Tulk Jesso Stephanie, Kelliher Aisling, Sanghavi Harsh, Martin Thomas, Henrickson Parker Sarah
Fralin Biomedical Research Institute, Virginia Tech, Roanoke, VA, United States.
Institute for Creativity, Arts, and Technology, Blacksburg, VA, United States.
Front Psychol. 2022 Apr 7;13:830345. doi: 10.3389/fpsyg.2022.830345. eCollection 2022.
The application of machine learning (ML) and artificial intelligence (AI) in healthcare domains has received much attention in recent years, yet significant questions remain about how these new tools integrate into frontline user workflow, and how their design will impact implementation. Lack of acceptance among clinicians is a major barrier to the translation of healthcare innovations into clinical practice. In this systematic review, we examine when and how clinicians are consulted about their needs and desires for clinical AI tools. Forty-five articles met criteria for inclusion, of which 24 were considered design studies. The design studies used a variety of methods to solicit and gather user feedback, with interviews, surveys, and user evaluations. Our findings show that tool designers consult clinicians at various but inconsistent points during the design process, and most typically at later stages in the design cycle (82%, 19/24 design studies). We also observed a smaller amount of studies adopting a human-centered approach and where clinician input was solicited throughout the design process (22%, 5/24). A third (15/45) of all studies reported on clinician trust in clinical AI algorithms and tools. The surveyed articles did not universally report validation against the "gold standard" of clinical expertise or provide detailed descriptions of the algorithms or computational methods used in their work. To realize the full potential of AI tools within healthcare settings, our review suggests there are opportunities to more thoroughly integrate frontline users' needs and feedback in the design process.
近年来，机器学习（ML）和人工智能（AI）在医疗保健领域的应用备受关注，但关于这些新工具如何融入一线用户工作流程以及其设计将如何影响实施，仍存在重大问题。临床医生缺乏接受度是医疗创新转化为临床实践的主要障碍。在这项系统评价中，我们研究了何时以及如何就临床人工智能工具征求临床医生的需求和期望。45篇文章符合纳入标准，其中24篇被视为设计研究。这些设计研究使用了多种方法来征求和收集用户反馈，包括访谈、调查和用户评估。我们的研究结果表明，工具设计师在设计过程中的不同但不一致的阶段征求临床医生的意见，最常见的是在设计周期的后期阶段（82%，19/24项设计研究）。我们还观察到较少的研究采用以人为本的方法，并且在整个设计过程中征求临床医生的意见（22%，5/24）。所有研究中有三分之一（15/45）报告了临床医生对临床人工智能算法和工具的信任情况。所调查的文章并未普遍报告针对临床专业知识“金标准”的验证情况，也未提供其工作中使用的算法或计算方法的详细描述。为了在医疗环境中充分发挥人工智能工具的潜力，我们的综述表明，在设计过程中有机会更全面地整合一线用户的需求和反馈。
