CBM-RAG: Demonstrating Enhanced Interpretability in Radiology Report Generation with Multi-Agent RAG and Concept Bottleneck Models

Hasan Md Tusfiqur Alam; Devansh Srivastav; Abdulrahman Mohamed Selim; Md Abdul Kadir; Md Moktadirul Hoque Shuvo; Daniel Sonntag

doi:10.1145/3731406.3731970

arXiv · 2504.20898

CBM-RAG: Demonstrating Enhanced Interpretability in Radiology Report Generation with Multi-Agent RAG and Concept Bottleneck Models

Hasan Md Tusfiqur Alam·Devansh Srivastav·Abdulrahman Mohamed Selim·Md Abdul Kadir·Md Moktadirul Hoque Shuvo·Daniel Sonntag

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Abstract

Advancements in generative Artificial Intelligence (AI) hold great promise for automating radiology workflows, yet challenges in interpretability and reliability hinder clinical adoption. This paper presents an automated radiology report generation framework that combines Concept Bottleneck Models (CBMs) with a Multi-Agent Retrieval-Augmented Generation (RAG) system to bridge AI performance with clinical explainability. CBMs map chest X-ray features to human-understandable clinical concepts, enabling transparent disease classification. Meanwhile, the RAG system integrates multi-agent collaboration and external knowledge to produce contextually rich, evidence-based reports. Our demonstration showcases the system's ability to deliver interpretable predictions, mitigate hallucinations, and generate high-quality, tailored reports with an interactive interface addressing accuracy, trust, and usability challenges. This framework provides a pathway to improving diagnostic consistency and empowering radiologists with actionable insights.

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Hasan Md Tusfiqur Alam, Devansh Srivastav, Abdulrahman Mohamed Selim, Md Abdul Kadir, Md Moktadirul Hoque Shuvo, Daniel Sonntag. 2025-04-29. CBM-RAG: Demonstrating Enhanced Interpretability in Radiology Report Generation with Multi-Agent RAG and Concept Bottleneck Models. https://doi.org/10.1145/3731406.3731970

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