AI vs. doctors: who spots kidney danger from chemo first?
NCT ID NCT07114276
First seen Jan 10, 2026 · Last updated Jun 22, 2026 · Updated 23 times
Summary
This study tested whether a machine learning model could predict kidney injury caused by platinum chemotherapy more accurately than doctors. Researchers followed 77 cancer patients receiving cisplatin or carboplatin, tracking their kidney function over time. The goal was to see if the computer model could better forecast acute kidney injury within 14 days or kidney disease within 89 days, compared to clinical judgment.
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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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Locations
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Taipei Medical University Wan Fang Hospital
Taipei, Taiwan
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 identify patients at high risk for kidney damage from platinum chemotherapy, allowing for earlier protective measures.
What could go wrong
This is a small, completed study (77 patients) that only tests prediction accuracy, not whether using the model actually improves patient outcomes. The model may not work as well in other populations or settings.
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.