人工智能在元分析中的准确性:对ChatGPT 4.0和数据合成中的传统方法进行比较研究
Aman Goyal1, Muhammad Daoud Tariq2, Areeba Ahsan2
1Department of Internal Medicine, Seth GS Medical College and KEM Hospital, Mumbai 400012, Maharashtra, India. amanmgy@gmail.com.
World journal of methodology
|September 3, 2025
概括
在进行干预性心脏病分析时,ChatGPT显示出有前途,与人类进行的研究密切相匹配. 需要进行进一步的研究,以确保人工智能生成的分析对临床决策具有稳定性.
科学领域:
- 心脏病学
- 医疗信息学
- 人工智能
背景情况:
- 在以证据为基础的医学,特别是心脏病学中,元分析对于合成研究数据至关重要.
- 这项研究探讨了使用ChatGPT来改进元分析过程.
研究的目的:
- 评估ChatGPT在干预心脏病领域进行元分析的能力.
- 将人工智能生成的元分析与人类对相同主题的分析进行比较.
主要方法:
- 系统的PubMed搜索2024年干预心脏病学元分析.
- 随机选择五个元分析进行比较.
- 使用ChatGPT 4.0进行数据提取和元分析.
- 比较关键效应大小 (风险比率,危险比率,赔率比率) 和P值.
主要成果:
- 由ChatGPT生成的元分析与原始人体分析具有很高的一致性.
- 大多数效果测量和P值都非常相匹配,可靠区间的差异很小.
- 一个值得注意的例外:ChatGPT报告了不显著的支架血栓形成风险,与原始研究的显著发现相反.
结论:
- 在干预性心脏病学中,ChatGPT具有潜在的分析.
- 进一步的研究对于人工智能透明度和临床信任数据质量至关重要.
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