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Updated: Jan 11, 2026

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人工智能生成的模拟患者队列中的人口偏差:与人口普查基准对比的比较分析

Miriam Veenhuizen1, Andrew O'Malley2

  • 1School of Medicine, University of St Andrews, North Haugh, St Andrews, KY16 9TF, UK.

Advances in simulation (London, England)
|November 19, 2025
PubMed
概括

生成型人工智能模型产生人口统计学上狭窄的合成患者队列,缺乏年龄,性别和种族的多样性. 在模拟患者中的这种偏见可能会对医学教育和公平的临床实践产生负面影响.

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Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
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Humans are very diverse and although we share many similarities, we also have many differences. The social groups we belong to help form our identities (Tajfel, 1974). These differences may be difficult for some people to reconcile, which may lead to prejudice toward people who are different. Prejudice is a negative attitude and feeling toward an individual based solely on one’s membership in a particular social group (Allport, 1954; Brown, 2010). Prejudice is common against people who...
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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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