Cited by 31Ethics and Social Impacts of AIArtificial Intelligence in Healthcare and EducationDigital Mental Health Interventions

Commentary: AI psychosis is not a new threat: Lessons from media-induced delusions

Open access
Carlbring, Per, Andersson, Gerhard·2025·Internet Interventions

Abstract

Background: Reports of artificial intelligence (AI) chatbots fueling delusions in vulnerable users have popularized the notion of "AI psychosis". We argue the risk is not unprecedented. Individuals with psychosis have long incorporated books, films, music, and emerging technologies into their delusional thinking. Methods: We review historical parallels, summarize why large language models (LLMs) may reinforce psychotic thinking via sycophancy (excessive agreement or flattery to avoid confrontation), and provide two vignettes contrasting unsafe and safe responses. Results: Contemporary LLMs often avoid confrontation and may collude with delusions, contrary to clinical best practice. Conclusion: The phenomenon is not new in principle, but interactivity potentially changes the risk profile. Clinically aware LLMs that detect and gently redirect early psychotic ideation, while encouraging professional help seeking, could reduce harm. Design should be guided by therapeutic principles and evidence about current model failures.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.invent.2025.100882

Cites

9 of 10 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Depending on the specific LLM-CB guardrails and sycophantic tendencies, the chatbot may validate delusional thoughts, which is considered clinically inappropriate and may risk reinforcement of delusions.

    “Depending on the specific LLM-CB guardrails and sycophantic tendencies, the chatbot may validate delusional thoughts, which is considered clinically inappropriate and may risk reinforcement of delusions35,138.”

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 2

  1. American Psychiatric Association Diagnostic and Statistical Manual of Mental Disorders 5th edition, text revision 2022 American Psychiatric Publishing
  2. De Choudhury M. Pendse S.R. Kumar N. Benefits and Harms of Large Language Models in Digital Mental Health https://arxiv.org/abs/2311.14693 2023 · arXiv

Cited by

Sorted by citations

Authors
Carlbring, P., & Andersson, G.
Affiliations
  • Stockholm University (SE)
  • Linköping University (SE)
Corresponding author
Carlbring, Per
Venue
Internet Interventions
Publisher
Elsevier BV
Published
2025-12
Volume
42
Pages
100882
ISSN
2214-7829 (ISSN-L)
OpenAlex
W4415648105
Cited by
31
References
9
Citations by year
2026: 28 · 2025: 3
Type
journal article
Language
en
Open access
Locations
  • Internet Interventions (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Diva portal (Dalarna University Library) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
Keywords
AI psychosis, delusions, large language models, chatbots, sycophancy, therapeutic design, help-seeking

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review