Towards Interpretable Radiology Report Generation via Concept Bottlenecks using a Multi-Agentic RAG (2412.16086v1)

Abstract: Deep learning has advanced medical image classification, but interpretability challenges hinder its clinical adoption. This study enhances interpretability in Chest X-ray (CXR) classification by using concept bottleneck models (CBMs) and a multi-agent Retrieval-Augmented Generation (RAG) system for report generation. By modeling relationships between visual features and clinical concepts, we create interpretable concept vectors that guide a multi-agent RAG system to generate radiology reports, enhancing clinical relevance, explainability, and transparency. Evaluation of the generated reports using an LLM-as-a-judge confirmed the interpretability and clinical utility of our model's outputs. On the COVID-QU dataset, our model achieved 81% classification accuracy and demonstrated robust report generation performance, with five key metrics ranging between 84% and 90%. This interpretable multi-agent framework bridges the gap between high-performance AI and the explainability required for reliable AI-driven CXR analysis in clinical settings.

Interpretability in Radiology Report Generation via Concept Bottlenecks

The paper "Towards Interpretable Radiology Report Generation via Concept Bottlenecks using a Multi-Agentic RAG" proposes a noteworthy advancement in the domain of radiology, specifically addressing the interpretability challenges of deep learning in medical imaging. The authors introduce a novel framework that synergizes Concept Bottleneck Models (CBMs) with a Multi-Agent Retrieval-Augmented Generation (RAG) system to enhance Chest X-ray (CXR) analysis by fostering transparent and clinically relevant report generation.

Overview of Methodology

The proposed approach is tackled in two stages: interpretable classification using CBMs and robust radiology report generation.

1. Interpretable Classification: The classification process is enhanced through CBMs that discover and use human-interpretable concepts in CXR images. This method bridges image embeddings generated by a vision-LLM, ChexAgent, with textual embeddings made using the Mistral Embed Model. Through cosine similarity and max pooling, the concept vectors are formed and used for classifying images with high interpretability.
2. Multi-Agent Radiology Report Generation: The pipeline employs a multi-agent structure in the RAG framework, using specialized agents per disease category. This framework includes retrieval and report-generation agents that synthesize relevant clinical information and refine it into a coherent radiological report. Enhanced by LlamaIndex and CrewAI, this system aims to provide detailed and clinically useful reports.

Performance Assessment

The paper offers a robust evaluation, using the COVID-QU dataset for both classification and report generation, achieving an accuracy of 81% for classification tasks. The interpretability and intervention capabilities are particularly underscored by the improvements noted when correcting misclassifications through concept vectors, demonstrating the validity of concept bottlenecks.

For report generation, the multi-agent RAG approach is quantitatively analyzed against single-agent frameworks and GPT-4 outputs. t-SNE visualizations and cluster evaluation metrics such as Silhouette Score and Davies-Bouldin Index illustrate that the multi-agent system captures the nuanced differences between diseases, reflecting clinical realities more accurately than a single-agent approach.

Results Discussion

Evaluating through LLM assessments, the multi-agent RAG model outperformed baseline methods on metrics including Semantic Similarity, Accuracy, and Clinical Usefulness. Adjustments in report clustering and effective use of disease-specific agents have shown to result in more clinically valid outputs, reaffirmed through the Mixture of Agents (MoA) approach.

Implications and Future Directions

This research presents significant implications for the practical deployment of AI systems in medical settings, specifically providing a model where interpretability and explainability are paramount. The integration of CBMs with multi-agent systems here not only provides accurate classifications but also generates insightful and reliable radiological reports that align with medical professionals' demands for transparency.

In the future, expanding this framework across various imaging modalities could broaden its applicability. Additionally, refining the system's adaptability and robustness through further enhancements in the multi-agent architecture could yield even more precise clinical applications.

Overall, this paper's approach contributes a meaningful step towards bridging high-performance AI with the interpretability and reliability crucial for clinical adoption.