Researchers have built a tool to uncover hidden issues in the massive datasets used to train medical AI.
The tool searches training data for subtle patterns that could lead AI models to incorrect conclusions, potentially jeopardizing patient care.
The work aims to help researchers and regulators make artificial intelligence more reliable and trustworthy for real-world clinical use.
The work, a collaboration between Johns Hopkins and the U.S. Food and Drug Administration, is published in npj Digital Medicine.
G-AUDIT identifies and ranks attributes in the data likely to lead the AI to make potentially incorrect conclusions.
Researchers tested it on a range of medical data, including images, text and spreadsheets.
It revealed hidden flaws in data collection, imaging conditions and other elements likely to lead to flawed conclusions about a patient's health.
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