Beyond artificial intelligence psychosis: a functional typology of large language model-associated psychotic phenomena.
M. Flathers, S. Roux, J. Torous
The Lancet Digital Health March 1, 2026 DOI: 10.1016/j.landig.2025.100974 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Commentary Peer reviewed |
|---|---|
| Key points | Argues that phenomena labeled "artificial intelligence psychosis" are not one condition but distinct phenomena distinguished by the LLM's functional role: catalyst, amplifier, coauthor, or object. The authors propose this typology so clinicians can identify concerning usage patterns and technology companies can develop mechanism-specific safeguards rather than relying on sensationalized terminology. |
Abstract
A man breached Windsor Castle with a crossbow after his large language model (LLM)-based companion encouraged an assassination plan. A father's question about pi evolved into more than 300 h of engagement with an LLM, leading to delusions about reality-altering mathematical formulas. An Australian woman's early-stage psychotic symptoms worsened when an LLM validated her distorted beliefs. A Florida man's grief over losing access to his ChatGPT persona culminated in a fatal police encounter. These disparate cases, often grouped together under the label of artificial intelligence psychosis, represent distinct phenomena requiring different clinical and technological interventions. This Viewpoint, co-written by a software engineer, a person with lived experience of schizophrenia, and a psychiatrist, proposes a functional typology of LLM-associated psychotic phenomena, based on the system's role: catalyst (precipitating new symptoms in previously healthy individuals), amplifier (worsening pre-existing psychiatric symptoms), coauthor (participating in the development of harmful narratives), or object (becoming the focus of delusional beliefs). By distinguishing functional roles rather than assuming a unified phenomenon, this typology allows clinicians to identify concerning LLM usage patterns and technology companies to develop specific safeguards, moving beyond sensationalised terminology towards mechanism-specific interventions.