Article record

使用基于人工智能的平台支持证据合成研究中的检索：EAACI方法学委员会的一项研究。

Use of Artificial Intelligence-Based Platforms to Support Searches in Evidence Synthesis Studies: A Study of the EAACI Methodology Committee.

AI/ML鼻科IF 4.6Q2

文献信息

PMID	42728233
原文	在 PubMed 查看原文 ↗
发表日期	2026
作者	Bernardo Sousa-Pinto 查看其他 14 位作者 Antonio Bognanni；Paweł Jemioło；Rafael José Vieira；Thinh H Nguyen；Zachary Munn；Timothy Hugh Barker；Lemma N Bulto；Danilo di Bona；Jean Bousquet；Mário Dinis-Ribeiro；João A Fonseca；Mariette Awad；Manuel Marques-Cruz；Holger J Schünemann
作者单位	MEDCIDS - Department of Community Medicine, Information and Health Decision Sciences, Faculty of Medicine, University of Porto, Porto, Portugal. 查看其他 13 条单位信息 CINTESIS@RISE - Health Research Network, Faculty of Medicine, University of Porto, Porto, Portugal.；Clinical Epidemiology and Research Center (CERC), Department of Biomedical Sciences, Humanitas University, Milan, Italy.；Personalized Medicine, Asthma and Allergy, IRCCS Humanitas Research Hospital, Milan, Italy.；AGH University of Krakow, Krakow, Poland.；Chair of Epidemiology and Preventive Medicine, Faculty of Medicine Jagiellonian University Medical College, Krakow, Poland.；Division of Immunology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA.；Health Evidence Synthesis, Recommendations and Impact (HESRI), School of Public Health, University of Adelaide, Adelaide, Australia.；Department of Medical and Surgical Science, University of Foggia, Foggia, Italy.；Institute of Allergology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Berlin, Germany.；Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Allergology and Immunology, Berlin, Germany.；Precancerous Lesions and Early Cancer Management Group, Research Center of IPO Porto (CI-IPOP)/CI-IPOP@RISE (Health Research Group), Portuguese Institute of Oncology of Porto (IPO Porto)/Porto Comprehensive Cancer Center (Porto.CCC), Porto, Portugal.；Gastroenterology Department, Portuguese Institute of Oncology of Porto, Porto, Portugal.；Department of Electrical and Computer Engineering, American University of Beirut, Beirut, Lebanon.
期刊	Clinical and translational allergy
SCI 分区	Q2
IF	4.6
研究类型	AI/ML · 临床
所属专科	鼻科

中文摘要

目的： 展示对使用人工智能（AI）支持证据合成任务（特别是原始研究检索）在过敏或呼吸学领域的评估。
方法： 我们在2024年11月使用几种策略（即提示策略和检索方法）查询了三个专门用于识别科学出版物的基于AI的平台，以识别与过敏或呼吸学领域健康相关案例研究相关的原始研究。我们比较了使用基于AI的平台与在多个电子文献数据库中进行系统检索所识别的合格原始研究的数量。我们还比较了通过查询基于AI的平台识别的原始研究与在系统评价背景下获得的荟萃分析结果。最后，我们开发了一个结构化的方法学框架和报告清单，用于使用这些基于AI的平台。
结果： 在我们的主要案例研究中，一项为过敏性鼻炎及其对哮喘的影响（ARIA）指南提供信息的随机对照试验（RCT）系统评价，一种涉及搜索多个基于AI平台的策略识别了所有带有DOI的全文文章的85.7%，但未能识别在试验数据库中注册的未发表试验，导致总体识别了56.3%的合格RCT。当仅考虑使用基于AI策略识别的原始研究与系统评价识别的原始研究时，荟萃分析估计相似。我们在观察性研究的系统评价案例研究中观察到较低的性能。
结论： 我们提供了一个示例，说明如何评估基于AI的平台在支持证据合成方面的作用，重点关注过敏和呼吸学领域。

英文摘要

OBJECTIVE: To demonstrate an evaluation of the use of artificial intelligence (AI) in supporting tasks in evidence synthesis (particularly search for primary studies) in the allergy or respirology fields.
METHODS: We queried three AI-based platforms specialised on identifying scientific publications using several strategies (i.e., prompting strategies and search approaches) in November 2024 to identify primary studies related to health-related case studies in the allergy or respirology field. We compared how many eligible primary studies we identified using AI-based platforms versus in a systematic search in multiple electronic bibliographic databases. We also compared meta-analytical results obtained with the primary studies identified by querying AI-based platforms versus in the context of a systematic review. Finally, we developed a structured methodological framework and a reporting checklist for using these AI-based platforms.
RESULTS: In our main case study, a systematic review of randomised controlled trials (RCTs) informing the Allergic Rhinitis and its Impact on Asthma (ARIA) guidelines, a strategy involving searching multiple AI-based platforms identified 85.7% of all full articles with DOI, but failed to identify unpublished trials registered in trial databases, resulting in an overall identification of 56.3% eligible RCTs. Meta-analytical estimates were similar when considering only the primary studies identified using AI-based strategies versus those identified by the systematic review. We observed a lower performance in the case study of systematic reviews of observational studies.
CONCLUSIONS: We provide an example on how it is possible to evaluate AI-based platforms in the support of evidence synthesis, with a focus on the allergy and respirology fields.