Improving Medical Diagnostics with Vision-Language Models: Convex Hull-Based Uncertainty Analysis
This addresses reliability concerns for VLMs in critical healthcare diagnostics, but it is incremental as it applies an existing uncertainty method to a new domain.
The paper tackled the problem of uncertainty in vision-language models (VLMs) for healthcare by proposing a convex hull-based analysis on a medical VQA task, finding that the LLM-CXR model shows high uncertainty at higher temperature settings.
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.