A new tool overcomes a significant hurdle in clinical AI design. Scientists from Harvard Medical School and Stanford University have created a diagnostic tool using artificial intelligence that can detect diseases on chest X-rays based on the natural language descriptions provided in the accompanyi
have created a diagnostic tool using artificial intelligence that can detect diseases on chest X-rays based on the natural language descriptions provided in the accompanying clinical reports.
For instance, to label a chest X-ray dataset, expert radiologists would have to look at hundreds of thousands of X-ray images one by one and explicitly annotate each one with the conditions detected. While more recent AI models have tried to address this labeling bottleneck by learning from unlabeled data in a “pre-training” stage, they eventually require fine-tuning on labeled data to achieve high performance.
“With CheXzero, one can simply feed the model a chest X-ray and corresponding radiology report, and it will learn that the image and the text in the report should be considered as similar—in other words, it learns to match chest X-rays with their accompanying report,” Rajpurkar added. “The model is able to eventually learn how concepts in the unstructured text correspond to visual patterns in the image.
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