评估认知表现:传统方法与聊天GPT
Xiao Fei1, Ying Tang1, Jianan Zhang1
1Department of Rehabilitation Medicine, The First People's Hospital of Changzhou, Changzhou, China.
Digital health
|August 19, 2024
概括
像ChatGPT这样的自然语言处理 (NLP) 模型在医学评估中表现有希望. 在评分和交互协议的改进改善了ChatGPT.
科学领域:
- 认知科学和人工智能是人工智能.
- 医疗信息学和数字健康
背景情况:
- 自然语言处理 (NLP) 模型,如ChatGPT,正在成为医学中基于文本的内容交付的潜在工具.
- 对于ChatGPT在支持认知性能评估方面的可靠性和全面性存在担忧,需要进一步研究其准确性.
研究的目的:
- 调查NLP模型 (GPT-3.5和GPT-4) 在与医疗保健专业人员相比评估认知表现的准确性和全面性.
- 探索改进评分协议和交互方法的潜力,以提高ChatGPT在认知评估中的实用性.
主要方法:
- 90名参与者的队列 (60名认知正常,30名中风幸存者) 接受了认知评估 (记忆,数值处理,口头流利,抽象思维).
- 评估由医疗保健专业人员和NLP模型 (GPT-3.5,GPT-4) 使用既定标准进行.
- 比较得分,并应用了对得分和互动的优化策略.
主要成果:
- 与健康个体的医生评估相比,GPT-3.5在记忆和语音评估中显示出显著的差异 (P < 0.001).
- 与医生评估相比,GPT-3.5在中风幸存者的口头评估中也表现出差异 (P = 0.002).
- 优化得分和交互协议部分缓解了这些差异.
结论:
- NLP模型,特别是GPT-4,可以产生与传统方法相比的认知评估结果,GPT-4显示与医生评级更接近.
- 对得分算法和交互协议的改进可以改善ChatGPT的结果与医生评估的一致性.
- 聊天GPT展示了多功能性和潜力,作为一个补充工具,在医学领域收集信息,特别是认知评估,即使在人口中,如中风.
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