Perivolaris Argyrios, Adams-McGavin Chris, Madan Yasmine, Kishibe Teruko, Antoniou Tony, Mamdani Muhammad, Jung James J
Institute of Medical Sciences, University of Toronto, Canada.
St. Michaels Hospital, Unity Health Toronto, Canada.
Future Healthc J. 2024 Aug 17;11(3):100172. doi: 10.1016/j.fhj.2024.100172. eCollection 2024 Sep.
Artificial intelligence (AI) has the potential to improve healthcare quality when thoughtfully integrated into clinical practice. Current evaluations of AI solutions tend to focus solely on model performance. There is a critical knowledge gap in the assessment of AI-clinician interactions. We systematically reviewed existing literature to identify interaction traits that can be used to assess the quality of AI-clinician interactions.
We performed a systematic review of published studies to June 2022 that reported elements of interactions that impacted the relationship between clinicians and AI-enabled clinical decision support systems. Due to study heterogeneity, we conducted a narrative synthesis of the different interaction traits identified from this review. Two study authors categorised the AI-clinician interaction traits based on their shared constructs independently. After the independent categorisation, both authors engaged in a discussion to finalise the categories.
From 34 included studies, we identified 210 interaction traits. The most common interaction traits included usefulness, ease of use, trust, satisfaction, willingness to use and usability. After removing duplicate or redundant traits, 90 unique interaction traits were identified. Unique interaction traits were then classified into seven categories: usability and user experience, system performance, clinician trust and acceptance, impact on patient care, communication, ethical and professional concerns, and clinician engagement and workflow.
We identified seven categories of interaction traits between clinicians and AI systems. The proposed categories may serve as a foundation for a framework assessing the quality of AI-clinician interactions.
人工智能（AI）若能审慎地融入临床实践，便有潜力提升医疗质量。当前对人工智能解决方案的评估往往仅聚焦于模型性能。在评估人工智能与临床医生的互动方面，存在重大的知识空白。我们系统地回顾了现有文献，以确定可用于评估人工智能与临床医生互动质量的互动特征。
我们对截至2022年6月发表的研究进行了系统回顾，这些研究报告了影响临床医生与人工智能支持的临床决策支持系统之间关系的互动要素。由于研究的异质性，我们对本次回顾中确定的不同互动特征进行了叙述性综合。两位研究作者根据共同的构建对人工智能与临床医生的互动特征进行了独立分类。在独立分类之后，两位作者进行了讨论以确定最终的类别。
从34项纳入研究中，我们确定了210个互动特征。最常见的互动特征包括有用性、易用性、信任、满意度、使用意愿和可用性。去除重复或冗余特征后，确定了90个独特的互动特征。然后将独特的互动特征分为七类：可用性和用户体验、系统性能、临床医生的信任和接受度、对患者护理的影响、沟通、伦理和专业问题，以及临床医生的参与度和工作流程。
我们确定了临床医生与人工智能系统之间的七类互动特征。所提出的类别可为评估人工智能与临床医生互动质量的框架奠定基础。
