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

• URL: http://arxiv.org/abs/2412.00056v1
• Date: Sun, 24 Nov 2024 17:49:48 GMT
• Title: Improving Medical Diagnostics with Vision-Language Models: Convex Hull-Based Uncertainty Analysis
• Authors: Ferhat Ozgur Catak, Murat Kuzlu, Taylor Patrick,
• Abstract summary: This paper proposes a novel approach to evaluate uncertainty in vision-language models (VLMs) using a convex hull approach on a healthcare application for Visual Question Answering (VQA)<n>According to the results, the LLM-CXR VLM shows a high uncertainty at higher temperature settings.
• Score: 0.3277163122167434
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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