Smart conversational agents for the detection of neuropsychiatric disorders: A systematic review

Objective: To determine whether smart conversational agents can be used for detection of neuropsychiatric disorders. Therefore, we reviewed the technologies used, targeted mental disorders and validation procedures of relevant proposals in this field. Methods: We searched Scopus, PubMed, Pro-Quest, IEEE Xplore, Web of Science, CINAHL and the Cochrane Library using a predefined search strategy. Studies were included if they focused on neuropsychiatric disorders and involved conversational data for detection and diagnosis. They were assessed for eligibility by independent reviewers and ultimately included if a consensus was reached about their relevance. Results: 2356 references were initially retrieved. Eventually, 17 articles – referring 9 smart conversational agents – met the inclusion criteria. Out of the selected studies, 7 are targeted at neurocognitive disorders, 7 at depression and 3 at other conditions. They apply diverse technological solutions and analysis techniques (82.4% use Artificial Intelligence), and they usually rely on gold standard tests for criterion validity assessment. Acceptability, reliability and other aspects of validity were rarely addressed. Conclusion: The use of smart conversational agents for the detection of neuropsychiatric disorders is an emerging and promising field of research, with a broad coverage of mental disorders and extended use of AI. However, the few published studies did not undergo robust psychometric validation processes. Future research in this field would benefit from more rigorous validation mechanisms and standardized software and hardware platforms.