On Large Visual Language Models for Medical Imaging Analysis: An Empirical Study

TL;DR

This paper empirically evaluates large visual language models' ability to analyze various biomedical images in zero-shot and few-shot settings, highlighting their potential in medical diagnostics.

Contribution

It provides the first comprehensive empirical study on the effectiveness of VLMs for medical imaging analysis across multiple biomedical modalities.

Findings

VLMs perform effectively on biomedical image analysis tasks.

VLMs show robustness in zero-shot and few-shot scenarios.

Potential for VLMs to assist in medical diagnostics.

Abstract

Recently, large language models (LLMs) have taken the spotlight in natural language processing. Further, integrating LLMs with vision enables the users to explore emergent abilities with multimodal data. Visual language models (VLMs), such as LLaVA, Flamingo, or CLIP, have demonstrated impressive performance on various visio-linguistic tasks. Consequently, there are enormous applications of large models that could be potentially used in the biomedical imaging field. Along that direction, there is a lack of related work to show the ability of large models to diagnose the diseases. In this work, we study the zero-shot and few-shot robustness of VLMs on the medical imaging analysis tasks. Our comprehensive experiments demonstrate the effectiveness of VLMs in analyzing biomedical images such as brain MRIs, microscopic images of blood cells, and chest X-rays.