Fairness in AI for Medical Image Analysis: An Intersectional Approach

This ArXiv paper likely explores how vision-language models can be improved for fairness in medical image disease classification across different demographic groups. The research will be crucial for reducing biases and ensuring equitable outcomes in AI-driven healthcare diagnostics.

• •Investigates fairness issues in medical image analysis using vision-language models.
• •Addresses potential biases across different demographic groups.
• •Aims to improve the reliability and trustworthiness of AI in healthcare.