Farlow Janice L, Abouyared Marianne, Rettig Eleni M, Kejner Alexandra, Edwards Heather A, Patel Rusha
Department of Otolaryngology-Head and Neck Surgery Indiana University School of Medicine Indianapolis Indiana USA.
Department of Otolaryngology-Head and Neck Surgery University of California Davis Sacramento California USA.
OTO Open. 2024 Apr 17;8(2):e139. doi: 10.1002/oto2.139. eCollection 2024 Apr-Jun.
Text-to-image artificial intelligence (AI) programs are popular public-facing tools that generate novel images based on user prompts. Given that they are trained from Internet data, they may reflect societal biases, as has been shown for text-to-text large language model programs. We sought to investigate whether 3 common text-to-image AI systems recapitulated stereotypes held about surgeons and other health care professionals. All platforms queried were able to reproduce common aspects of the profession including attire, equipment, and background settings, but there were differences between programs most notably regarding visible race and gender diversity. Thus, historical stereotypes of surgeons may be reinforced by the public's use of text-to-image AI systems, particularly those without procedures to regulate generated output. As AI systems become more ubiquitous, understanding the implications of their use in health care and for health care-adjacent purposes is critical to advocate for and preserve the core values and goals of our profession.
文本到图像的人工智能（AI）程序是面向公众的流行工具，可根据用户提示生成新颖的图像。鉴于它们是基于互联网数据进行训练的，它们可能会反映出社会偏见，就像文本到文本的大语言模型程序所显示的那样。我们试图调查三种常见的文本到图像AI系统是否重现了对外科医生和其他医疗保健专业人员的刻板印象。所有查询的平台都能够重现该职业的常见方面，包括着装、设备和背景设置，但程序之间存在差异，最明显的是在可见的种族和性别多样性方面。因此，公众使用文本到图像AI系统，尤其是那些没有程序来规范生成输出的系统，可能会强化对外科医生的历史刻板印象。随着AI系统变得越来越普遍,了解它们在医疗保健以及与医疗保健相关目的中的使用所带来的影响，对于倡导和维护我们职业的核心价值观和目标至关重要。
