Implicit gender, racial, and ethnic biases in large language models: an audit study of automated psychiatric diagnoses.

LLMs are being explored as tools to support psychiatric diagnosis, but concerns remain about fairness and reliability. This study evaluated six LLMs using identical psychiatric cases that varied only by gender, race, or ethnicity. The models often produced different diagnoses and reasoning despite unchanged symptoms, reflecting and sometimes amplifying existing biases. Findings highlight risks of overdiagnosis and unequal assessments across groups, underscoring the need for careful oversight before using LLMs in mental health decision-making.