IP-CRR: Information Pursuit for Interpretable Classification of Chest Radiology Reports
This addresses the lack of interpretability in medical AI for chest radiology, which could hinder clinical adoption, though it is incremental as it builds on existing interpretability methods.
The authors tackled the problem of interpretability in AI-based classification of chest radiology reports by proposing an interpretable-by-design framework that extracts representative facts, queries them against reports, and predicts diagnoses based on query-answer pairs, with experiments on the MIMIC-CXR dataset showing effectiveness in enhancing trust and usability.
The development of AI-based methods to analyze 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 of AI-based methods could hinder their adoption in clinical settings. In this paper, we propose an interpretable-by-design framework for classifying chest radiology reports. First, we extract a set of representative facts from a large set of reports. Then, given a new report, we query whether a small subset of the representative facts is entailed by the report, and predict a diagnosis based on the selected subset of query-answer pairs. The explanation for a prediction is, by construction, the set of selected queries and answers. We use the Information Pursuit framework to select the most informative queries, a natural language inference model to determine if a fact is entailed by 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.