A concept-bottleneck classifier coupled with a multi-agent RAG pipeline generates chest X-ray reports, with LLM-based evaluation scoring the multi-agent approach higher than single-agent or GPT-4 baselines.
In: 2024 International Joint Conference on Neural Networks (IJCNN)
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Towards Interpretable Radiology Report Generation via Concept Bottlenecks using a Multi-Agentic RAG
A concept-bottleneck classifier coupled with a multi-agent RAG pipeline generates chest X-ray reports, with LLM-based evaluation scoring the multi-agent approach higher than single-agent or GPT-4 baselines.