Evaluating the Influence of Demographic Identity in the Medical Use of Large Language Models

As large language models (LLMs) are increasingly adopted in medical decision-making, concerns about demographic biases in AIgenerated recommendations remain unaddressed. In this study, we systematically investigate how demographic attributes--specifically race and gender--affect the diagnostic, medication, and treatment decisions of LLMs. Using the MedQA dataset, we construct a controlled evaluation framework comprising 20,000 test cases with systematically varied doctor-patient demographic pairings. We evaluate two LLMs of different scales: Claude 3.5 Sonnet, a highperformance proprietary model, and Llama 3.1-8B, a smaller open-source alternative. Our analysis reveals significant disparities in both accuracy and bias patterns across models and tasks. While Claude 3.5 Sonnet demonstrates higher overall accuracy and more stable predictions, Llama 3.1-8B exhibits greater sensitivity to demographic attributes, particularly in diagnostic reasoning. Notably, we observe the largest accuracy drop when Hispanic patients are treated by White male doctors, underscoring potential risks of bias amplification. These findings highlight the need for rigorous fairness assessments in medical AI and inform strategies to mitigate demographic biases in LLM-driven healthcare applications.