Assessing the potential of GPT-4 to perpetuate racial and gender biases in health care: a model evaluation study
医学诊断
医疗保健
转化式学习
医疗保健
心理学
医学
护理部
发展心理学
病理
政治学
法学
作者
Travis Zack,Eric Lehman,Mirac Süzgün,Jorge A. Rodriguez,Leo Anthony Celi,Judy Wawira Gichoya,Dan Jurafsky,Peter Szolovits,David W. Bates,Raja-Elie E. Abdulnour,Atul J. Butte,Emily Alsentzer
Large language models (LLMs) such as GPT-4 hold great promise as transformative tools in health care, ranging from automating administrative tasks to augmenting clinical decision making. However, these models also pose a danger of perpetuating biases and delivering incorrect medical diagnoses, which can have a direct, harmful impact on medical care. We aimed to assess whether GPT-4 encodes racial and gender biases that impact its use in health care.