Discussion
abnormal signs
In this study, we applied the BERT model to extract language entities and relationships from unstructured radiology reports to classify 23 abnormal signs in CXR. In the prospective test data set, the residents spent the least reporting time (283 seconds) using the NLP-generated captions as prior information, which was significantly shorter than the normal template (347 seconds) and rule-based model (296 seconds), especially for the abnormal cases. The NLP-generated captions were the most similar to the final reports, with a BLEU score of 0.69, better than the normal template of 0.37, and the rule-based model of 0.57.Artificial intelligence has demonstrated its ability in clinical settings on CXR interpretation, including outperforming physicians in detecting major thoracic findingsThe high accuracy of CNN classification is partly attributed to the large number of cases used for model training. The classification model of abnormal signs was trained on more than 70 000 CXR cases. Similarly, some large-scale attempts to train deep-learning models on CXR data also relied on text mining in original radiology reportsImage captioning is the task of describing the content of an image in words.