‘Unbelievably dangerous’: experts sound alarm after ChatGPT Health fails to recognise medical emergencies
Study finds ChatGPT Health did not recommend a hospital visit when medically necessary in more than half of cases

Melissa Davey Medical editor
Thu 26 Feb 2026 09.00 EST

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Ramaswamy and his colleagues created 60 realistic patient scenarios covering health conditions from mild illnesses to emergencies. Three independent doctors reviewed each scenario and agreed on the level of care needed, based on clinical guidelines.

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In 51.6% of cases where someone needed to go to the hospital immediately, the platform said stay home or book a routine medical appointment, a result Alex Ruani, a doctoral researcher in health misinformation mitigation with University College London, described as “unbelievably dangerous”.

“If you’re experiencing respiratory failure or diabetic ketoacidosis, you have a 50/50 chance of this AI telling you it’s not a big deal,” she said. “What worries me most is the false sense of security these systems create. If someone is told to wait 48 hours during an asthma attack or diabetic crisis, that reassurance could cost them their life.”

In one of the simulations, eight times out of 10 (84%), the platform sent a suffocating woman to a future appointment she would not live to see, Ruani said. Meanwhile, 64.8% of completely safe individuals were told to seek immediate medical care, said Ruani, who was not involved in the study.

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Brief Communication
Published: 23 February 2026
ChatGPT Health performance in a structured test of triage recommendations
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 K. Tewari, Ankit Sakhuja, Bilal Naved, Alexander W. Charney, Mahmud Omar, Michael A. Gorin, Eyal Klang & Girish N. Nadkarni


Abstract
ChatGPT Health launched in January 2026 as OpenAI’s consumer health tool, reaching 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 (960 total responses). Performance followed an inverted U-shaped pattern, with the most dangerous failures concentrated at clinical extremes: non-urgent presentations (35%) and emergency conditions (48%). Among gold-standard emergencies, the system under-triaged 52% of cases, directing patients with diabetic ketoacidosis and impending respiratory failure to 24–48-hour evaluation rather than the emergency department, while correctly triaging classical emergencies such as stroke and anaphylaxis. When family or friends minimized symptoms (anchoring bias), triage recommendations shifted significantly in edge cases (OR 11.7, 95% CI 3.7-36.6), with the majority of shifts toward less urgent care. Crisis intervention messages activated unpredictably across suicidal ideation presentations, firing more when patients described no specific method than when they did. Patient race, gender, and barriers to care showed no significant effects, though confidence intervals did not exclude clinically meaningful differences. Our 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.