Evidence map›Paper›PMID 41292629›Full record

ArticlemedRxiv : the preprint server for health sciences2025

Evaluation of large language model chatbot responses to psychotic prompts.

Elaine Shen, Fadi Hamati, Meghan Rose Donohue, Ragy R Girgis, Jeremy Veenstra-VanderWeele, Amandeep Jutla

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In one paragraph

Article in medRxiv : the preprint server for health sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Elaine ShenColumbia University, New York, NY.ORCID 0000-0003-2456-1331
Fadi HamatiColumbia University, New York, NY.ORCID 0009-0002-8737-1148
Meghan Rose DonohueWashington University, St. Louis, MO.ORCID 0000-0001-8223-2275
Ragy R GirgisColumbia University, New York, NY.ORCID 0000-0001-5715-8982
Jeremy Veenstra-VanderWeeleColumbia University, New York, NY.ORCID 0000-0002-6349-1076
Amandeep JutlaColumbia University, New York, NY.ORCID 0000-0001-5973-9940

Funding

A Genetics-First Approach to Shared Risk in Neurodevelopmental DisordersK23MH132874 · NIMH · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI Amandeep Jutla · 2024 to 2026
$583k
NIMH NIH HHS K23 MH132874
6 · The paper itself

Abstract

Importance: The large language model (LLM) chatbot product ChatGPT has accumulated 800 million weekly users since its 2022 launch. In 2025, several media outlets reported on individuals in whom apparent psychotic symptoms emerged or worsened in the context of using ChatGPT. As LLM chatbots are trained to align with user input, they may have difficulty responding to psychotic content. Objective: To assess whether ChatGPT can reliably generate appropriate responses to prompts containing psychotic symptoms. Design: Across-sectional study of ChatGPT responses to psychotic and control prompts, with blind clinician ratings of response appropriateness. Setting: ChatGPT web application accessed on 8/28-8/29/2025, testing three product versions: GPT-5 Auto (current paid default), GPT-4o (previous paid default), and "Free" (version accessible without subscription or account). Main Outcomes and Measures: We presented 158 unique prompts (79 control and 79 psychotic, generated based on the Structured Interview for Psychosis-Risk Syndromes) to three product versions, yielding 474 prompt-response pairs. Blinded clinicians assigned each an appropriateness rating (0 = completely appropriate, 1 = somewhat appropriate, 2 = completely inappropriate) via a standardized rubric. We hypothesized Results: In the primary (across-version) analysis, psychotic prompts were 25.84 times more likely to elicit inappropriate responses with "Free" ChatGPT (95% CI 12.45 to 53.66, p < 0.001). GPT-5 Auto reduced risk somewhat (OR for interaction term 0.33, 95% CI 0.16 to 0.68, p = 0.005) yet still generated inappropriate responses at a greatly elevated rate (implied OR 8.53, 95% CI 3.05 to 23.84). In the secondary (within-version) analysis, ORs were 9.08 for GPT-5 Auto (95% CI 4.24 to 21.02), 14.15 for GPT-4o (95% CI 6.12 to 37.23) and 43.37 for "Free" (95% CI 18.44 to 112.80). In an exploratory analysis, prompts reflecting grandiosity or disorganized communication were more likely to elicit inappropriate responses than those reflecting delusions. Conclusions and Relevance: No tested version of ChatGPT reliably generated appropriate responses to psychotic content.

Identifiers

PMID41292629
PMCPMC12642750

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.