ArticleNPJ digital medicine2025
Evaluating search engines and large language models for answering health questions.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
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The trial behind it
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Who cites it
20 citing papers in PubMed.
- Bilingual Performance of Large Language Models in Answering Consumer Health Questions in English and Chinese: Comparative Benchmark Study.Journal of medical Internet research · 2026Article
- Evaluating the First CE-Marked LLM-Based Chatbot for Evidence-Based Neurology.European journal of neurology · 2026Article
- Artificial intelligence advancements for orthopaedic clinical reasoning: longitudinal assessment of newer models (ChatGPT-5, Grok-3, Gemini 2.5 Flash) compared to clinicians.Archives of orthopaedic and trauma surgery · 2026Article
- Review
- Large Language Model-Assisted Shared Decision-Making (SDM) in Rhinology: A Randomized Controlled Pilot Study.Laryngoscope investigative otolaryngology · 2026Article
- Fourier Kolmogorov-Arnold Network integrated into BioBERT-based model for Biomedical Named Entity Recognition.NPJ digital medicine · 2026Article
- Benchmarking large language model-based agent systems for clinical decision tasks.NPJ digital medicine · 2026Article
- Article
- Evaluating reasoning large language models with human-like thinking in ophthalmic question answering.BMJ open ophthalmology · 2026Article
- Evaluating Artificial Intelligence Models in Dermatology: Comparative Analysis.JMIR dermatology · 2025Article
- The ABCs of PEMs: Using Artificial Intelligence to Enhance the Readability of Patient Educational Materials in Pediatric Orthopaedics.Journal of the Pediatric Orthopaedic Society of North America · 2025Article
- Evaluating Web Retrieval-Assisted Large Language Models With and Without Whitelisting for Evidence-Based Neurology: Comparative Study.Journal of medical Internet research · 2025Article
- Comparative Assessment of Large Language Models in Optics and Refractive Surgery: Performance on Multiple-Choice Questions.Vision (Basel, Switzerland) · 2025Article
- Evolving Health Information-Seeking Behavior in the Context of Google AI Overviews, ChatGPT, and Alexa: Interview Study Using the Think-Aloud Protocol.Journal of medical Internet research · 2025Article
- Experimental evidence of the effects of large language models versus web search on depth of learning.PNAS nexus · 2025Article
- Ethical implications of using general-purpose LLMs in clinical settings: a comparative analysis of prompt engineering strategies and their impact on patient safety.BMC medical informatics and decision making · 2025Article
- Evaluating ChatGPT's Utility in Biologic Therapy for Systemic Lupus Erythematosus: Comparative Study of ChatGPT and Google Web Search.JMIR formative research · 2025Article
- Performance of five free large language models in dental trauma: a 30-day longitudinal benchmark study.Frontiers in oral health · 2025Article
- Chatbots and Diabetes: Is There Gender Bias?Journal of patient experience · 2025Article
- ChatGPT and human dietitian responses to diet-related questions on an online Q&A platform: A comparative study.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Search engines (SEs) have traditionally been primary tools for information seeking, but the new large language models (LLMs) are emerging as powerful alternatives, particularly for question-answering tasks. This study compares the performance of four popular SEs, seven LLMs, and retrieval-augmented (RAG) variants in answering 150 health-related questions from the TREC Health Misinformation (HM) Track. Results reveal SEs correctly answer 50-70% of questions, often hindered by many retrieval results not responding to the health question. LLMs deliver higher accuracy, correctly answering about 80% of questions, though their performance is sensitive to input prompts. RAG methods significantly enhance smaller LLMs' effectiveness, improving accuracy by up to 30% by integrating retrieval evidence.
Identifiers
What OpenQuestion holds
Registered trials
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.