Artificial intelligence and the challenge of genetic essentialism: interview with artificial intelligences on sickle-cell disease

ABSTRACT Objectives Sickle cell disease (SCD) has historically been associated with populations of African, Mediterranean, Middle Eastern, and Indian descent, often reinforcing genetic essentialism, a deterministic view of genetic inheritance. This study examines how artificial intelligence (AI) models address genetic essentialism in the context of SCD and assesses their potential influence on youth understanding of genetic risk. Methods We conducted semi-structured interviews with three publicly available AI models: ChatGPT-3.5, DeepSeek-V3, and Qwen 2.5-Max. The models were asked five questions regarding SCD definition, hereditary risk estimation, risk assessment for a hypothetical couple, and premarital screening recommendations. Responses were analyzed qualitatively to identify patterns of genetic essentialism, biases, and epistemological simplifications. Results AI models provided scientifically accurate but epidemiologically biased responses. They strongly linked SCD prevalence to ethnicity and geographic origin, reinforcing deterministic narratives. While they acknowledged genetic counseling and environmental factors, their explanations emphasized ancestry as the primary determinant of genetic risk. Moreover, the AI models exhibited a structured response pattern. While the response is scholarly and nuanced, it suggests that an individual’s likelihood of passing on the disease is chiefly determined by their genetic nationality. Conclusion AI-generated health information, while grounded in biomedical knowledge, may inadvertently perpetuate genetic essentialism by emphasizing ethnicity-based risk factors. This has implications for genetic literacy, particularly among young users with limited critical engagement with AI-generated content. Addressing these biases requires the integration of digital health literacy, genetic literacy, and health literacy into educational frameworks to foster a nuanced understanding of genetic risk and disease prevention.