Looking at Radiology Report Generation through a Causal Lens: A Survey
Satyam Kumar Indian Institute of Technology, Bombay, Kaustubh Shivshankar Shejole, Pushpak Bhattacharyya Indian Institute of Technology, Bombay, Dhirubhai Ambani Institute Of Information and Communication Technology
Automatic radiology report generation (RRG) has emerged as a promising approach to reduce clinicians’ workload, yet existing systems are vulnerable to biases induced by spurious correlations across data, models, and evaluation pipelines. Such biases raise serious fairness concerns and may adversely affect patient care, making their mitigation critical in clinical settings. Leveraging causal inference to identify true cause-effect relationships can mitigate many biases and yield fair, reliable systems with clinically meaningful outputs. Existing surveys on RRG primarily emphasize deep learning approaches while overlooking the critical role of causality. This survey addresses this gap by analyzing bias across the RRG pipeline, formalizing RRG as a causal modeling problem, and reviewing representative causal techniques from the literature. Based on the level of intervention, we organize existing mitigation strategies into a three-tier taxonomy. We further examine commonly used public medical imaging datasets and evaluation metrics through a causal lens, revealing their biases and limitations in capturing causal alignment and clinical fidelity. To address these limitations, we advocate broader demographic coverage and causal-aware evaluation metrics to improve fairness and reliability, and identify important directions for future work.
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Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表