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MA-RAG is a multi-agent retrieval-augmented generation framework for query-driven summarization of longitudinal Parkinson's disease clinical assessments, achieving substantial improvements in factual precision and reducing hallucination rates compared to baseline methods.
Introduces HDSR and HDSR-PL, methods that use hallucination detectors to guide iterative self-refinement and preference learning, achieving up to 48% reduction in hallucinations for clinical summarization using Llama and Gemma models on MIMIC-IV-Note.
This paper presents The Daily Dose (TDD), an LLM-driven system for automated clinical summarization and clinical-trial identification integrated into routine radiation oncology practice, with early evaluation showing positive usability and time savings.