大型语言模型对患者关于骨关节炎的常见问题的表现:对ChatGPT-3.5,ChatGPT-4.0和困惑感进行比较分析
Mingde Cao1, Qianwen Wang2, Xueyou Zhang2
1Department of Orthopaedics and Traumatology, Faculty of Medicine, The Chinese University of Hong Kong, 999077 Hong Kong, China; Center for Neuromusculoskeletal Restorative Medicine (CNRM), The Chinese University of Hong Kong, 999077 Hong Kong, China.
Journal of sport and health science
|November 29, 2024
概括
大型语言模型 (LLM) 显示出回答骨关节炎 (OA) 问题的前景. 在测试的LLM中,ChatGPT-4.0显示了最高的准确性,尽管所有LLM都提供了全面的信息.
科学领域:
- 医疗保健中的人工智能
- 医疗信息检索 医疗信息检索
- 骨肌肉疾病 骨肌肉疾病
背景情况:
- 大型语言模型 (LLM) 越来越多地用于信息检索,提供上下文相关的响应.
- 骨关节炎 (OA) 是一种普遍存在的骨肌疾病,患者经常在网上寻找信息.
- 本研究评估了领先的LLMs在回答常见的OA相关查询时的准确性.
研究的目的:
- 评估三个大型语言模型 (LLM) 在回答骨关节炎 (OA) 相关问题的准确性和全面性.
- 为了比较ChatGPT-3.5,ChatGPT-4.0和Perplexity在提供OA信息方面的性能.
主要方法:
- 从25个常见问题中定义了6个涵盖OA的主题 (发病,危险因素,临床表现,诊断,治疗/预防,预后).
- 三位骨科专家在4分准确度尺度上对LLM的答案进行了评分,最终得分由共识决定.
- 满意的答案进一步评估了使用5分级的综合性.
主要成果:
- 聊天GPT-4.0表现出卓越的准确性 (64%的优秀评分),超过了聊天GPT-3.5 (40%) 和困惑 (28%) (p < 0.001).
- 所有的LLM都获得了高的平均综合性分数 (困惑度:3.88,聊天GPT-4.0:4.56,聊天GPT-3.5:5.96).
- 性能在各个领域都是一致的,ChatGPT-4.0也在"治疗和预防"类别中领先 (53.8%的优秀评分,p < 0.001).
结论:
- 在提供准确和全面的OA信息方面,LLM,特别是ChatGPT-4.0和Perplexity,具有显著的潜力.
- 持续的改进和有针对性的纠正误解对于进一步提高医学环境中的LLM准确性至关重要.
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