# Disease probability-enhanced follow-up chest X-ray radiology report summary generation

## TL;DR

This paper introduces a new AI framework for generating follow-up chest X-ray reports that track disease progression and device changes.

## Contribution

The novel framework uses disease probability soft guidance and masked entity modeling loss to improve follow-up radiology report generation.

## Key findings

- The proposed model outperforms state-of-the-art methods in follow-up report generation.
- Incorporating medical lexicon through masked entity modeling improves clinical fidelity.
- Disease probability soft guidance enhances the accuracy of abnormality reporting.

## Abstract

A chest X-ray radiology report describes abnormal findings not only from X-ray obtained at a given examination, but also findings on disease progression or change in device placement with reference to the X-ray from previous examination. Majority of the efforts on automatic generation of radiology report pertain to reporting the former, but not the latter, type of findings. To the best of the authors’ knowledge, there is only one work dedicated to generating summary of the latter findings, i.e., follow-up radiology report summary. In this study, we propose a transformer-based framework to tackle this task. Motivated by our observations on the significance of medical lexicon on the fidelity of report summary generation, we introduce two mechanisms to bestow clinical insight to our model, namely disease probability soft guidance and masked entity modeling loss. The former mechanism employs a pretrained abnormality classifier to guide the presence level of specific abnormalities, while the latter directs the model’s attention toward medical lexicon. Extensive experiments were conducted to demonstrate that the performance of our model exceeded the state-of-the-art.