AIMC Topic: Radiology

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Enhancing Large Language Models with Retrieval-Augmented Generation: A Radiology-Specific Approach.

Radiology. Artificial intelligence
Retrieval-augmented generation (RAG) is a strategy to improve the performance of large language models (LLMs) by providing an LLM with an updated corpus of knowledge that can be used for answer generation in real time. RAG may improve LLM performance...

Impact of ChatGPT and Large Language Models on Radiology Education: Association of Academic Radiology-Radiology Research Alliance Task Force White Paper.

Academic radiology
Generative artificial intelligence, including large language models (LLMs), holds immense potential to enhance healthcare, medical education, and health research. Recognizing the transformative opportunities and potential risks afforded by LLMs, the ...

Optimizing Large Language Models in Radiology and Mitigating Pitfalls: Prompt Engineering and Fine-tuning.

Radiographics : a review publication of the Radiological Society of North America, Inc
Large language models (LLMs) such as generative pretrained transformers (GPTs) have had a major impact on society, and there is increasing interest in using these models for applications in medicine and radiology. This article presents techniques to ...

Increasing Accessibility: Effectiveness of a Remote Artificial Intelligence Education Curriculum for International Medical Graduates.

The clinical teacher
BACKGROUND: Applications of artificial intelligence (AI) in medicine are expanding every year. AI education is crucial to its appropriate use in healthcare; however, most US medical schools lack a dedicated AI curriculum. These resources are sparse f...

Foundation Models in Radiology: What, How, Why, and Why Not.

Radiology
Recent advances in artificial intelligence have witnessed the emergence of large-scale deep learning models capable of interpreting and generating both textual and imaging data. Such models, typically referred to as foundation models (FMs), are train...

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...

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...

Image-Based Generative Artificial Intelligence in Radiology: Comprehensive Updates.

Korean journal of radiology
Generative artificial intelligence (AI) has been applied to images for image quality enhancement, domain transfer, and augmentation of training data for AI modeling in various medical fields. Image-generative AI can produce large amounts of unannotat...

Generating colloquial radiology reports with large language models.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: Patients are increasingly being given direct access to their medical records. However, radiology reports are written for clinicians and typically contain medical jargon, which can be confusing. One solution is for radiologists to provide ...