Objective: The growing use of large language models (LLMs) stimulates a need for a
comprehensive review of their applications and outcomes in mental health care contexts …

Recent advancements in large language models (LLMs) have transformed the field of
question answering (QA). However, evaluating LLMs in the medical field is challenging due …

Medical vision-language models (VLMs) combine computer vision and natural language
processing to analyze visual and textual medical data. Our paper reviews recent …

To address challenges in the digital economy's landscape of digital intelligence, large
language models (LLMs) have been developed. Improvements in computational power and …

By leveraging the retrieval of information from external knowledge databases, Large
Language Models (LLMs) exhibit enhanced capabilities for accomplishing many knowledge …

In the complex and multidimensional field of medicine, multimodal data are prevalent and
crucial for informed clinical decisions. Multimodal data span a broad spectrum of data types …

With the advent and widespread deployment of Multimodal Large Language Models
(MLLMs), the imperative to ensure their safety has become increasingly pronounced …

Objective The objective of this study is to systematically examine the efficacy of both
proprietary (GPT-3.5, GPT-4) and open-source large language models (LLMs)(LLAMA 7B …

Large language model (LLM) based agents have recently attracted much attention from the
research and industry communities. Compared with original LLMs, LLM-based agents are …

The incorporation of Large Language Models (LLMs) in healthcare marks a significant
advancement. However, the application has predominantly been limited to discriminative …