arXiv AI
MA-RAG: Multi-Agent Retrieval-Augmented Generation for Query-Driven Summarization of Longitudinal Parkinson's Disease Assessments
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Retrieval-Augmented Agentic Rubric Generation for Reliable Medical Response Evaluation
The paper introduces a retrieval‑augmented multi‑agent framework that automatically generates instance‑specific evaluation rubrics for medical language models. By retrieving authoritative medical evidence, decomposing it into atomic facts, and combining these with user interaction constraints, the system produces fine‑grained criteria that outperform GPT‑4o on HealthBench and LLMEval‑Med. The generated rubrics also guide response refinement, improving medical LLM output quality by 9.2%.
arXiv AI
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Ten Headache Specialists versus Artificial Intelligence for Clinical Literature Summarization: A Critical Evaluation and Comparison
arXiv:2606. 05436v1 Announce Type: new Abstract: Summarizing the latest medical literature to guide clinical decision-making is essential for evidence-based medicine and high-quality patient care.
arXiv AI
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Evaluating AI Generated Summaries for Cancer Patients
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arXiv AI
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EHRNote-ChatQA: A Benchmark for Evidence-Grounded Multi-Turn Clinical Question Answering over Longitudinal Discharge Summaries
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arXiv Computation and Language
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Improving Health Literacy through Lay Summarization of Radiological Reports: An Evaluation of BioNER and Retrieval-Augmented Generation
The paper examines how Retrieval-Augmented Generation (RAG) and Named Entity Recognition (NER) affect the quality of lay summaries of radiology reports. Using a framework that extracts clinically relevant findings via NER and grounds them with RAG, the authors evaluate few‑shot and fine‑tuned versions of Qwen and BioBART. Results show that NER consistently improves readability and overall quality, RAG alone offers no benefit and can introduce hallucinations, and the best performance comes from fine‑tuned BioBART with NER.
arXiv AI
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From Conflict to Consensus: Boosting Medical Reasoning via Multi-Round Agentic RAG
arXiv:2603. 03292v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) exhibit high reasoning capacity in medical question-answering, but their tendency to produce hallucinations and outdated knowledge poses critical risks in healthcare fields.