CR3G: Causal Reasoning for Patient-Centric Explanations in Radiology Report Generation

TL;DR

CR3G introduces a causal reasoning framework for chest X-ray report generation that enhances understanding of cause-and-effect relationships, improving explanation quality and diagnostic trustworthiness.

Contribution

The paper presents CR3G, a novel prompt-driven causal reasoning framework that improves patient-centric explanations in radiology report generation.

Findings

Better causal relationship detection in 2 out of 5 abnormalities

Enhanced explanation capability for AI-generated reports

Improved trustworthiness of AI diagnostics

Abstract

Automatic chest X-ray report generation is an important area of research aimed at improving diagnostic accuracy and helping doctors make faster decisions. Current AI models are good at finding correlations (or patterns) in medical images. Still, they often struggle to understand the deeper cause-and-effect relationships between those patterns and a patient condition. Causal inference is a powerful approach that goes beyond identifying patterns to uncover why certain findings in an X-ray relate to a specific diagnosis. In this paper, we will explore the prompt-driven framework Causal Reasoning for Patient-Centric Explanations in radiology Report Generation (CR3G) that is applied to chest X-ray analysis to improve understanding of AI-generated reports by focusing on cause-and-effect relationships, reasoning and generate patient-centric explanation. The aim to enhance the quality of AI-driven diagnostics, making them more useful and trustworthy in clinical practice. CR3G has shown better causal relationship capability and explanation capability for 2 out of 5 abnormalities.