在大型医疗保健系统中产生人工智能成本,这是收入周期中的一个例子
Michael L Burns1, Ssu-Ying Chen2, Chu-An Tsai2
1Department of Anesthesiology, University of Michigan, Ann Arbor, MI, USA. mlburns@med.umich.edu.
NPJ digital medicine
|September 30, 2025
概括
大型语言模型在医疗保健中显示出对文本分类的承诺. 替代模型比基础模型提供更好的准确性和更低的成本,尽管存在诸如计算成本和可靠性等挑战.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 大型语言模型 (LLM) 越来越多地应用于医疗保健,特别是医疗自由文本分类.
- 基础模型,如ChatGPT开发的模型,显示出潜力,但面临限制.
研究的目的:
- 评估与医学文本分类的基础模型相比,替代LLM的性能.
- 确定与在医疗保健环境中实施LLM相关的挑战.
- 为医疗保健系统探索具有成本效益的AI解决方案.
主要方法:
- 对医疗自由文本分类任务的不同大型语言模型的比较分析.
- 评估模型准确性,计算成本和可靠性.
主要成果:
- 与基础模型相比,替代的LLM显示出更高的准确性和更低的计算成本.
- 仍然存在重大挑战,包括高计算成本和确保模型可靠性用于临床使用.
结论:
- 结合本地和商业LLM的混合方法可能为医疗保健系统提供一个平衡且具有成本效益的解决方案.
- 解决计算成本和可靠性对于在医疗保健中广泛采用人工智能至关重要.
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