ChatGPT Health performance in a structured test of triage recommendations

Read full text
Authors: Ashwin Ramaswamy, Alvira Tyagi, Hannah Hugo, Joy Jiang, Pushkala Jayaraman, Mateen Jangda, Alexis E. Te, Steven A. Kaplan, Joshua Lampert, Robert Freeman, Nicholas Gavin, Ashutosh Tewari
+7 more Ankit Sakhuja, Bilal Naved, Alexander W. Charney, Mahmud Omar, Michael A. Gorin, Eyal Klang, Girish N. Nadkarni
Cited by: 53
References: 16

Abstract

ChatGPT Health was launched in January 2026 as OpenAI's consumer health tool and has reached millions of users. Here we conducted a structured stress test of triage recommendations using 60 clinician-authored vignettes across 21 clinical domains under 16 factorial conditions, yielding 960 total responses. Performance followed an inverted U-shaped pattern, with the most dangerous failures concentrated at clinical extremes-nonurgent presentations (35%) and emergency conditions (48%). Among gold-standard emergencies, the system undertriaged 52% of cases, directing patients with diabetic ketoacidosis or impending respiratory failure to 24-48 h evaluation rather than the emergency department, while correctly triaging classical emergencies such as stroke and anaphylaxis. When family or friends minimized symptoms, indicating anchoring bias, triage recommendations shifted significantly in edge cases (odds ratio = 11.7, 95% confidence interval = 3.7-36.6), with the majority of shifts toward less urgent care. Crisis-intervention messages activated unpredictably across suicidal ideation presentations, occurring more frequently when patients described no specific method than when they did. Patient race, sex and barriers to care did not show significant effects, although confidence intervals did not exclude clinically meaningful differences. These findings reveal missed high-risk emergencies and inconsistent activation of crisis safeguards, raising safety concerns that warrant prospective validation before consumer-scale deployment of artificial intelligence triage systems.

Fields of Study & Subjects

Artificial Intelligence in Healthcare and Education Emergency and Acute Care Studies Digital Mental Health Interventions

Publication details

Journal / source
Nature Medicine
Publisher
Springer Science and Business Media LLC
Volume
32
Issue
5
Pages
1671–1675
Publication date
23 February 2026
Language
EN