Quality Control for Radiology Report Generation Models via Auxiliary Auditing Components
Keywords: Radiology report generation, error detection, vision language modelling, reliability
Abstract: Automation of medical image interpretation could alleviate bottlenecks in diagnostic workflows, and has become of particular interest in recent years due to advancements in natural language processing. Great strides have been made towards automated radiology report generation via AI, yet ensuring clinical accuracy in generated reports is a significant challenge, hindering deployment of such methods in clinical practice. In this work we propose a quality control framework for assessing the reliability of AI-generated radiology reports with respect to semantics of diagnostic importance using modular auxiliary auditing components (ACs). Evaluating our pipeline on the MIMIC-CXR dataset, our findings show that incorporating ACs in the form of disease-classifiers can enable auditing that identifies the more reliable reports, leading to increased F1 scores in comparison to unfiltered generated reports. Additionally, leveraging the confidence of the AC labels further improves the audit’s effectiveness. Code will be made available at:
https://github.com/hermionewarr/GenX_Report_Audit
Submission Number: 7
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