大型语言模型在医学中的未来景观
Jan Clusmann1,2, Fiona R Kolbinger1,3, Hannah Sophie Muti1,3
1Else Kroener Fresenius Center for Digital Health, TUD Dresden University of Technology, Dresden, Germany.
大型语言模型 (LLM) 为医学提供了强大的文本处理,但需要谨慎使用. 本综述探讨了人工智能 (AI) 在医疗保健和研究中的潜在好处和风险.
科学领域:
- 人工智能在医学中的应用
- 自然语言处理应用程序
背景情况:
- 大型语言模型 (LLM) 是先进的人工智能 (AI) 工具,擅长处理和生成类似人类的文本.
- 随着OpenAI的ChatGPT发布,公众对LLM的认识急剧增加,突出了他们在回答问题,总结和翻译等任务中的能力.
研究的目的:
- 为医疗领域LLM的潜在应用和局限性提供系统和全面的概述.
- 批判性地评估LLMs在临床实践,医学研究和医学教育中的作用.
主要方法:
- 对医学LLM的当前文献进行系统审查和全面分析.
- 评估在文本处理,知识传播和互动方面的LLM能力.
主要成果:
- 在基于文本的任务中,LLM展示了接近人类的能力,为民主化医学知识和改善医疗保健准入提供了潜力.
- 显著的风险包括由于缺乏问责制和透明度,可能导致广泛的错误信息和科学不当行为.
结论:
- 在医学上,LLM具有双重潜力:提高知识的可访问性,同时带来错误信息和道德挑战的风险.
- 仔细考虑LLM的局限性和强大的问责制框架对于负责任地融入临床实践,研究和教育至关重要.
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