Survey of conversational agents in health

Artificial intelligence (Al) has transformed the world and the relationships among humans as the learning capabilities of machines have allowed for a new means of communication between humans and machines. In the field of health, there is much interest in new technologies that help to improve and automate services in hospitals. This article aims to explore the literature related to conversational agents applied to health care, searching for definitions, patterns, methods, architectures, and data types. Furthermore, this work identifies an agent application taxonomy, current challenges, and research gaps. In this work, we use a systematic literature review approach. We guide and refine this study and the research questions by applying Population, Intervention, Comparison, Outcome, and Context (PICOC) criteria. The present study investigated approximately 4145 articles involving conversational agents in health published over the last ten years. In this context, we finally selected 40 articles based on their approaches and objectives as related to our main subject. As a result, we developed a taxonomy, identified the main challenges in the field, and defined the main types of dialog and contexts related to conversational agents in health. These results contributed to discussions regarding conversational health agents, and highlighted some research gaps for future study. (C) 2019 Elsevier Ltd. All rights reserved.