使用自动化工具促进对证据的批判性评估

Alexandra Bannach-Brown, Nils Hijlkema, Niels van Beuningen, Wynand Alkema, Carlijn R Hooijmans, Kimberley E Wever, Malcolm Macleod, Olena Maksym, Tomas Novotny, Lorenzo Niccolai, Vieri Emiliani
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引用次数: 0

摘要

欧洲食品安全局(EFSA)于2021年5月启动了“风险评估中用于证据管理的人工智能行动路线图”项目,旨在通过整合人工智能(AI)方法优化风险评估流程。一个关键的焦点是偏差风险(RoB)评估的自动化,这是评估研究方法学质量的系统评价中一个关键且耗时的步骤。根据框架合同OC/EFSA/AMU/2021/03 -具体合同3,该项目建立并试点了一个原型自动化工具,以支持偏见风险评估过程。本报告详细介绍了定义当前EFSA流程、选择适当的关键评估工具和用例、开发和验证基于零次学习(AutoCAT)的GPT模型的原型自动化工具所采取的步骤。实施阶段包括将AutoCAT与现有的系统审查平台集成,并在EFSA环境中部署。评估阶段包括使用开发和系统测试数据集评估工具,以及与EFSA科学专家进行用户测试。讨论了该项目的成果,强调了试点原型的局限性,并提出了未来的工作和在其他情况下的潜在应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Facilitating critical appraisal of evidence using automation tools

The European Food Safety Authority (EFSA) initiated the “Roadmap for actions on Artificial Intelligence for evidence management in risk assessment” project in May 2021, aiming to optimize risk assessment processes by integrating Artificial Intelligence (AI) methodologies. A key focus is the automation of the Risk of Bias (RoB) assessment, a critical and time-consuming step in systematic reviews that evaluates the methodological quality of studies. Implemented under Framework Contract OC/EFSA/AMU/2021/03 - Specific Contract 3, this project built and piloted a prototype automation tool to support the risk of bias assessment process. This report details the steps taken to define current EFSA processes, select appropriate critical appraisal tools and use cases, and develop and validate a prototype automation tool based on a GPT model with zero-shot learning (AutoCAT). The implementation phase includes integrating AutoCAT with existing systematic review platforms, and deployment within the EFSA environment. The assessment phase involved evaluating the tool with development and system test datasets, and user testing with EFSA scientific experts. The project's outcomes are discussed, highlighting the limitation of the pilot prototype and proposing future work and potential applications in other contexts.

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