JMIR Publications

ABSTRACT

Background:

A rising number of conversational agents (CA) or chatbots are equipped with Artificial Intelligence (AI) architecture. They are increasingly prevalent in healthcare applications such as education and support of patients with chronic diseases, one of the leading causes of death in the 21st century. AI-based chatbots enable more effective and frequent interactions with such patients.

Objective:

The goal of this systematic literature review is to review the characteristics, health care conditions, and AI architectures of AI-based conversational agents designed specifically for chronic diseases.

Methods:

We conducted a systematic literature review using PubMed Medline, EMBASE, PyscInfo, CINAHL, ACM Digital Library, ScienceDirect, and Web of Science. We applied a predefined search strategy using the terms “conversational agent”, “healthcare”, “Artificial Intelligence” and their synonyms. We updated the search results using Google alerts and screened reference lists for other relevant articles. We included primary research studies that involved the prevention, treatment, or rehabilitation of chronic diseases, involved a conversational agent, and included any kind of AI architecture. Two independent reviewers conducted screening and data extraction and Cohen’s kappa was used to measure interrater agreement. A narrative approach was applied for data synthesis.

Results:

The literature search found 2052 articles, out of which ten papers met the inclusion criteria. The small number of identified studies together with the prevalence of quasi-experimental studies (n=7) and prevailing prototype nature of the chatbots (n=7) revealed the immaturity of the field. The reported chatbots addressed a broad variety of chronic diseases (n=6), showcasing a tendency to develop specialized CAs for individual chronic conditions. However, there lacks comparison of these chatbots within and between chronic diseases. In addition, the reported evaluation measures were not unified and the addressed health goals widely defined. Together, these study characteristics complicated comparability and opens room for future research. While natural language processing represented the most utilized AI technique (n=7) and the majority of CAs allowed for multimodal interaction (n=6), the identified studies demonstrated broad heterogeneity, lack of depth of reported AI techniques and systems, as well as inconsistent usage of taxonomy of the underlying AI software, hereby further aggravating comparability and generalizability of study results.

Conclusions:

The literature on AI-based conversational agents for chronic conditions is scarce and mostly consists of quasi-experimental studies with chatbots in prototype stage that utilize natural language processing and allow for multimodal user interaction. Future research could profit from evidence-based evaluation of the AI-based CAs and comparison thereof within and between different chronic health conditions. Besides increased comparability, the quality of chatbots developed for specific chronic conditions and their subsequent impact on the target patients could be enhanced by more structured development and standardized evaluation processes.