数字精神病学中的语言模型:简化医疗保健材料的挑战
Ankit Aich1,2, Tingting Liu1, Salvatore Giorgi1
1National Institute on Drug Abuse, Baltimore, 21224, MD, USA.
NPP - digital psychiatry and neuroscience
|December 8, 2025
概括
大型语言模型 (LLM) 显示了简化复杂医疗语言的潜力,但目前的版本如GPT-3.5,GPT-4,GPT-4o,LLaMA-3和Mistral太不一致,无法在医疗保健环境中安全使用.
科学领域:
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
- 医学沟通 医学沟通
背景情况:
- 复杂的医学语言对患者的理解,满意度和参与造成了重大障碍.
- 有效的患者-提供者沟通对于治疗坚持和积极的健康结果至关重要.
- 大型语言模型 (LLM) 提供了一种潜在的技术解决方案,以减轻医疗保健中的语言挑战.
研究的目的:
- 评估突出的大型语言模型 (LLM) 在简化复杂的医疗保健信息方面的有效性.
- 评估LLM生成的简化医学文本与推标准的一致性和准确性.
主要方法:
- 五位领先的LLM (GPT-3.5,GPT-4,GPT-4o,LLaMA-3,Mistral) 的任务是简化医疗信息.
- 分析了输出的可读性,主题坚持性,以及与美国医学杂志推标准的一致性.
主要成果:
- LLM 证明了能够调整文本以接近目标阅读水平的能力.
- 在LLM输出的阅读水平中观察到显著的变化.
- 输出频繁偏离了最初的医学主题,表明缺乏精度.
结论:
- 虽然LLM在简化医疗术语方面表现有前途,但目前的模型表现出不一致性.
- 可读性和主题偏差的变化使得这些LLM不适合在面向患者的医疗保健沟通中直接应用.
- 需要进一步开发,以确保LLM在医疗保健环境中的安全性和可靠性.
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