Generative AI and Eating Disorders: Exposing Stereotypes in Image Depictions and Setting a Research Agenda.

OBJECTIVE

As generative AI becomes more widely adopted for mental health support and information, concerns exist about its potential to depict psychiatric illnesses in stereotypical ways. This Spotlight investigates how ChatGPT-5 visually represents eating disorders and whether prompt-based manipulations can generate more inclusive outputs, with the goal of laying a roadmap for future research to ensure these technologies are applied in safe, ethical, and clinically responsive ways.

METHODS

ChatGPT-5 was prompted to generate images depicting anorexia nervosa, bulimia nervosa, binge-eating disorder, and body image difficulties. A standard one-shot prompt asked the model to create an artistic, realistic, fictional image of the specified condition. The model was then re-tasked to generate multiple images per condition using two approaches: (1) standard prompts and (2) prompts explicitly instructing variation in age, gender, ethnicity, and body type. Outputs were visually compared to evaluate whether bias-reducing instructions produced more inclusive and diverse depictions.

RESULTS

Standard prompts produced images that reinforced conventional stereotypes, depicting eating disorders largely through young, White, female figures with either emaciated (for anorexia nervosa) or overweight (for binge-eating disorder and body image difficulties) body types. Minimal change was observed with single bias-reducing prompts. In contrast, generating multiple images with explicit diversity instructions produced noticeably more inclusive representations, featuring variation in age, gender and ethnicity, although some stereotypical features persisted.

CONCLUSION

ChatGPT-5's depictions of eating disorders rely on stereotypical templates by default. However, prompting the model to generate multiple images with explicit demographic diversity instructions improved representational inclusivity.