妇产科医生接受GPT:客观评估人工智能模型在患者教育中的作用
Lindsey Burleson1, Ella Boardley1, Alexandra LaShell1
1Obstetrics and Gynecology, Indiana University School of Medicine, Indianapolis, USA.
大型语言模型 (LLM) 在产妇科医生患者教育中表现有前途. 虽然CoPilot得分很高,但ACOG常见问题和ChatGPT更被专家推用于患者咨询.
科学领域:
- 医疗保健中的人工智能
- 医疗信息学 医疗信息学
- 生成型人工智能应用程序
背景情况:
- 大型语言模型 (LLM) 在医疗保健中越来越多地被使用.
- 现有的研究重点是医学教育中的LLM,而不是患者教育.
- 没有研究专门评估了产科和妇科 (OB-GYN) 患者教育中的LLMs.
研究的目的:
- 评估LLM在妇产科医生患者教育和咨询方面的潜力.
- 为了将LLM的答案与已建立的资源比较,例如ACOG常见问题.
- 为了评估LLM在常见的妇产科医生患者查询上的表现.
主要方法:
- 专家审查员评估了来自三个LLM和ACOG常见问题中的答案.
- 一个标准化标签被用来评估对10个常见的妇产科医生临床问题的答案.
- 评估人员评价了资源对患者咨询的适用性.
主要成果:
- 微软的CoPilot在专家评审人员的评分中取得了更高的成绩.
- ACOG常见问题和ChatGPT更频繁地被推用于患者咨询.
- 评估了LLM的准确性和患者友好性.
结论:
- 在妇产科医生患者教育和咨询中,LLM具有潜在的作用.
- 专家评估表明,建议患者使用LLM的细微方法.
- 需要进一步的研究,以优化LLM整合到病人护理途径.
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