Evaluating diagnostic content of AI-generated radiology reports of chest X-rays.

Radiology reports are of core importance for the communication between the radiologist and clinician. A computer-aided radiology report system can assist radiologists in this task and reduce variation between reports thus facilitating communication with the medical doctor or clinician. Producing a well structured, clear, and clinically well-focused radiology report is essential for high-quality patient diagnosis and care. Despite recent advances in deep learning for image caption generation, this task remains highly challenging in a medical setting. Research has mainly focused on the design of tailored machine learning methods for this task, while little attention has been devoted to the development of evaluation metrics to assess the quality of AI-generated documents. Conventional quality metrics for natural language processing methods like the popular BLEU score, provide little information about the quality of the diagnostic content of AI-generated radiology reports. In particular, because radiology reports often use standardized sentences, BLEU scores of generated reports can be high while they lack diagnostically important information. We investigate this problem and propose a new measure that quantifies the diagnostic content of AI-generated radiology reports. In addition, we exploit the standardization of reports by generating a sequence of sentences. That is, instead of using a dictionary of words, as current image captioning methods do, we use a dictionary of sentences. The assumption underlying this choice is that radiologists use a well-focused vocabulary of 'standard' sentences, which should suffice for composing most reports. As a by-product, a significant training speed-up is achieved compared to models trained on a dictionary of words. Overall, results of our investigation indicate that standard validation metrics for AI-generated documents are weakly correlated with the diagnostic content of the reports. Therefore, these measures should be not used as only validation metrics, and measures evaluating diagnostic content should be preferred in such a medical context.