Improving Medical Diagnostics with Vision-Language Models: Convex Hull-Based Uncertainty Analysis

TL;DR

This paper introduces a convex hull-based method to evaluate uncertainty in vision-language models for healthcare, highlighting the importance of uncertainty assessment for reliable medical diagnostics.

Contribution

It presents a novel convex hull approach to quantify uncertainty in VLM responses, specifically applied to medical Visual Question Answering tasks.

Findings

Higher temperature settings increase model uncertainty.

Uncertainty assessment is crucial for trustworthy healthcare applications.

Convex hull method effectively captures response variability.

Abstract

In recent years, vision-language models (VLMs) have been applied to various fields, including healthcare, education, finance, and manufacturing, with remarkable performance. However, concerns remain regarding VLMs' consistency and uncertainty, particularly in critical applications such as healthcare, which demand a high level of trust and reliability. This paper proposes a novel approach to evaluate uncertainty in VLMs' responses using a convex hull approach on a healthcare application for Visual Question Answering (VQA). LLM-CXR model is selected as the medical VLM utilized to generate responses for a given prompt at different temperature settings, i.e., 0.001, 0.25, 0.50, 0.75, and 1.00. According to the results, the LLM-CXR VLM shows a high uncertainty at higher temperature settings. Experimental outcomes emphasize the importance of uncertainty in VLMs' responses, especially in healthcare applications.