IP-CRR: Information Pursuit for Interpretable Classification of Chest Radiology Reports
Journal:
arXiv
Published Date:
Apr 30, 2025
Abstract
The development of AI-based methods for analyzing radiology reports could
lead to significant advances in medical diagnosis--from improving diagnostic
accuracy to enhancing efficiency and reducing workload. However, the lack of
interpretability in these methods has hindered their adoption in clinical
settings. In this paper, we propose an interpretable-by-design framework for
classifying radiology reports. The key idea is to extract a set of most
informative queries from a large set of reports and use these queries and their
corresponding answers to predict a diagnosis. Thus, the explanation for a
prediction is, by construction, the set of selected queries and answers. We use
the Information Pursuit framework to select informative queries, the Flan-T5
model to determine if facts are present in the report, and a classifier to
predict the disease. Experiments on the MIMIC-CXR dataset demonstrate the
effectiveness of the proposed method, highlighting its potential to enhance
trust and usability in medical AI.
