ArticleJournal of medical Internet research2025
Evaluating Web Retrieval-Assisted Large Language Models With and Without Whitelisting for Evidence-Based Neurology: Comparative Study.
Article in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of Large Language Models in Chronic Disease Care: Mixed Methods Systematic Review and Thematic Synthesis.Journal of medical Internet research · 2026Pooled it
- Evaluation Methods for Inference-Time Retrieval-Augmented and Graph Retrieval-Augmented Large Language Models in Health Care: Scoping Review.Journal of medical Internet research · 2026Article
- Evaluating the First CE-Marked LLM-Based Chatbot for Evidence-Based Neurology.European journal of neurology · 2026Article
- Generative AI vs web search for patient education: a comparative evaluation of OSA information quality.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2026Article
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Authors and funding
4 authors.
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Abstract
backgroundLarge language models (LLMs) coupled with real-time web retrieval are reshaping how clinicians and patients locate medical evidence, and as major search providers fuse LLMs into their interfaces, this hybrid approach might become the new "gateway" to the internet. However, open-web retrieval exposes models to nonprofessional sources, risking hallucinations and factual errors that might jeopardize evidence-based care.
objectiveWe aimed to quantify the impact of guideline-domain whitelisting on the answer quality of 3 publicly available Perplexity web-based retrieval-augmented generation (RAG) models and compare their performance using a purpose-built, biomedical literature RAG system (OpenEvidence).
methodsWe applied a validated 130-item question set derived from the American Academy of Neurology (AAN) guidelines (65 factual and 65 case based). Perplexity Sonar, Sonar-Pro, and Sonar-Reasoning-Pro were each queried 4 times per question with open-web retrieval and again with retrieval restricted to aan.com and neurology.org ("whitelisted"). OpenEvidence was queried 4 times. Two neurologists, blinded to condition, scored each response (0=wrong, 1=inaccurate, and 2=correct); any disagreements that arose were resolved by a third neurologist. Ordinal logistic models were used to assess the influence of question type and source category (AAN or neurology vs nonprofessional) on accuracy.
resultsFrom the 3640 LLM answers that were rated (interrater agreement: κ=0.86), correct-answer rates were as follows (open vs whitelisted, respectively): Sonar, 60% vs 78%, Sonar-Pro, 80% vs 88%, and Sonar-Reasoning-Pro, 81% vs 89%; for OpenEvidence, the correct-answer rate was 82%. A Friedman test on modal scores across the 7 configurations was significant (χ
conclusionsRestricting retrieval to authoritative neurology domains yielded a clinically meaningful 8 to 18 percentage-point gain in correctness and halved output variability, upgrading a consumer search assistant to a decision-support-level tool that at least performed on par with a specialized literature engine. Lightweight source control is therefore a pragmatic safety lever for maintaining continuously updated, web-based RAG-augmented LLMs fit for evidence-based neurology.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.