RT Journal Article
T1 Evaluating search engines and large language models for answering health questions
A1 Fernández Pichel, Marcos
A1 Pichel Campos, Juan Carlos
A1 Losada Carril, David Enrique
AB 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.
PB Springer Nature
YR 2025
FD 2025-03-10
LK https://hdl.handle.net/10347/44538
UL https://hdl.handle.net/10347/44538
LA eng
NO Fernández-Pichel, M., Pichel, J.C. & Losada, D.E. Evaluating search engines and large language models for answering health questions. npj Digit. Med. 8, 153 (2025). https://doi.org/10.1038/s41746-025-01546-w
NO The authors thank the financial support supplied by the Xunta de Galicia—Consellería de Cultura, Educación, Formación Profesional e Universidades (ED431G 2023/04, ED431C 2022/19) and the ERDF, which acknowledges the CiTIUS Research Center in Intelligent Technologies of the USC as a Research Center of the Galician University System. They also thank the financial support obtained from (i) project PID2022-137061OB-C22 (Ministerio de Ciencia e Innovación, Agencia Estatal de Investigación, Proyectos de Generación de Conocimiento; supported by the European Regional Development Fund) and (ii) project PLEC2021-007662 (MCIN/AEI/10.13039/501100011033, Ministerio de Ciencia e Innovación, Agencia Estatal de Investigación, Plan de Recuperación, Transformación y Resiliencia, Unión Europea-Next Generation EU).
DS Minerva
RD 1 may 2026