在系统审查中开发和评估用于数据库搜索的生成性AI聊天机器人
Wai San Wilson Tam1, Neo Tung2, Shi Xuan Lee1
1Alice Lee Centre for Nursing Studies, National University of Singapore, Singapore.
概括
生成型人工智能聊天机器人可以创建系统审查搜索,识别大约67-72%的研究. 虽然它们对于最初的策略制定是有用的,但它们并不能取代专家图书馆员.
科学领域:
- 信息科学 信息科学 信息科学
- 医学图书馆 医学图书馆员
- 研究中的人工智能.
背景情况:
- 系统性审查 (SRs) 需要广泛的,可重复的搜索,这是一个耗时且需要专业专业知识的过程.
- 生成性AI (GAI) 提供了一个简化SR搜索策略开发的机会,但经验证据有限.
研究的目的:
- 概述一种用于SR搜索策略开发的基于ChatGPT的定制聊天机器人创建方法.
- 评估这种GAI辅助方法的性能.
主要方法:
- 通过使用ChatGPT-4.0进行横截面评估,开发了一个模仿医学图书馆员的聊天机器人,用于基于PICO的搜索.
- 聊天机器人根据方法参考进行了培训,并通过试点测试进行了改进.
- 用P-I-O提示为PubMed和Embase生成了50个CochraneSR (2024) 的查询.
主要成果:
- 在试点测试期间,聊天机器人实现了83.7%的检索率.
- 在主要评估中,聊天机器人发现了包括在内研究的中位数为67.4% (索引研究为72.0%).
- 当结果不在摘要中或干预有不同的术语时,性能下降.
结论:
- GAI聊天机器人提供了一个快速的方法来生成初始的SR搜索策略,实现适度的研究识别率.
- 这些工具作为有价值的起点,但不能完全取代信息专业人员的专业知识.
- 提高GAI的表现可能包括整合图书馆员的专业知识,结构化的提示和受控的词汇,需要进一步的研究和透明的报告.
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