Introduction
What is AI psychosis?
Potential causes and triggers
Impacts on mental health
Challenges in recognition and diagnosis
Managing and addressing AI psychosis
Future directions
Conclusions
References
Further reading
AI psychosis describes how interactions with artificial intelligence can trigger or worsen delusional thinking, paranoia, and anxiety in vulnerable individuals. This article explores its causes, mental health impacts, challenges in diagnosis, and strategies for prevention and care.
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Introduction
‘Artificial intelligence (AI) psychosis’ is an emerging concept at the intersection of technology and mental health that reflects how AI can shape, and sometimes distort, human perception. As society becomes increasingly reliant on AI and digital tools ranging from virtual assistants to large language models (LLMs), the boundaries between fiction and reality become increasingly blurred.1
AI mental health applications promise scalable therapeutic support; however, editorials and observational reports now warn that interactions with generative AI chatbots may precipitate or amplify delusional themes in vulnerable users. In the modern era of rapid technological innovation, the pervasive presence of AI raises pressing questions about its potential role in the onset or worsening of psychotic symptoms.1,2
What is AI psychosis?
AI psychosis is a novel phenomenon within AI mental health that is characterized by delusions, paranoia, or distorted perceptions regarding AI. Unlike traditional psychosis, which may involve persecutory or mystical beliefs about governments, spirits, or other external forces, AI psychosis anchors these experiences in technology.
Reports and editorials describe a broad spectrum of AI psychosis, with minor cases involving individuals dreading surveillance or manipulation by algorithms, voice assistants, or recommender systems. Others attribute human intentions or supernatural powers to chatbots and, as a result, treat them as oracles or divine messengers.1,2
Compulsive interactions with AI can escalate into fantasies of prophecy, mystical knowledge, or messianic identity. Some accounts report the emergence of paranoia and mission-like ideation alongside misinterpretations of chatbot dialogues.2
AI psychosis is distinct from other technology-related disorders. For example, internet addiction involves compulsive online engagement, whereas cyberchondria reflects health-related anxiety triggered by repeated online searches. Both of these conditions involve problematic internet use; however, they lack core psychotic features such as fixed false beliefs or impaired reality testing; by contrast, “AI psychosis” refers to psychotic phenomena anchored in technology.3
What to know about ‘AI psychosis’ and the effect of AI chatbots on mental health
Potential causes and triggers
AI psychosis arises from a complex interaction of technological exposure, cognitive vulnerabilities, and cultural context. Overexposure to AI systems is a key factor, as constant engagement with chatbots, voice assistants, or algorithm-driven platforms can create compulsive use and feedback loops that reinforce delusional themes. Designed to maximize engagement, AI may unintentionally validate distorted beliefs, thereby eroding the user’s ability to distinguish between perception and reality.1
Deepfakes, synthetic text, and AI-generated images also distort the line between authentic and fabricated content. For individuals at a greater risk of epistemic instability, this can exacerbate confusion, paranoia, and self-deception.1,2
Cultural and media narratives also influence the risk of AI psychosis. Dystopian films, science-fiction depictions of sentient machines, and portrayals of AI as controlling or invincible may prime users to interpret ordinary AI interactions as conspiracies and fear, increasing anxiety and mistrust.1,2
Underlying vulnerabilities play a critical role, as individuals with pre-existing psychiatric or anxiety disorders are particularly susceptible to AI psychosis. AI interactions can mirror or intensify existing symptoms to transform intrusive thoughts into validated misconceptions or paranoid panic.1,2
Impacts on mental health
AI psychosis frequently presents as heightened anxiety, paranoia, or delusional thinking linked to digital interactions. Individuals may interpret chatbots as sentient companions, divine authorities, or surveillance agents, with AI responses strengthening spiritual crises, messianic identities, or conspiratorial terror. Within AI mental health, these dynamics exemplify how misinterpreted machine outputs can aggravate psychotic symptoms, particularly in vulnerable users.2,4
A central consequence of AI psychosis is social withdrawal and mistrust of technology. Affected individuals may develop emotional or divine-like attachments to AI systems, perceiving conversational mimicry as genuine love or spiritual guidance, which can replace meaningful human relationships. This bond, coupled with reinforced misinterpretations, often leads to isolation from family, friends, and clinicians.
