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大型语言模型能否为父母提供关于慢性脏疾病的准确和高质量的信息?
Rüya Naz1, Okan Akacı2, Hakan Erdoğan3
1Bursa Yüksek Ihtisas Research and Training Hospital, University of Health Sciences, Bursa, Turkey.
Journal of evaluation in clinical practice
|July 3, 2024
概括
人工智能 (AI) 大型语言模型 (LLM) 在向家长提供关于慢性病 (CKD) 的信息方面显示出高准确性. 然而,由于与参考来源相比的潜在限制,建议谨慎使用.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 患者教育 患者教育
背景情况:
- 大型语言模型 (LLM) 是人工智能工具,可以生成类似人类的文本.
- 它们在医学中的应用正在调查中.
- 在特定医疗领域的LLM的性能和质量需要评估.
研究的目的:
- 比较AI LLMs (ChatGPT,Gemini,Copilot) 为父母提供有关慢性病 (CKD) 的信息.
- 评估与参考来源相比,LLM生成的信息的准确性和质量.
主要方法:
- 确定了40个关于CKD的常见问题.
- 评估了针对脏疾病的LLM反应:改善全球结果指南.
- 使用F1,精度和回忆分数来评估准确性.
- 使用五分全球质量评分 (GQS) 测量响应质量.
主要成果:
- 双子座和ChatGPT在诊断和生活方式类别中表现出高准确度 (F1分数为1和0.89).
- 所有LLM在诊断,治疗和生活方式方面都表现出强烈的回忆力.
- 双子号实现了最高的平均GQS (3.46 ± 0.55),超过了ChatGPT 3.5 (1.93 ± 0.63) 和Copilot (2.02 ± 0.69) 的表现.
结论:
- LLM为家长提供高度准确的CKD信息,但与参考来源相比,LLM存在局限性.
- 存在错误信息和误解的可能性.
- 患者和家长应谨慎使用LLM来获取医疗信息.
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