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本文引用的文献

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Evaluating the effectiveness of large language models in abstract screening: a comparative analysis.评估大型语言模型在摘要筛选中的有效性：一项对比分析。
Syst Rev. 2024 Aug 21;13(1):219. doi: 10.1186/s13643-024-02609-x.
2
How to optimize the systematic review process using AI tools.如何使用人工智能工具优化系统评价过程。
JCPP Adv. 2024 Apr 23;4(2):e12234. doi: 10.1002/jcv2.12234. eCollection 2024 Jun.
3
Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence.基于人工智能的诊断和预后预测模型研究报告指南（TRIPOD-AI）和偏倚风险工具（PROBAST-AI）制定方案。
BMJ Open. 2021 Jul 9;11(7):e048008. doi: 10.1136/bmjopen-2020-048008.
4
An evaluation of DistillerSR's machine learning-based prioritization tool for title/abstract screening - impact on reviewer-relevant outcomes.评估基于机器学习的 DistillerSR 优先筛选工具在标题/摘要筛选中的应用——对与评审员相关结果的影响。
BMC Med Res Methodol. 2020 Oct 15;20(1):256. doi: 10.1186/s12874-020-01129-1.
5
The semi-automation of title and abstract screening: a retrospective exploration of ways to leverage Abstrackr's relevance predictions in systematic and rapid reviews.标题和摘要筛选的半自动化：一种利用 Abstrackr 的相关性预测进行系统和快速综述的回溯性探索方法。
BMC Med Res Methodol. 2020 Jun 3;20(1):139. doi: 10.1186/s12874-020-01031-w.
6
Software tools to support title and abstract screening for systematic reviews in healthcare: an evaluation.支持医疗保健系统评价标题和摘要筛选的软件工具：评价。
BMC Med Res Methodol. 2020 Jan 13;20(1):7. doi: 10.1186/s12874-020-0897-3.
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Toward systematic review automation: a practical guide to using machine learning tools in research synthesis.迈向系统评价自动化：在研究综合中使用机器学习工具的实用指南。
Syst Rev. 2019 Jul 11;8(1):163. doi: 10.1186/s13643-019-1074-9.
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A question of trust: can we build an evidence base to gain trust in systematic review automation technologies?信任的问题：我们能否建立一个证据基础，以获得对系统评价自动化技术的信任？
Syst Rev. 2019 Jun 18;8(1):143. doi: 10.1186/s13643-019-1062-0.
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RobotReviewer: evaluation of a system for automatically assessing bias in clinical trials.机器人评审员：用于自动评估临床试验偏倚的系统评估
J Am Med Inform Assoc. 2016 Jan;23(1):193-201. doi: 10.1093/jamia/ocv044. Epub 2015 Jun 22.
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Reducing workload in systematic review preparation using automated citation classification.使用自动引文分类减少系统评价准备工作中的工作量。
J Am Med Inform Assoc. 2006 Mar-Apr;13(2):206-19. doi: 10.1197/jamia.M1929. Epub 2005 Dec 15.

利用人工智能增强健康研究中的系统评价：高级工具和挑战。

Leveraging artificial intelligence to enhance systematic reviews in health research: advanced tools and challenges.

机构信息

Health Services and Outcomes Research, National Healthcare Group, Level 4 @ NSC, 1 Mandalay Rd, Singapore, 308205, Singapore.

Tan Tock Seng Hospital, Singapore, Singapore.

出版信息

Syst Rev. 2024 Oct 25;13(1):269. doi: 10.1186/s13643-024-02682-2.

DOI:10.1186/s13643-024-02682-2
PMID:39456077
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11504244/
Abstract

Artificial Intelligence (AI) is transforming systematic reviews (SRs) in health research by automating processes such as study screening, data extraction, and quality assessment. This perspective highlights recent advancements in AI tools that enhance efficiency and accuracy in SRs. It discusses the benefits, challenges, and future directions of AI integration, emphasising the need for human oversight to ensure the reliability of AI outputs in evidence synthesis and decision-making in healthcare.

摘要

人工智能（AI）正在通过自动化研究筛选、数据提取和质量评估等流程，改变健康研究中的系统评价（SR）。本文从这一视角强调了 AI 工具在提高 SR 效率和准确性方面的最新进展，探讨了 AI 集成的益处、挑战和未来方向，强调了在医疗保健中的证据综合和决策中，需要人为监督以确保 AI 输出的可靠性。