AI predicts which kids need genetic drug tests
NCT ID NCT06902688
First seen Mar 06, 2026 · Last updated Jun 22, 2026 · Updated 14 times
Summary
This study tests whether a machine learning model can predict which children admitted to the hospital will need a medication that requires genetic testing. The goal is to offer the test earlier, so doctors can pick the safest drug or dose. The trial will involve 275 children aged 6 months to 18 years at a single hospital in Canada.
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This is a summary of
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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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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The Hospital for Sick Children
RECRUITINGToronto, Ontario, M5G1X8, Canada
Contact Phone: •••-•••-•••• Email: •••••@•••••
Contact
What this could mean
Our plain-language read of the trial. This is informational only — not medical advice or a prediction.
Active substance
machine learning model
What this could lead to
If successful, this could help doctors personalize medication for children faster, reducing side effects and improving treatment.
What could go wrong
This is an early-stage study focused on testing the model's accuracy, not on patient outcomes. The model may not work as expected in real-world hospital settings.
As listed by the trial registrant
The condition terms exactly as the trial's registrant entered them.