Exploring Biases of Large Language Models in the Field of Mental Health: Comparative Questionnaire Study of the Effect of Gender and Sexual Orientation in Anorexia Nervosa and Bulimia Nervosa Case Vignettes

LLM-generated mental HRQoL estimates for AN and BN case vignettes may be biased by gender, with male cases scoring lower despite no real-world evidence supporting this pattern. This highlights the risk of bias in generative artificial intelligence in the field of mental health. Understanding and mitigating biases related to gender and other factors, such as ethnicity, and socioeconomic status are crucial for responsible use in diagnostics and treatment recommendations.