Imagine an AI doctor that offers diagnoses and treatment plans, potentially revolutionizing healthcare access. But what if this AI doctor harbors hidden biases, leading to unequal care? This critical question is at the heart of new research exploring how to ensure fairness in medical Large Language Models (LLMs). One of the biggest challenges is evaluating these complex AI systems for bias. Traditional methods rely on manually crafted scenarios, which are time-consuming and limited in scope. This new research introduces a method to automatically generate diverse and realistic medical scenarios, drawing directly from evidence-based medical literature. This approach tackles several key issues: First, it ensures the scenarios are grounded in real medical knowledge, minimizing the risk of the AI hallucinating or fabricating information. Second, it checks for pre-existing relationships between patient characteristics (like race or gender) and health outcomes, so the evaluation focuses on true bias rather than justified differences in care. This automated approach allows researchers to test medical LLMs on a much larger and more diverse set of scenarios than previously possible. Early experiments show promising results. The generated scenarios effectively revealed biases in several medical LLMs, highlighting the urgent need for fairness interventions. This research is a crucial step towards ensuring that AI doctors provide equitable care for everyone, regardless of their background. However, the work also highlights the ongoing challenges in detecting and mitigating bias in AI, emphasizing the need for continuous improvement and human oversight to ensure responsible AI development in healthcare.