助力健康研究系统审查过程中的生成人工智能的作用:系统审查
Muhammed Rashid1, Cheng Su Yi2, Thipsukhon Sathapanasiri1
1Department of Pharmacotherapy, College of Pharmacy, University of Utah, Salt Lake City, UT, USA.
概括
生成性人工智能 (GAI) 在医疗保健系统审查中表现有前途,用于制定问题和提取数据. 然而,不一致的可靠性意味着GAI尚未被推用于文献搜索或研究选择.
科学领域:
- 医疗保健研究
- 人工智能应用
- 系统审查方法
背景情况:
- 人工智能越来越多地被用于医疗保健.
- 生成性人工智能 (GAI) 正在成为系统审查 (SR) 流程的工具.
- 评估GAI在SR方面的表现对于其采用至关重要.
研究的目的:
- 在系统审查 (SR) 过程中总结生成AI (GAI) 的性能指标.
- 评估GAI在SR不同阶段的有效性.
主要方法:
- 搜索了多个数据库 (PubMed,EMBASE,Scopus,ProQuest) 到 2025 年 3 月.
- 包括将GAI与其他GAI或人类审查者进行比较的实验研究.
- 通过改进的诊断准确性研究质量评估v2评估研究质量.
- 使用叙事方法来总结发现.
主要成果:
- 其中包括30项使用GAI工具的研究,如ChatGPT,Bard和微软Bing AI.
- GAI在PICO (参与者,干预,比较者,结果) 制定和数据提取方面表现出有效性.
- 由于可靠性不一致,不建议使用GAI进行文献搜索和研究选择.
- GAI对偏差风险评估的证据是混合的.
- 针对GAI进行的研究通常质量很高.
结论:
- GAI为斯里兰卡的PICO制定和数据提取提供了有前途的支持.
- GAI有可能改善医疗保健的SR工作流程.
- 需要进一步验证和实践应用研究才能完全整合.
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