  The automation of chest X-ray reporting has garnered significant interest due
to the time-consuming nature of the task. However, the clinical accuracy of
free-text reports has proven challenging to quantify using natural language
processing metrics, given the complexity of medical information, the variety of
writing styles, and the potential for typos and inconsistencies. Structured
reporting and standardized reports, on the other hand, can provide consistency
and formalize the evaluation of clinical correctness. However, high-quality
annotations for structured reporting are scarce. Therefore, we propose a method
to predict clinical findings defined by sentences in structured reporting
templates, which can be used to fill such templates. The approach involves
training a contrastive language-image model using chest X-rays and related
free-text radiological reports, then creating textual prompts for each
structured finding and optimizing a classifier to predict clinical findings in
the medical image. Results show that even with limited image-level annotations
for training, the method can accomplish the structured reporting tasks of
severity assessment of cardiomegaly and localizing pathologies in chest X-rays.
