AI takes on radiologists: can a computer beat the standard for liver cancer diagnosis?
NCT ID NCT06626087
First seen Jun 05, 2026 · Last updated Jun 22, 2026 · Updated 5 times
Summary
This study tested a new artificial intelligence (AI) algorithm against the standard LI-RADS criteria for diagnosing liver cancer (hepatocellular carcinoma) on CT scans. Researchers enrolled 300 people at risk for liver cancer who had a new liver nodule found on ultrasound. The goal was to see if the AI could match or improve diagnostic accuracy compared to the current standard method.
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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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Department of Medicine and Department of Surgery, The University of Hong Kong, Queen Mary Hospital
Hong Kong, Hong Kong
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Department of Medicine, The University of Hong Kong, Queen Mary Hospital
Hong Kong, Hong Kong
What this could mean
Our plain-language read of the trial. This is informational only — not medical advice or a prediction.
Active substance
Artificial intelligence algorithm
What this could lead to
If successful, this AI could help doctors diagnose liver cancer more accurately and quickly from CT scans.
What could go wrong
This is a completed study with 300 participants, but the AI is still a prototype and may not outperform current methods in real-world 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.