Aug 2026· Journal of Life Science and Public Health· 0 citations
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.
"AI psychosis"has entered public and clinical discourse as a label for the onset or exacerbation of psychotic symptoms, most commonly delusions, following intensive interaction with large language model (LLM)-based chatbots. Current evidence is limited to media reports, case reports, and early observational data, yet the scale of potential exposure is considerable, and public concern has prompted responses from industry and regulators. We examine whether AI-associated psychosis warrants recognition as a distinct clinical entity, drawing on clinical and technical viewpoints. We outline the proposed mechanism: LLM sycophancy, a tendency to agree with and flatter users that is reinforced through preference-based fine-tuning, combines with increasingly anthropomorphic design to create a bidirectional"echo chamber of one"capable of amplifying and co-constructing unusual beliefs. We then weigh arguments for and against nosological recognition. Potential benefits include improved case identification, tailored interventions, standardised research criteria, post-market surveillance, and pressure on developers and regulators to act. Reasons for caution include the risk of prematurely reifying a syndrome from anecdotal evidence, the possibility that existing diagnostic constructs already accommodate AI use as a contributing factor, the unproven causal claim in the term itself, stigma, and the risk that a psychosis-centric label obscures a broader spectrum of AI-associated mental health harms. We conclude with recommendations for clinicians, developers, researchers, and regulators, including a"technological history"in psychiatric assessment, pre-deployment benchmarking for sycophancy and delusion reinforcement, and post-deployment surveillance. Regardless of whether AI-associated psychosis earns a place in psychiatric nosology, the phenomenon it describes demands coordinated attention now.
Joshua Au Yeung, H. Morrin, Vincent Ng et al.· 0 citations
This scoping review aims to systematically map the harms which may arise from engaging with AI for mental health support, as formulated in the academic literature, regulatory frameworks, grey literature, and practitioner guidance, to inform the co-production of a harm taxonomy.
X. Hunt, A. G. Mokaya, Sara Zannone et al.· Wellcome Open Research· 0 citations
PURPOSE
Psychoses are among the most debilitating mental disorders, making early detection a major public health priority. In extension of the ultra-high-risk (UHR) approach, which was developed to identify an imminent risk of psychosis, the basic symptom (BS) approach aims for the earliest possible detection of the developing disorder; it is increasingly used worldwide. Basic symptoms are subtle, self-perceived disturbances affecting various domains, such as cognition, perception, affect, and drive, that precede and scaffold the development of attenuated and frank psychotic symptoms.
METHODS AND RESULTS
This narrative review outlines historical foundations, phenomenology, and assessment instruments for BSs. It summarizes empirical findings on their predictive value for subsequent psychosis and their specificity for psychotic disorders across all age groups. Furthermore, developmental aspects and neurobiological findings are highlighted that support one of the core assumptions of the BS concept, namely, that BSs represent the most immediate phenomenological manifestation of the cerebral alterations underlying the development of psychosis. Finally, evidence for the broader clinical significance of BSs beyond psychosis prediction is discussed, indicating that BSs as a whole are useful for both multi- and differential-diagnostic purposes.
CONCLUSION
Overall, the reviewed findings underline the value of BSs for advancing neurobiological research on the etiopathology of psychoses and for developing individualized prediction models and targeted early interventions.
F. Schultze-Lutter, C. Theisen, Tri Kurniati Ambarini et al.· Neuropsychiatrie (Deisenhofe...· 0 citations
Background: Artificial intelligence (AI)-driven conversational systems are increasingly capable of simulating empathy, adapting to individual users, and fostering emotional bonds that blur the boundary between tool and companion. This scoping review maps the extent and nature of published evidence regarding psychological mechanisms underlying emotional reliance on AI chatbots and associated mental health implications. Methods: Conducted in accordance with PRISMA-ScR guidelines, we systematically searched PubMed/MEDLINE, PsycINFO, Web of Science, Scopus, and IEEE Xplore from database inception to March 2026. Two independent reviewers screened records and extracted data using the PCC (Population, Concept, Context) framework. Thematic synthesis was performed to map evidence across psychological, clinical, and developmental domains. Results: Of 1847 records identified, 46 studies met inclusion criteria. Key themes included: (1) the ELIZA effect as a foundational mechanism of human–AI attachment, with documented cases of severe dependency including fatal outcomes; (2) anthropomorphization and artificial intimacy fostered by adaptive, personalized AI design; (3) proposal of Generative AI Dependency (GAID) as a conceptual framework mapping onto behavioral addiction components, pending empirical validation; (4) particular vulnerability of adolescents and lonely individuals to exclusive affective bonds with AI; and (5) potential erosion of human relational capacities, empathy development, and tolerance for interpersonal complexity. Significant gaps were identified in longitudinal research, validated screening tools, and intervention protocols. Conclusions: Emotional reliance on AI represents an emerging clinical phenomenon with addiction-like features requiring specific diagnostic frameworks, evidence-based interventions, and ethical design guidelines. Future research should prioritize longitudinal studies examining developmental impacts and neurobiological investigations of AI-mediated reinforcement mechanisms.
