{"slug": "ten-headache-specialists-versus-artificial-intelligence-for-clinical-literature", "title": "Ten Headache Specialists versus Artificial Intelligence for Clinical Literature Summarization: A Critical Evaluation and Comparison", "summary": "Ten headache specialists across the United States and Canada preferred expert-written summaries over those generated by three state-of-the-art large language models in a blinded evaluation of clinical literature summarization. The study, which compared summaries from Sonnet, GPT-4o, and Llama 3.1 against human-written syntheses, found that experts sometimes struggled to distinguish between human- and AI-generated content despite favoring the expert versions. The findings highlight key features valued by clinicians that can guide future improvements in both human and automated literature summarization for evidence-based medicine.", "body_md": "arXiv:2606.05436v1 Announce Type: new\nAbstract: Summarizing the latest medical literature to guide clinical decision-making is essential for evidence-based medicine and high-quality patient care. Yet clinicians face increasing challenges due to limited time with patients and a rapidly growing volume of published articles. Although retrieval-augmented large language models (LLMs) have shown promise in clinical summarization, human evaluations of their effectiveness in synthesizing broader scientific literature and direct comparisons to expert-written syntheses remain scarce. We constructed a RAG-based agentic AI framework using three state-of-the-art LLMs: Sonnet, GPT-4o, and Llama 3.1. A headache specialist created 13 questions, three for prompt optimization and ten for evaluation. Ten headache specialists across the United States and Canada each wrote a summary for one question, yielding four summaries per question (expert, Sonnet, GPT-4o, and Llama). The experts, blinded to authorship, critically evaluated the summaries, excluding the topic for which they wrote a summary, based on correctness, completeness, conciseness, and clinical utility, scoring each from 1 to 10 using standardized rubrics. They also ranked the summaries by preference and indicated whether they believed each summary was written by an expert or an LLM. Our study, comparing LLM- and expert-written literature summaries evaluated by headache specialists, showed that expert-written summaries were preferred, although experts sometimes found it challenging to distinguish between human- and AI-generated summaries. We also identified key expert-valued features beyond standard evaluation metrics that can guide future refinement of both human and AI literature summarization pipelines.", "url": "https://wpnews.pro/news/ten-headache-specialists-versus-artificial-intelligence-for-clinical-literature", "canonical_source": "https://arxiv.org/abs/2606.05436", "published_at": "2026-06-06 04:00:00+00:00", "updated_at": "2026-06-06 04:18:15.633144+00:00", "lang": "en", "topics": ["large-language-models", "artificial-intelligence", "natural-language-processing", "ai-research", "ai-agents"], "entities": ["Sonnet", "GPT-4o", "Llama 3.1"], "alternates": {"html": "https://wpnews.pro/news/ten-headache-specialists-versus-artificial-intelligence-for-clinical-literature", "markdown": "https://wpnews.pro/news/ten-headache-specialists-versus-artificial-intelligence-for-clinical-literature.md", "text": "https://wpnews.pro/news/ten-headache-specialists-versus-artificial-intelligence-for-clinical-literature.txt", "jsonld": "https://wpnews.pro/news/ten-headache-specialists-versus-artificial-intelligence-for-clinical-literature.jsonld"}}