AI Assistants Based on Large Language Models for Adolescent Health and Well-Being: A Systematic Review

Objective: Large Language Models (LLMs) are emerging as a key component for digital health systems based on Artificial Intelligence (AI). The objective of this paper is to systematically review the characteristics, effectiveness, and implementation challenges of LLM-based assistants targeting adolescent health and well-being. Methods: A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. PubMed, Scopus, and Web of Science were searched in November 2025 for studies published from 2022 onwards. Eligible studies examined LLM-based assistants used directly by adolescents in health and well-being contexts and reported quantitative outcomes. Data was extracted independently by multiple reviewers and synthesised narratively. Risk of bias was assessed using the Mixed Methods Appraisal Tool (MMAT). Results: Nine studies met the inclusion criteria, involving between 3 and 40 participants and covering mental health support, physical activity promotion, treatment engagement, vaccination awareness, and academic self-efficacy. Most studies were proof-of-concept investigations or pilot studies. LLM-based assistants were associated with reductions in depression, anxiety, stress, and negative affect, improvements in emotional regulation, physical activity, treatment motivation, HPV knowledge, and academic self-efficacy. Risk of bias was generally moderate and the evidence base was limited by small samples, short follow-up periods, and heterogeneous methodologies. Conclusions: LLM-based assistants show promise as scalable and accessible tools for supporting adolescent health and well-being, particularly in mental health domains. However, the current evidence remains preliminary. Larger, long-term, and rigorously designed studies are required to establish effectiveness, safety, and implementation feasibility. Registration: This review was not pre-registered and no review protocol has been published prior to conducting the review.