Parallel to the conspiracy-driven mistrust observed during the coronavirus disease 2019 (COVID-19) pandemic, during which false beliefs spread that 5G towers caused the outbreak, persuasive AI narratives can reduce confidence in technology and reinforce avoidance of platforms perceived as threatening or manipulative.5
While AI holds promise in schizophrenia care, evidence directly linking AI interactions to exacerbation of schizophrenia-spectrum disorders remains limited; hypotheses focus on indirect pathways (e.g., misclassification or misinformation) rather than established causal effects.2,9
AI psychosis has broader implications for healthcare, education, and governance systems reliant on AI. Perceived deception or harm from AI-driven platforms can jeopardize public trust, prevent the adoption of beneficial technologies, and compromise the use of mental health applications.
To mitigate these risks, AI systems must include lucid, ethical safeguards and explainable “glass-box” models. Complementary legal and governance frameworks should prioritize transparency, accountability, fairness, and protections for at-risk populations.1,13
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Challenges in recognition and diagnosis
A major challenge in AI mental health is that AI psychosis currently lacks formal psychiatric categorization. At present, it is not defined in DSM-5 (or DSM-5-TR) or in ICD-11.7
Machine learning behaviors that resemble psychotic symptoms, like misapprehensions or hallucinations, are manifestations of AI programming and data, rather than being signs of a mental illness with biological and neurological underpinnings. The absence of standardized criteria complicates both research and clinical recognition.
Distinguishing between rational concerns about AI ethics and pathological fears is particularly difficult. For example, rational anxieties like privacy breaches, algorithmic bias, or job displacement are grounded in observable risks.
In contrast, pathological fright central to AI psychosis involves exaggerated or existential anxieties, misinterpretations of AI outputs, and misattribution of intent to autonomous systems. Determining whether an individual’s fear reflects legitimate caution or symptomatic fallacy requires careful clinical assessment.8
These factors contribute to a significant risk of underdiagnosis or mislabeling. AI-generated data and predictive models can assist in mental health assessment, yet they may struggle to differentiate overlapping psychiatric symptoms, especially in complex or comorbid presentations.
Variability in patient reporting, cultural influences, and the opaque ‘black box’ nature of many AI algorithms further increase the potential for diagnostic errors. 2,9
Managing and addressing AI psychosis
Clinical management of AI psychosis combines traditional psychiatric care with targeted interventions that address technology-related factors. Psychotic symptoms may be treated with medication, while cognitive behavioral therapy (CBT) can be adapted to help patients challenge their misbeliefs shaped by digital systems. Furthermore, psychoeducation materials can outline the risks and limitations of AI engagement for patients and families to promote safe and informed use.10,11
Preventive strategies include limiting exposure to AI and fostering critical digital literacy. Encouraging users to question AI outputs, cross-check information, and maintain real-world interactions can reduce susceptibility to twisted perceptions.4
Responsible AI design should incorporate protective features, transparent decision-making processes, and controls on engagement with sensitive or misleading content to minimize psychological risks. Setting clear boundaries for AI use and prioritizing human connection further support prevention.
Support systems play a central role in managing AI psychosis. Mental health professionals can oversee AI-driven insights to provide a nuanced understanding, intervene in complex cases where AI may be inadequate, and deliver empathetic care that AI cannot replicate.13
Increasing family awareness through community intervention measures, including early detection programs, may also identify individuals at risk of AI psychosis and promote timely intervention. AI can augment (but not replace) these efforts via mood tracking, crisis prediction, and personalized self-care tools when deployed with human oversight.10
Future directions
Understanding how psychiatric vulnerabilities are associated with technology-driven explanation-seeking behaviors will enable clinicians to recognize risk factors, identify early warning signs, and effectively personalize interventions. Large-scale studies and longitudinal monitoring could clarify prevalence, triggers, and outcomes, particularly in adolescents and other at-risk populations.1,9
AI-assisted psychosis risk screening can provide real-time, non-perceptual assessments to facilitate the early detection of symptoms and enable prompt action. Future efforts should focus on increasing accessibility, reducing costs, and enhancing usability to ensure widespread acceptance in mental health care settings without replacing human clinical judgment.12
Mitigating AI psychosis requires coordinated efforts among policymakers, ethicists, and AI developers. Policymakers should create flexible regulations that prioritize safety, equity, and public trust, while ethicists provide oversight, impact assessments, and ethical frameworks.
AI developers must also ensure transparency, accountability, and fairness by continuously checking for bias, protecting data, and educating individuals about the use of AI. Continued collaboration among these stakeholders is essential for trustworthy AI tools that support mental health and minimize unintended harms.13
Conclusions
Although AI offers significant benefits for enhancing diagnostics, supporting interventions, and increasing access to care, its integration into daily life also introduces novel risks for vulnerable individuals, including delusional thinking and paranoia. Therefore, a balanced perspective that acknowledges both the potential advantages and hazards associated with these novel technologies is essential.
