This research introduces a systematic approach to detect and evaluate bias patterns in Large Language Models designed for medical applications.

• Creates a comprehensive taxonomy of potential bias types in medical AI
• Develops a scalable evaluation framework using medical knowledge graphs and ontologies
• Identifies specific patterns of bias across different medical conditions and patient demographics
• Proposes practical strategies to mitigate unfair treatment risks

Why it matters: As medical AI adoption grows, understanding and addressing bias is critical for patient safety, regulatory compliance, and ethical deployment of these powerful tools in healthcare settings.

Enabling Scalable Evaluation of Bias Patterns in Medical LLMs