Generative AI-Induced Psychosis and Its Public Health Implications: A Scoping Review
TL;DR
The findings underscore the need for responsible AI governance, robust clinical oversight, multidisciplinary collaboration, multidisciplinary collaboration, and longitudinal research to better understand and mitigate the long-term mental health consequences of generative AI.
Abstract
The rapid advancement and widespread adoption of generative artificial intelligence (AI), particularly large language models (LLMs) and conversational AI systems, have transformed digital mental healthcare by improving access to information, clinical decision support, and psychological assistance. However, increasing concerns have emerged regarding their potential to contribute to adverse psychiatric outcomes, including psychosis, particularly among vulnerable individuals. This scoping review mapped the current evidence on generative AI-induced psychosis and examined its implications for public health. The review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) and the Joanna Briggs Institute (JBI) methodology. Searches of Google Scholar, PubMed, Scopus, SpringerLink, and ScienceDirect identified 1,876 records, of which 21 studies met the inclusion criteria and were included in the final synthesis. The evidence indicates that generative AI has considerable potential to enhance psychiatric assessment, clinical decision-making, emotional support, and access to mental health services. At the same time, emerging risks include the reinforcement of delusional beliefs, hallucination-like experiences, reality distortion, misinformation, emotional dependency, and the exacerbation of psychotic symptoms, particularly among individuals with pre-existing psychological vulnerabilities. Ethical concerns relating to privacy, algorithmic bias, digital inequality, and the absence of comprehensive regulatory frameworks were also consistently reported. Although the current evidence remains limited and largely exploratory, the findings underscore the need for responsible AI governance, robust clinical oversight, multidisciplinary collaboration, and longitudinal research to better understand and mitigate the long-term mental health consequences of generative AI.