AIMC Topic: Large Language Models

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Comparison of ChatGPT-4o, Google Gemini 1.5 Pro, Microsoft Copilot Pro, and Ophthalmologists in the management of uveitis and ocular inflammation: A comparative study of large language models.

Journal francais d'ophtalmologie
PURPOSE: The aim of this study was to compare the latest large language models (LLMs) ChatGPT-4o, Google Gemini 1.5 Pro and Microsoft Copilot Pro developed by three different companies, with each other and with a group of ophthalmologists, to reveal ...

[Technical foundations of large language models].

Radiologie (Heidelberg, Germany)
BACKGROUND: Large language models (LLMs) such as ChatGPT have rapidly revolutionized the way computers can analyze human language and the way we can interact with computers.

Leveraging large language models for knowledge-free weak supervision in clinical natural language processing.

Scientific reports
The performance of deep learning-based natural language processing systems is based on large amounts of labeled training data which, in the clinical domain, are not easily available or affordable. Weak supervision and in-context learning offer partia...

Medical Misinformation in AI-Assisted Self-Diagnosis: Development of a Method (EvalPrompt) for Analyzing Large Language Models.

JMIR formative research
BACKGROUND: Rapid integration of large language models (LLMs) in health care is sparking global discussion about their potential to revolutionize health care quality and accessibility. At a time when improving health care quality and access remains a...

A systematic review of large language model (LLM) evaluations in clinical medicine.

BMC medical informatics and decision making
BACKGROUND: Large Language Models (LLMs), advanced AI tools based on transformer architectures, demonstrate significant potential in clinical medicine by enhancing decision support, diagnostics, and medical education. However, their integration into ...

Using a Longformer Large Language Model for Segmenting Unstructured Cancer Pathology Reports.

JCO clinical cancer informatics
PURPOSE: Many Natural Language Processing (NLP) methods achieve greater performance when the input text is preprocessed to remove extraneous or unnecessary text. A technique known as text segmentation can facilitate this step by isolating key section...

A Future of Self-Directed Patient Internet Research: Large Language Model-Based Tools Versus Standard Search Engines.

Annals of biomedical engineering
PURPOSE: As generalist large language models (LLMs) become more commonplace, patients will inevitably increasingly turn to these tools instead of traditional search engines. Here, we evaluate publicly available LLM-based chatbots as tools for patient...

Virtual Patient Simulations Using Social Robotics Combined With Large Language Models for Clinical Reasoning Training in Medical Education: Mixed Methods Study.

Journal of medical Internet research
BACKGROUND: Virtual patients (VPs) are computer-based simulations of clinical scenarios used in health professions education to address various learning outcomes, including clinical reasoning (CR). CR is a crucial skill for health care practitioners,...

AI-driven evidence synthesis: data extraction of randomized controlled trials with large language models.

International journal of surgery (London, England)
The advancement of large language models (LLMs) presents promising opportunities to enhance evidence synthesis efficiency, particularly in data extraction processes, yet existing prompts for data extraction remain limited, focusing primarily on commo...