AI could help ER doctors spot the cause of chest pain faster
NCT ID NCT06196307
First seen Jun 26, 2026 · Last updated Jun 27, 2026 · Updated 1 time
Summary
This study is testing whether machine learning can help doctors quickly and accurately figure out what is causing chest pain in emergency patients. Researchers will collect data from 10,000 adults with non-traumatic chest pain, including test results and medical history. The goal is to create a tool that reduces misdiagnosis and speeds up treatment decisions.
What this could mean
Our plain-language read of the trial. This is informational only — not medical advice or a prediction.
What this could lead to
If successful, this could lead to a machine-learning tool that helps doctors quickly and accurately diagnose the cause of chest pain in the emergency room.
What could go wrong
This is an observational study, not a treatment trial. The model may not work as well in real-world settings or for all types of chest pain.
Disclaimer
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the original study
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Summaries may miss details or leave out important information. Before applying or accepting participation, make sure you have read and understood the full study. Curemydisease.com takes no responsibility whatsoever for anything missed, misunderstood, or acted upon as a result of our summary — we know it does not capture everything.
This is a summary of the original study . Summaries may miss details or leave out important information. Before applying or accepting participation, make sure you have read and understood the full study. Curemydisease.com takes no responsibility whatsoever for anything missed, misunderstood, or acted upon as a result of our summary — we know it does not capture everything.
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Conditions
The condition(s) this trial relates to.
As listed by the trial registrant
The condition terms exactly as the trial's registrant entered them.
Contacts and locations
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Study contacts
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Contact
Phone: •••-•••-•••• Email: •••••@•••••
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Contact
Phone: •••-•••-•••• Email: •••••@•••••
Locations
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Xiaonan He
RECRUITINGBeijing, Chaoyang, 100029, China
Contact Phone: •••-•••-•••• Email: •••••@•••••