate the accuracy of radiologists and radiology residents in the detection of paediatric appendicular fractures with and without the help of a commercially available fracture detection artificial intelligence (AI) solution i...

Leveraging Transformers-based models and linked data for deep phenotyping in radiology.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Despite significant investments in the normalization and the standardization of Electronic Health Records (EHRs), free text is still the rule rather than the exception in clinical notes. The use of free text has implications...

Leveraging Large Language Models in Radiology Research: A Comprehensive User Guide.

Academic radiology
Large Language Models (LLMs) such as ChatGPT have been increasingly integrated into radiology research, revolutionizing the research landscape. The Radiology Research Alliance (RRA) of the Association for Academic Radiology (AAR) has convened a Task ...