While computer vision has achieved tremendous success with multimodal encoding and
direct textual interaction with images via chat-based large language models, similar …

This study introduces an evaluation framework for multimodal models in medical imaging
diagnostics. We developed a pipeline incorporating data preprocessing, model inference …

Influenced by ChatGPT, artificial intelligence (AI) large models have witnessed a global
upsurge in large model research and development. As people enjoy the convenience by this …

The advent of Large Language Models (LLMs) has sparked considerable interest in the
medical image domain, as they can generalize to multiple tasks and offer outstanding …

Background Studies have explored the application of multimodal large language models
(LLMs) in radiologic differential diagnosis. Yet, how different multimodal input combinations …

Background Large language models have already demonstrated potential in medical text
processing. GPT-4V, a large vision-language model from OpenAI, has shown potential for …

Inspired by the success of large language models (LLMs), there is growing research interest
in developing LLMs in the medical domain to assist clinicians. However, for hospitals, using …

Abstract Recent advancements in Computer Assisted Diagnosis have shown promising
performance in medical imaging tasks, particularly in chest X-ray analysis. However, the …

The innovation of large language models (LLMs) has widened possibilities for renovating
healthcare education through AI-powered learning resources, such as chatbots. This …

Federated learning (FL) enables collaborative learning across multiple biomedical data silos
with multimodal foundation models while preserving privacy. Due to the heterogeneity in …