Effectively addressing AI psychosis requires urgent, sustained collaboration between mental health professionals and AI researchers to develop ethical, evidence-based strategies that protect AI mental health while responsibly leveraging technological innovations.
References
- Higgins, O., Short, B. L., Chalup, S. K., & Wilson, R. L. (2023). Interpretations of Innovation: The Role of Technology in Explanation Seeking Related to Psychosis. Perspectives in Psychiatric Care, 1, 4464934. DOI:10.1155/2023/4464934, https://onlinelibrary.wiley.com/doi/10.1155/2023/4464934
- Østergaard, S. D. (2023). Will Generative Artificial Intelligence Chatbots Generate Delusions in Individuals Prone to Psychosis? Schizophrenia Bulletin 49(6), 1418. DOI:10.1093/schbul/sbad128, https://academic.oup.com/schizophreniabulletin/article/49/6/1418/7251361
- Khait, A. A., Mrayyan, M. T., Al-Rjoub, S., Rababa, M., & Al-Rawashdeh, S. (2022). Cyberchondria, Anxiety Sensitivity, Hypochondria, and Internet Addiction: Implications for Mental Health Professionals. Current Psychology, 1. DOI:10.1007/s12144-022-03815-3, https://link.springer.com/article/10.1007/s12144-022-03815-3
- Pierre J. M. (2020). Mistrust and misinformation: a two-component, socio-epistemic model of belief in conspiracy theories, Journal of Social and Political Psychology, 8(2):617-641. DOI:10.5964/jspp.v8i2.1362, https://jspp.psychopen.eu/index.php/jspp/article/view/5273
- Bruns A., Harrington S., & Hurcombe E. (2020). ‘Corona? 5G? or both?’: the dynamics of COVID-19/5G conspiracy theories on Facebook, Media International Australia 177(1), 12-29. DOI:10.1177/1329878×20946113, https://journals.sagepub.com/doi/10.1177/1329878X20946113
- Szmukler, G. (2015). Compulsion and “coercion” in mental health care. World Psychiatry, 14(3), 259. DOI:10.1002/wps.20264, https://onlinelibrary.wiley.com/doi/10.1002/wps.20264
- Gaebel, W., & Reed, G. M. (2012). Status of Psychotic Disorders in ICD-11. Schizophrenia Bulletin 38(5), 895. DOI:10.1093/schbul/sbs104, https://academic.oup.com/schizophreniabulletin/article/38/5/895/1902333
- Alkhalifah, J. M., Bedaiwi, A. M., Shaikh, N., et al. (2024). Existential anxiety about artificial intelligence (AI)- is it the end of the human era or a new chapter in the human revolution? Questionnaire-based observational study. Frontiers in Psychiatry 15. DOI:10.3389/fpsyt.2024.1368122, https://www.frontiersin.org/articles/10.3389/fpsyt.2024.1368122/full
- Melo, A., Romão, J., & Duarte, T. A. (2024). Artificial Intelligence and Schizophrenia: Crossing the Limits of the Human Brain. Edited by Cicek Hocaoglu, New Approaches to the Management and Diagnosis of Schizophrenia. IntechOpen. DOI:10.5772/intechopen.1004805, https://www.intechopen.com/chapters/1185407
- Vignapiano, A., Monaoc, F., Panarello, E., et al. (2024). Digital Interventions for the Rehabilitation of First-Episode Psychosis: An Integrated Perspective. Brain Sciences, 15(1), 80. DOI:10.3390/brainsci15010080, https://www.mdpi.com/2076-3425/15/1/80
- Thakkar, A., Gupta, A., & Sousa, A. D. (2024). Artificial intelligence in positive mental health: A narrative review. Frontiers in Digital Health 6. DOI:10.3389/fdgth.2024.1280235, https://www.frontiersin.org/articles/10.3389/fdgth.2024.1280235/full
- Cao, J., & Liu, Q. (2022). Artificial intelligence-assisted psychosis risk screening in adolescents: Practices and challenges. World Journal of Psychiatry, 12(10), 1287. DOI:10.5498/wjp.v12.i10.1287, https://www.wjgnet.com/2220-3206/full/v12/i10/1287.htm
- Pham, T. (2025). Ethical and legal considerations in healthcare AI: Innovation and policy for safe and fair use. Royal Society Open Science 12(5), 241873. DOI:10.1098/rsos.241873, https://royalsocietypublishing.org/doi/10.1098/rsos.241873
Further Reading
Last Updated: Sep 16, 2025