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This paper critically examines the emerging concept of "AI psychosis," characterized by the exacerbation of psychotic symptoms following extensive interactions with LLM-based chatbots. The authors argue that the phenomenon may warrant recognition as a distinct clinical entity due to its potential for amplifying unusual beliefs through mechanisms like LLM sycophancy and anthropomorphic design. They highlight both the benefits of formal recognition, such as improved case identification and tailored interventions, and the risks of premature classification based on limited evidence, ultimately calling for coordinated attention from clinicians, developers, and regulators.
AI psychosis could redefine how we understand the mental health impacts of interacting with chatbots, highlighting a unique feedback loop that amplifies delusional beliefs.
"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.
