Evidence and Future Directions for Pediatric Health Care Chatbots: Systematic Review
Abstract
Background: Pediatric health care requires distinct considerations, including caregiver involvement and developmental differences in cognition and communication as children gain autonomy, particularly as pediatric health care chatbots gradually emerge. Because childhood and adolescence are formative periods for health behaviors and self-management practices, pediatric chatbots also warrant evaluation against long-term rather than immediate outcomes.
Objective: This study aimed to characterize and synthesize the available evidence on pediatric health care chatbots evaluated for health-related outcomes. Furthermore, by identifying gaps in the existing literature, we sought to propose specific considerations for the design, evaluation, and implementation of pediatric health care chatbots.
Methods: PubMed, Embase, Scopus, PsycINFO, the Cochrane Library, and the Web of Science were systematically searched without publication year restrictions. Randomized controlled trials, mixed methods, and observational studies that evaluated health care chatbots for children (aged <19 y) or caregivers and assessed health-related outcomes were included. Nonoriginal papers, end-of-life or palliative care studies, and non-English publications were excluded. Study quality was assessed using the Mixed Methods Appraisal Tool and the Oxford Levels of Evidence 2.
Results: A total of 9 studies were included, with 5 (55.6%) involving pediatric participants only, while 4 (44.4%) involved caregivers. Six…
How strong is the evidence?
Evidence Hub reads this as a systematic review, done in people. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
Read the paper
Related studies
- Knowledge Management Perspective of Generative Artificial Intelligence
- Navigating Generative Artificial Intelligence Promises and Perils for Knowledge and Creative Work
- Characterizing the evolving research on enterprise content management
- Large language models and multimodal foundation models for precision oncology
- From Generative AI to Extended Reality: Multidisciplinary Perspectives on the Challenges, Opportunities, and Future of Educational Computing
- Generative Artificial Intelligence: A New Engine for Advancing Environmental Science and Engineering
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.