A Systematic Review of Health Dialog Systems.

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.