AIMC Topic: Large Language Models

Clear Filters Showing 361 to 370 of 574 articles

Large Language Model Enhanced Logic Tensor Network for Stance Detection.

Neural networks : the official journal of the International Neural Network Society
Social media platforms, rich in user-generated content, offer a unique perspective on public opinion, making stance detection an essential task in opinion mining. However, traditional deep neural networks for stance detection often suffer from limita...

Evaluating AI proficiency in nuclear cardiology: Large language models take on the board preparation exam.

Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology
BACKGROUND: Previous studies evaluated the ability of large language models (LLMs) in medical disciplines; however, few have focused on image analysis, and none specifically on cardiovascular imaging or nuclear cardiology. This study assesses four LL...

Extracting International Classification of Diseases Codes from Clinical Documentation Using Large Language Models.

Applied clinical informatics
BACKGROUND:  Large language models (LLMs) have shown promise in various professional fields, including medicine and law. However, their performance in highly specialized tasks, such as extracting ICD-10-CM codes from patient notes, remains underexplo...

The added value of including thyroid nodule features into large language models for automatic ACR TI-RADS classification based on ultrasound reports.

Japanese journal of radiology
OBJECTIVE: The ACR Thyroid Imaging, Reporting, and Data System (TI-RADS) uses a score based on ultrasound (US) imaging to stratify the risk of nodule malignancy and recommend appropriate follow-up. This study aims to analyze US reports and explore ho...

Application of NotebookLM, a large language model with retrieval-augmented generation, for lung cancer staging.

Japanese journal of radiology
PURPOSE: In radiology, large language models (LLMs), including ChatGPT, have recently gained attention, and their utility is being rapidly evaluated. However, concerns have emerged regarding their reliability in clinical applications due to limitatio...

AdversaFlow: Visual Red Teaming for Large Language Models with Multi-Level Adversarial Flow.

IEEE transactions on visualization and computer graphics
Large Language Models (LLMs) are powerful but also raise significant security concerns, particularly regarding the harm they can cause, such as generating fake news that manipulates public opinion on social media and providing responses to unethical ...

Towards Dataset-Scale and Feature-Oriented Evaluation of Text Summarization in Large Language Model Prompts.

IEEE transactions on visualization and computer graphics
Recent advancements in Large Language Models (LLMs) and Prompt Engineering have made chatbot customization more accessible, significantly reducing barriers to tasks that previously required programming skills. However, prompt evaluation, especially a...

Large Language Models to Identify Advance Care Planning in Patients With Advanced Cancer.

Journal of pain and symptom management
CONTEXT: Efficiently tracking Advance Care Planning (ACP) documentation in electronic heath records (EHRs) is essential for quality improvement and research efforts. The use of large language models (LLMs) offers a novel approach to this task.

Using large language models (ChatGPT, Copilot, PaLM, Bard, and Gemini) in Gross Anatomy course: Comparative analysis.

Clinical anatomy (New York, N.Y.)
The increasing application of generative artificial intelligence large language models (LLMs) in various fields, including medical education, raises questions about their accuracy. The primary aim of our study was to undertake a detailed comparative ...

Exploring the potential of large language models in identifying metabolic dysfunction-associated steatotic liver disease: A comparative study of non-invasive tests and artificial intelligence-generated responses.

Liver international : official journal of the International Association for the Study of the Liver
BACKGROUND AND AIMS: This study sought to assess the capabilities of large language models (LLMs) in identifying clinically significant metabolic dysfunction-associated steatotic liver disease (MASLD).