CheXbert: Radiologist-level Automated Radiology Report Labeler using Deep Learning

The CheXbert labeler accurately detects the presence or absence of 14 common medical conditions in radiology reports, converting unstructured radiology text into a structured format. Previous approaches to report labeling typically rely either on sophisticated engineering based on medical domain knowledge or manual annotations by experts. CheXbert uses a novel approach to medical image report labeling that leverages recent advances in natural language processing. CheXbert is developed using labels provided by both board-certified radiologists and the previous state-of-the-art automatic labeler.
In experiments, CheXbert performed comparably to a radiologist and is able to outperform the previous best automatic labeler with statistical significance, setting a new state-of-the-art for report labeling on one of the largest datasets of chest x-rays. Accurate labeling of radiology text reports can enable high-quality training of AI-based medical imaging interpretation models.

Stage of Development