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

Area of Science:

• Artificial Intelligence
• Computer Vision
• Medical Informatics

Background:

• Vision-language models (VLMs) demonstrate significant potential across diverse sectors, including healthcare.
• However, concerns regarding the consistency and uncertainty of VLM outputs hinder their adoption in critical fields like medicine.

Purpose of the Study:

• To propose and evaluate a novel method for assessing uncertainty in VLM responses within a healthcare context.
• To investigate the relationship between model temperature and response uncertainty using a geometric approach.

Main Methods:

• A convex hull approach was employed to evaluate uncertainty in VLM responses.
• The LLM-CXR model, a medical VLM, was used to generate responses at varying temperature settings.
• Uncertainty was geometrically characterized in feature space.

Main Results:

• The LLM-CXR VLM exhibited increased uncertainty at higher temperature settings.
• This uncertainty was demonstrably quantifiable using the proposed geometric characterization.
• The study highlights the critical need to address uncertainty in medical VLM applications.

Conclusions:

• The convex hull approach provides a valuable method for evaluating VLM uncertainty in healthcare.
• Understanding and managing VLM uncertainty is paramount for ensuring reliability and trust in medical applications.
• Further research into uncertainty quantification in VLMs is essential for safe and effective deployment.