用凸包法分析医学视觉问答模型的不确定性，提升诊断可信度。
Improving Medical Diagnostics with Vision-Language Models: Convex Hull-Based Uncertainty Analysis
- 通过凸包法分析不同温度下模型输出分布，评估不确定性
- 高温度设置下模型响应不确定性显著升高
- 为医疗AI决策提供可解释的可靠性判断依据
近年来，视觉语言模型（VLMs）在医疗、教育、金融和制造等领域表现出色。然而，在医疗等关键应用中，其一致性和不确定性仍存疑虑，需更高信任度。本文提出一种基于凸包的方法，用于评估医疗视觉问答（VQA）任务中VLM响应的不确定性。选用LLM-CXR模型在不同温度设置（0.001、0.25、0.50、0.75、1.00）下生成回答。实验表明，当温度升高时，模型输出的不确定性显著增加。结果强调了在医疗场景中量化和理解模型不确定性的必要性。
原文摘要 · Abstract (English)
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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