在系统性审查中对ChatGPT的选性能进行方法的洞察
Mahbod Issaiy1, Hossein Ghanaati1, Shahriar Kolahi1
1Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Science, Tehran, Iran.
BMC medical research methodology
|March 28, 2024
概括
这项研究表明,与全科医生相比,ChatGPT可以有效地选放射学摘要进行系统审查,实现高灵敏度并节省大量时间. 虽然它不是人类专家的替代品,但它为减少工作量提供了一个有希望的工具.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 系统审查选是耗时的.
- 机器学习需要训练数据和注释.
- 像ChatGPT这样的大型语言模型 (LLM) 提供了自动选的潜力.
研究的目的:
- 为了评估ChatGPT在自动化放射学方面的有效性,系统审查抽象查.
- 在没有先前培训数据的情况下评估ChatGPT的性能.
- 将ChatGPT的查精度和效率与全科医生进行比较.
主要方法:
- 展望模拟研究比较ChatGPT和全科医生 (GPs).
- 评估了三个子领域的1198个放射学摘要.
- 评估灵敏度,特异性,PPV,NPV,工作负载节约和评价者间协议 (Kappa).
主要成果:
- 聊天GPT在不到一个小时内选了摘要; 医生花了7-10天.
- 聊天GPT实现了95%的灵敏度和99%的NPV,超过了GP的共识.
- 工作负载节省率在40-83%之间,但ChatGPT具有较低的特异性和PPV (Kappa=0.27).
结论:
- 聊天GPT证明了在放射学中自动化系统审查查的巨大潜力.
- 它提供高灵敏度和大幅减少工作负载.
- 作为一个高效的第一线工具,补充人类的专业知识.
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