Abstract

Background Health dialog systems have seen increased adoption by patients, hospitals, and universities due to the confluence of advancements in machine learning and the ubiquity of high-performance hardware that supports real-time speech recognition, high-fidelity text-to-speech, and semantic understanding of natural language.

Objectives This review seeks to enumerate opportunities to apply dialog systems toward the improvement of health outcomes while identifying both gaps in the current literature that may impede their implementation and recommendations that may improve their success in medical practice.

Methods A search over PubMed and the ACM Digital Library was conducted on September 12, 2017 to collect all articles related to dialog systems within the domain of health care. These results were screened for eligibility with the main criteria being a peer-reviewed study of a system that includes both a natural language interface and either end-user testing or practical implementation.

Results Forty-six studies met the inclusion criteria including 24 quasi-experimental studies, 16 randomized control trials, 2 case–control studies, 2 prospective cohort studies, 1 system description, and 1 human–computer conversation analysis. These studies evaluated dialog systems in five application domains: medical education (n = 20), clinical processes (n = 14), mental health (n = 5), personal health agents (n = 5), and patient education (n = 2).

Conclusion We found that dialog systems have been widely applied to health care; however, most studies are not reproducible making direct comparison between systems and independent confirmation of findings difficult. Widespread adoption will also require the adoption of standard evaluation and reporting methods for health dialog systems to demonstrate clinical significance.

中文翻译：

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对健康对话系统的系统回顾。

摘要

背景技术 由于机器学习的进步以及支持实时语音识别，高保真文本转语音和语音识别的高性能硬件的广泛应用，健康对话系统在患者，医院和大学中得到了越来越多的采用。自然语言的语义理解。

目标 这篇综述旨在枚举将对话系统应用于改善健康结果的机会，同时找出当前文献中可能妨碍其实施的差距和可能改善其在医学实践中成功的建议。

方法 2017年9月12日对PubMed和ACM数字图书馆进行了搜索，以收集与医疗保健领域内的对话系统有关的所有文章。筛选这些结果是否符合资格，主要标准是对系统进行的同行评审研究，该系统包括自然语言界面以及最终用户测试或实际实施。

结果 46项研究符合纳入标准，包括24项准实验研究，16项随机对照试验，2项病例对照研究，2项前瞻性队列研究，1项系统描述和1项人机对话分析。这些研究在五个应用领域中评估了对话系统：医学教育（n  = 20），临床过程（n  = 14），心理健康（n  = 5），个人健康代理（n  = 5）和患者教育（n  = 2 ）。

结论 我们发现对话系统已被广泛应用于医疗保健。但是，大多数研究无法重现，因此很难在系统之间进行直接比较和对结果进行独立确认。广泛采用还需要采用健康对话系统的标准评估和报告方法来证明其临床意义。