Core Facility Digital Medicine and Interoperability, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany.
Institute for Medical Informatics, Charité - Universitätsmedizin Berlin, Berlin, Germany.
J Med Internet Res. 2023 Jun 22;25:e41089. doi: 10.2196/41089.
Resources are increasingly spent on artificial intelligence (AI) solutions for medical applications aiming to improve diagnosis, treatment, and prevention of diseases. While the need for transparency and reduction of bias in data and algorithm development has been addressed in past studies, little is known about the knowledge and perception of bias among AI developers.
This study's objective was to survey AI specialists in health care to investigate developers' perceptions of bias in AI algorithms for health care applications and their awareness and use of preventative measures.
A web-based survey was provided in both German and English language, comprising a maximum of 41 questions using branching logic within the REDCap web application. Only the results of participants with experience in the field of medical AI applications and complete questionnaires were included for analysis. Demographic data, technical expertise, and perceptions of fairness, as well as knowledge of biases in AI, were analyzed, and variations among gender, age, and work environment were assessed.
A total of 151 AI specialists completed the web-based survey. The median age was 30 (IQR 26-39) years, and 67% (101/151) of respondents were male. One-third rated their AI development projects as fair (47/151, 31%) or moderately fair (51/151, 34%), 12% (18/151) reported their AI to be barely fair, and 1% (2/151) not fair at all. One participant identifying as diverse rated AI developments as barely fair, and among the 2 undefined gender participants, AI developments were rated as barely fair or moderately fair, respectively. Reasons for biases selected by respondents were lack of fair data (90/132, 68%), guidelines or recommendations (65/132, 49%), or knowledge (60/132, 45%). Half of the respondents worked with image data (83/151, 55%) from 1 center only (76/151, 50%), and 35% (53/151) worked with national data exclusively.
This study shows that the perception of biases in AI overall is moderately fair. Gender minorities did not once rate their AI development as fair or very fair. Therefore, further studies need to focus on minorities and women and their perceptions of AI. The results highlight the need to strengthen knowledge about bias in AI and provide guidelines on preventing biases in AI health care applications.
为了提高疾病的诊断、治疗和预防水平，越来越多的资源被投入到医疗人工智能（AI）解决方案中。尽管过去的研究已经解决了数据和算法开发中透明度和减少偏差的需求，但对于 AI 开发者对偏差的认识和看法却知之甚少。
本研究旨在调查医疗保健领域的 AI 专家，以调查开发者对医疗保健应用 AI 算法的偏差的看法，以及他们对偏差的认识和使用预防措施的情况。
采用基于网络的调查，分别以德语和英语两种语言提供，使用 REDCap 网络应用程序中的分支逻辑，最多包含 41 个问题。只有具有医学 AI 应用领域经验且完成调查问卷的参与者的结果才被纳入分析。分析了人口统计学数据、技术专长以及对公平性的看法，以及对 AI 中偏差的认识，并评估了性别、年龄和工作环境之间的差异。
共有 151 名 AI 专家完成了基于网络的调查。中位数年龄为 30 岁（IQR 26-39 岁），67%（101/151）的受访者为男性。三分之一的人认为他们的 AI 开发项目是公平的（47/151，31%）或中等公平的（51/151，34%），12%（18/151）的人报告他们的 AI 几乎是公平的，1%（2/151）的人完全不公平。一位自认为是多样化的参与者认为 AI 开发项目几乎是公平的，而在 2 位未定义性别的参与者中，AI 开发项目分别被评为中等公平或公平。受访者选择的偏差原因包括缺乏公平的数据（90/132，68%）、指南或建议（65/132，49%）或知识（60/132，45%）。一半的受访者（83/151，55%）仅使用来自 1 个中心的图像数据（76/151，50%），35%（53/151）仅使用国家数据。
本研究表明，AI 整体偏差的感知是中等公平的。性别少数群体从未将他们的 AI 开发评为公平或非常公平。因此，需要进一步研究关注少数群体和女性以及他们对 AI 的看法。结果强调了需要加强对 AI 偏差的认识，并提供关于预防 AI 医疗保健应用中偏差的指南。
