背景:直接面向消费者(DTC)的医疗保健人工智能(AI)应用程序具有弥合医疗保健资源的时空差异的潜力,但由于人工智能错误,它们也伴随着个人和社会风险。此外,消费者直接与医疗保健AI互动的方式正在重塑传统的医患关系。然而,学术界对此类应用程序的研究概述缺乏系统的理解。
目的:本文系统地描述和分析了纳入研究的特点,确定了文献中提到的DTC医疗保健AI应用程序的现有障碍和设计建议,并为未来的设计和开发提供了参考。
方法:本范围审查遵循系统审查的首选报告项目和范围审查的Meta分析扩展指南,并根据Arksey和O\'Malley的5阶段框架进行。关于DTC医疗保健AI应用程序的同行评审论文发表于2023年3月27日,在WebofScience上,Scopus,ACM数字图书馆,IEEEXplore,PubMed,谷歌学者也包括在内。论文采用布劳恩和克拉克的反思性主题分析方法进行了分析。
结果:在检索到的2898篇论文中,包括涵盖这一新兴领域的32个(1.1%)。收录的论文最近发表(2018-2023年),大多数(23/32,72%)来自发达国家。医学领域主要是普通实践(8/32,25%)。在用户和功能方面,一些应用程序是专为单一消费者群体设计的(24/32,75%),提供疾病诊断(14/32,44%),健康自我管理(8/32,25%),和医疗信息查询(4/32,13%)。其他与医生相关的应用程序(5/32,16%),家庭成员(1/32,3%),护理人员(1/32,3%),和医疗保健部门(2/32,6%),通常提醒这些群体注意消费者用户的异常情况。此外,确定了与DTC医疗保健AI应用程序相关的8个障碍和6个设计建议。在面向消费者的医疗保健AI系统中,一些特别值得注意的更微妙的障碍以及相应的设计建议,包括增强以人为本的可解释性,建立校准的信任和解决过度信任,在人工智能中表现出同理心,提高消费级产品的专业化水平,扩大测试人群的多样性,进一步讨论。
结论:蓬勃发展的DTC医疗保健AI应用程序既存在风险,也存在机遇,这凸显了探索其现状的必要性。本文对纳入研究的特点进行了系统的归纳和整理,确定了面临的现有障碍,并为此类应用程序提出了未来的设计建议。据我们所知,这是第一个对这些应用程序的学术研究进行系统总结和分类的研究。进行此类系统设计和开发的未来研究可以参考这项研究的结果,这对于改善DTC医疗保健AI应用程序提供的医疗保健服务至关重要。
Direct-to-consumer (
DTC) health care artificial intelligence (AI) apps hold the potential to bridge the spatial and temporal disparities in health care resources, but they also come with individual and societal risks due to AI errors. Furthermore, the manner in which consumers interact directly with health care AI is reshaping traditional physician-patient relationships. However, the academic community lacks a systematic comprehension of the research overview for such apps.
This paper systematically delineated and analyzed the characteristics of included studies, identified existing barriers and design recommendations for
DTC health care AI apps mentioned in the literature and also provided a reference for future design and development.
This scoping review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guidelines and was conducted according to Arksey and O\'Malley\'s 5-stage framework. Peer-reviewed papers on
DTC health care AI apps published until March 27, 2023, in Web of Science, Scopus, the ACM Digital Library, IEEE Xplore, PubMed, and Google Scholar were included. The papers were analyzed using Braun and Clarke\'s reflective thematic analysis approach.
Of the 2898 papers retrieved, 32 (1.1%) covering this emerging field were included. The included papers were recently published (2018-2023), and most (23/32, 72%) were from developed countries. The medical field was mostly general practice (8/32, 25%). In terms of users and functionalities, some apps were designed solely for single-consumer groups (24/32, 75%), offering disease diagnosis (14/32, 44%), health self-management (8/32, 25%), and health care information inquiry (4/32, 13%). Other apps connected to physicians (5/32, 16%), family members (1/32, 3%), nursing staff (1/32, 3%), and health care departments (2/32, 6%), generally to alert these groups to abnormal conditions of consumer users. In addition, 8 barriers and 6 design recommendations related to
DTC health care AI apps were identified. Some more subtle obstacles that are particularly worth noting and corresponding design recommendations in consumer-facing health care AI systems, including enhancing human-centered explainability, establishing calibrated trust and addressing overtrust, demonstrating empathy in AI, improving the specialization of consumer-grade products, and expanding the diversity of the test population, were further discussed.
The booming
DTC health care AI apps present both risks and opportunities, which highlights the need to explore their current status. This paper systematically summarized and sorted the characteristics of the included studies, identified existing barriers faced by, and made future design recommendations for such apps. To the best of our knowledge, this is the first study to systematically summarize and categorize academic research on these apps. Future studies conducting the design and development of such systems could refer to the results of this study, which is crucial to improve the health care services provided by DTC health care AI apps.