基于自然语言处理的开放访问聊天机器人应用程序 (ChatGPT) 紧急分选预测的性能:初步的,基于场景的横截面研究
İbrahim Sarbay1, Göksu Bozdereli Berikol2, İbrahim Ulaş Özturan3,4
1Department of Emergency Medicine, Keşan State Hospital, Edirne, Turkey.
Turkish journal of emergency medicine
|August 2, 2023
概括
人工智能聊天机器人ChatGPT在紧急医疗分组中显示出公平的协议,在高急性病例中表现最好. 进一步的医学培训可能会提高其对所有分拣预测的准确性.
科学领域:
- 医疗保健中的人工智能
- 自然语言处理自然语言处理.
- 医学选系统 医疗选系统
背景情况:
- 聊天机器人在医疗保健中越来越多地用于患者互动.
- OpenAI的ChatGPT是一个著名的基于机器学习的对话性AI.
- 人工智能在紧急医疗 (EM) 预选中的应用需要进行调查.
研究的目的:
- 评估ChatGPT在预测紧急医疗分类类别方面的表现.
- 为了比较ChatGPT的选预测与专家的共识.
主要方法:
- 基于紧急严重性指数 (ESI) 手册的50个研究人员生成的案例场景的横截面研究.
- 两名EM专家确定了参考分类类别,第三名担任仲裁员.
- 记录了ChatGPT的分拣预测,并与过分分拣和不足分拣分析的专家共识进行了比较.
主要成果:
- 聊天GPT证明了与EM专家的公平协议 (科恩的卡帕:0.341).
- 总体而言,22%的病例被过度选,18%的病例被ChatGPT选不足.
- 高急性病例 (ESI-1和ESI-2) 的表现明显好,F1得分为0.821和AUC为0.846.
结论:
- 在EM分拣中ChatGPT的表现对高急性病例 (ESI-1,ESI-2) 最有希望.
- 人工智能工具可以帮助识别关键护理需求.
- 加强医疗知识的整合可以提高ChatGPT在所有分拣级别的准确性.
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