评估一个大型语言模型能够回答临床医生对证据摘要的要求的能力
medRxiv : the preprint server for health sciences
|May 15, 2024
概括
生成型人工智能 (AI) 工具在回答临床问题方面表现有前途,但参考验证仍然是一个挑战. 需要进一步的研究,以有效地将AI整合到医学图书馆工作流程中.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
背景情况:
- 临床证据要求对于明智的医疗决策至关重要.
- 医学图书馆员为临床问题提供黄金标准的证据综合.
研究的目的:
- 评估生成性AI工具 (GPT-4) 在回答临床问题的性能.
- 将人工智能产生的反应与医学图书馆的专家综合进行比较.
主要方法:
- 临床问题来源于现有的数据库.
- 一个标准化的提示 (COSTAR框架) 被用于AI查询.
- 人工智能响应的准确性和完整性与图书馆员生成的摘要进行了评估.
- 由AI提供的引用被验证为准确.
主要成果:
- 人工智能工具正确回答了83.3%的临床问题,部分回答了16.2%.
- 在各个问题类别中没有发现显著的绩效差异.
- 只有37%的人工智能生成的引用被证实为非制造的.
结论:
- 生成型人工智能在临床问题答案方面展示了有希望的功能.
- 在验证人工智能生成的参考文献和确保额外概念的事实准确性方面存在挑战.
- 需要进一步调查,以优化AI整合到医学图书馆工作流程中.
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