New AI models still reproduce racial and gender stereotypes in medicine
“Large language models have the potential to transform health care but risk exacerbating health disparities if they perpetuate biases,” says lead researcher Joshua Docking, from Flinders University’s College of Medicine and Public Health. Researchers have previously demonstrated potential racial and gender biases in clinical vignettes generated by GPT-4, including overrepresentation of Black patients in stereotypical medical conditions. Since then, next-generation reasoning LLMs have emerged, offering improved reasoning capability and demonstrating superior benchmark performance.
- ▪“Large language models have the potential to transform health care but risk exacerbating health disparities if they perpetuate biases,” says lead researcher Joshua Docking, from Flinders University’s College of Medicine and Public Health.
- ▪Researchers have previously demonstrated potential racial and gender biases in clinical vignettes generated by GPT-4, including overrepresentation of Black patients in stereotypical medical conditions.
- ▪Since then, next-generation reasoning LLMs have emerged, offering improved reasoning capability and demonstrating superior benchmark performance.
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| Canonical URL | https://news.flinders.edu.au/blog/2026/08/09/new-ai-models-still-reproduce-racial-and-gender-stereotypes-in-medicine/ |
| Publication time | Mon, 10 Aug 2026 04:12:06 +0000 |
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Posted on August 9, 2026August 7, 2026 by News Desk New AI models still reproduce racial and gender stereotypes in medicine Stock prohot: Getty Images Relying on Artificial Intelligence (AI) in health care also carries a risk that existing racial and gender stereotypes will be reflected in the medical content it generates. Flinders University researchers have evaluated two next-generation reasoning Large Language Models (LLMs)– o3-mini and DeepSeek-R1 – and found that when asked to describe fictional patients with common medical conditions, these models frequently reproduced racial and gender stereotypes, indicating that advancements in AI reasoning do not inherently improve representational fairness.
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