No labels? No problem! New tool overcomes major hurdle in clinical AI design harvardmed natBME
The step is deemed a major advance in clinical AI design because most current AI models require laborious human annotation of vast reams of data before the labeled data are fed into the model to train it.shows that the model, called CheXzero, performed on par with human radiologists in its ability to detect pathologies on chest X-rays.Most AI models require labeled datasets during their"training" so they can learn to correctly identify pathologies.
"We're living the early days of the next-generation medical AI models that are able to perform flexible tasks by directly learning from text," said study lead investigator Pranav Rajpurkar, assistant professor of biomedical informatics in the Blavatnik Institute at HMS."Up until now, most AI models have relied on manual annotation of huge amounts of data—to the tune of 100,000 images—to achieve a high performance. Our method needs no such disease-specific annotations.
Upon testing, CheXzero successfully identified pathologies that were not explicitly annotated by human clinicians. It outperformed other self-supervised AI tools and performed with accuracy similar to that of human radiologists.
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