Artificial Intelligence in Digital Self-Diagnosis Tools: A Narrative Overview of Reviews

Abstract

Digital self-diagnosis tools, or symptom checkers, many of which incorporate artificial intelligence technology, are intended to provide diagnostic information and triage advice to lay users. This narrative overview of reviews explores the common themes and issues raised by existing evidence synthesis literature on these tools to establish a common ground for interdisciplinary research. We searched 3 bibliographic databases (PubMed, Scopus and Web of Science) and Google Scholar using keyword combinations of <i>artificial</i>, <i>self-diagnosis</i>, <i>intelligence</i>, and <i>machine learning</i> for publications from 2019 to 2023. We included systematic reviews, meta-analyses, scoping reviews, narrative syntheses, and opinion pieces that discussed tools where users proactively entered personal health information to acquire a predicted diagnosis of their symptoms or triage advice. This overview reveals significant gaps in understanding the key areas of development, implementation, impact, and oversight of digital self-diagnosis tools. Additionally, the terminology used to describe these tools and their underlying technologies varies widely, encompassing technologies ranging from simple branching logic algorithms to complex deep neural networks. Our interdisciplinary analysis identified gaps and critical areas for future research across all stages of the lifecycles of these tools. The diverse challenges uncovered highlight the necessity for multiagency and multidisciplinary efforts promoting responsible development and implementation.

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