CR3G: Causal Reasoning for Patient-Centric Explanations in Radiology Report Generation
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Abstract (arXiv)
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.
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References · 7
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
- 2023GPT-4 Technical Reportcited by 18,396
- 2017ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseasescited by 972
- 2019MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographscited by 597
- 2019PadChest: A large chest x-ray image dataset with multi-label annotated reportscited by 309
- 2023Exploring the Boundaries of GPT-4 in Radiologycited by 31
- 2024M4CXR: Exploring Multi-task Potentials of Multi-modal Large Language Models for Chest X-ray Interpretationcited by 17
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- 2026Radiologist-Guided Causal Concept Bottleneck Models for Chest X-Ray Interpretation
- 2023Rethinking Radiology Report Generation via Causal Inspired Counterfactual Augmentation
- 2025Phrase-grounded Fact-checking for Automatically Generated Chest X-ray Reports
- 2023Longitudinal Data and a Semantic Similarity Reward for Chest X-Ray Report Generation
- 2025VICCA: Visual Interpretation and Comprehension of Chest X-ray Anomalies in Generated Report Without Human Feedback
- 2025CXReasonBench: A Benchmark for Evaluating Structured Diagnostic Reasoning in Chest X-rays
- 2025Reasoning Visual Language Model for Chest X-Ray Analysis
- 2024Anatomically-Grounded Fact Checking of Automated Chest X-ray Reports