在儿科发育查中评估ChatGPT-4omni:直接与顺序提示
Pathrada Traipidok1, Papichaya Srisombundit1, Nattaporn Tassanakijpanich1
1Prince of Songkla University Faculty of Medicine, Hat Yai, Songkhla, Thailand.
BMJ paediatrics open
|March 3, 2025
概括
像ChatGPT-4o这样的大型语言模型显示出对儿科发育查的希望. 在这项研究中,直接提示更敏感,而顺序提示更具体.
科学领域:
- 儿科医疗保健技术 儿童医疗保健技术
- 人工智能在医学中的应用
- 发展心理学 发展心理学
背景情况:
- 发展查对于儿童的早期干预至关重要.
- 目前的选方法可能耗时且资源密集.
- 大型语言模型 (LLM) 为创新的医疗保健解决方案提供了潜力.
研究的目的:
- 评估ChatGPT-4o在儿科发育查中的疗效.
- 为了比较使用ChatGPT-4o的直接和顺序提示方法.
- 为了比较ChatGPT-4o的表现与婴儿发育的贝利尺度,第三版.
主要方法:
- 分析了106个儿科病例.
- 聊天GPT-4o使用了直接和顺序提示技术.
- 性能指标包括灵敏度,特异性和整体准确性.
主要成果:
- 直接提示实现了73.42%的灵敏度和69.81%的整体准确度.
- 序列提示显示了62.96%的特异性和67.92%的整体准确性.
- 这两种LLM提示方法都显示出发展查的潜力.
结论:
- 通过直接和顺序提示,ChatGPT-4o可以提高儿科发育查的效率和可访问性.
- 直接提示表现出更高的灵敏度,而顺序提示提供了更大的特异性.
- 需要进一步的研究来优化LLM在临床实践中的整合.
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