使用大型语言模型的潜力和陷,如ChatGPT,GPT-4或LLaMA作为临床助理
Jingqing Zhang1,2, Kai Sun1,2, Akshay Jagadeesh1
1Pangaea Data Limited, London, SE1 7LY, United Kingdom.
概括
大型语言模型 (LLM) 在医疗保健中显示出患者识别的前景,在某些情况下,GPT-4的表现优于其他模型. 然而,由于精度的局限性和伦理方面的担忧,在广泛临床使用之前需要进一步开发.
科学领域:
- 人工智能在医学中的应用
- 临床信息学 临床信息学
- 计算健康 计算健康
背景情况:
- 大型语言模型 (LLM) 越来越多地被用于医疗保健应用.
- 它们在临床环境中的实用性,特别是用于患者识别和决策支持,需要严格的评估.
研究的目的:
- 评估LLM (ChatGPT,GPT-4,LLaMA) 在识别特定疾病患者方面的表现.
- 将LLM能力与使用电子健康记录 (EHR) 的传统机器学习模型进行比较.
主要方法:
- 使用MIMIC-III数据库与黄金标记的EHR用于患者识别.
- 评估了慢性阻塞性肺病 (COPD),慢性病 (CKD),初级胆道肝硬化 (PBC) 和癌症的模型.
- 使用F1得分,精度和特异性等指标比较GPT-4,ChatGPT和LLaMA性能.
主要成果:
- 对于COPD,CKD和PBC (F1分数≥85%) 的疾病特定机器学习模型,GPT-4的性能与疾病特定机器学习模型相比较或优越.
- 对于初级胆汁硬化,GPT-4的F1得分比传统模型高4.23%.
- 与GPT-4相比,ChatGPT和LLaMA的性能较低;几次射击提高了精度和特异性,但降低了灵敏度.
结论:
- 在临床任务中,LLM具有潜在的补充工具,例如患者识别.
- 必须解决准确性问题 (错误阳性/负性),解释性限制以及道德问题 (数据隐私,透明度).
- 未来的研究应该专注于改进LLM培训数据和模型设计,以提高临床实用性.
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