ArticleFrontiers in public health2026
Same child, different risk: demographic bias in childhood obesity attribution by large language models.
Article in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Large language models (LLMs) are increasingly consulted for pediatric health information, yet their demographic biases remain unsystematically evaluated in pediatric contexts. Objectives: To assess bias and variability in childhood obesity risk attribution across seven LLMs (ChatGPT, Claude, DeepSeek, Gemini, GLM, Grok, and Qwen), spanning both Western and Chinese-origin developers; all prompts, including those submitted to the Chinese-origin models, were in English only. Methods: A structured prompt-based experimental design was employed across six clinical domains (general obesity risk, dietary pattern, physical activity, sleep, mental health, and genetic predisposition) and six demographic comparison dimensions (sex, three race/ethnicity pairings, socioeconomic status, and urban-rural residence). Seventy-eight unique prompts were submitted to each model in triplicate, yielding 1,638 outputs. Neutral prompts were scored on a five-dimension binary rubric (accuracy, representation, stigmatizing/harmful language, social determinants, cultural fit); comparative prompts were coded for directional risk attribution. Results: Claude achieved the highest neutral prompt composite score (mean 3.00 ± 0.91) and GLM the lowest (1.44 ± 0.51); between-model differences were statistically significant (Kruskal-Wallis Conclusions: Publicly accessible English-language web-interface outputs from current LLMs showed systematic demographic patterns in pediatric obesity risk attribution, supporting the need for pre-deployment and post-deployment bias auditing before clinical or consumer health use.
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