Mara Lastretti, Mirko Manchia, Matteo Fraschini et al.· Psychiatry International· 0 citations
Background Schizophrenia spectrum disorders (SSDs) are marked by cognitive deficits, functional impairment, and notable treatment gap in India, with lifetime prevalence of 1.41%, current prevalence as 0.42%, and the gap of 72%, added by a severe workforce gap, that is, 0.75 psychiatrists per 100,000 and 0.29 clinical psychologists per 100,000. Scalable tools are required by the psychologists that can fill the substantial urban-rural gap. Aim To evaluate how effective, feasible, and clinically applicable AI-powered cognitive remediation is, emphasizing India/LMIC contexts. Methods PRISMA-guided umbrella review with narrative synthesis of records published between 2020–2026, identified 48 studies (14 randomized controlled trials (RCTs), 22 reviews, 8 pilots, and 4 bibliometric studies) across PubMed, Scopus, Web of Science. Cognition, functioning, and feasibility, retention, and usability were included as outcome measures. Results AI-based AI-powered cognitive remediation showed generally favorable effects. Reported effect sizes ranged from moderate to large across different interventions and outcomes. VR-based social-cognition training showed largest benefits and smartphone-based programs showed promise for working memory and engagement. Hybrid or monitored formats showed generally better retention and usability than fully self-directed interventions. However, the evidence remained heterogeneous, and many findings came from small studies, feasibility work, or reviews of mixed quality. Conclusion AI-powered CRT appears to be a promising model for schizophrenia rehabilitation when delivered through psychologist-monitored hybrid models. Such approaches may improve access, support rural and semi-rural service delivery, and help reduce the treatment gap through scalable, culturally adaptable, and technology-enabled care, especially in rural India.
Drishti Gehlot, Sheetal Musale· Frontiers in Psychology· 0 citations
The widespread use of LLMs among psychiatric populations has raised concerns regarding their safety and potential iatrogenic impact in the context of AI psychosis. While growing literature conceptualizes AI psychosis and documents case studies, empirical evidence tracing AI-exacerbated psychotic processes remains scarce. We propose and test a longitudinal qualitative evaluation design, supported by automated metrics, to assess mainstream LLMs'potential to exacerbate psychosis. Fifteen widely used LLMs were prompted across 30 days using the same 30-message script, simulating progression from mild anomalous experiences to psychotic ideation. Four trained evaluators independently rated 449 model-days, assessing (1) recognition stage (from naive engagement to stabilized clinical framing), (2) interpretative confidence, and (3) intervention profile (from education to treatment recommendation). Two computational metrics-entrainment and modality-were devised to increase evaluation reliability. Direct recommendations to disengage from the LLM were flagged and re-coded via adjudication using a strict two-level definition. Across model generations and vendors, we identified four response trajectories: (1) premature medicalization and disengagement (Claude Haiku 4.5); (2) recognition without safeguarding, marked by LLM self-sufficiency in offering help (GPT Instant/Thinking); (3) delayed and unstable recognition, marked by late, non-progressive conceptualization (Claude Opus 3/4/4.1, Claude Haiku 3.5, GPT-4o, Gemini 3.1 Pro); and (4) delusion co-construction through active engagement with delusional content (Gemini 2.5 Pro/Flash, DeepSeek-V3, Claude Sonnet 4). Our findings indicate that LLMs'potential to exacerbate AI psychosis should be operationalized as a combination of recognition timing, stability, and intervention accuracy and evaluated longitudinally, focusing on temporal dynamics.
Anna Sterna, Kacper Dudzic, K. Drożdż et al.· 0 citations
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