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.

翻译：胸部X光报告的自动化因其耗时性而引起了广泛关注。然而，由于医学信息的复杂性、书写风格的多样性以及存在拼写错误和不一致的可能性，自由文本报告的临床准确性难以通过自然语言处理指标进行量化。相比之下，结构化报告和标准化报告可以提供一致性，并使临床正确性的评估更加规范化。然而，用于结构化报告的高质量标注数据十分稀缺。因此，我们提出了一种方法，用于预测结构化报告模板中由句子定义的临床发现，从而填充这些模板。该方法包括：使用胸部X光片及相关自由文本放射学报告训练对比性语言-图像模型，然后为每个结构化发现创建文本提示，并优化分类器以预测医学图像中的临床发现。结果表明，即使仅有少量图像级标注用于训练，该方法也能完成结构化报告任务，包括评估心脏肥大严重程度以及定位胸部X光中的病理区域。