在医学中创建和采用大型语言模型
Nigam H Shah1,2,3, David Entwistle1, Michael A Pfeffer1,2
1Stanford Health Care, Palo Alto, California.
JAMA
|August 7, 2023
概括
大型语言模型 (LLM) 提供医疗效益,但积极参与至关重要. 我们必须通过医学数据和现实世界的测试来指导LLM的发展, 以确保安全有效地融入医疗保健.
科学领域:
- 医疗信息学
- 医疗保健中的人工智能
- 临床决策支持
背景情况:
- 对大语言模型 (LLM) 对潜在医疗应用的兴趣日益增长.
- 目前在医学领域的LLM课程往往缺乏具体的医学培训数据和效益验证.
- 如果不积极参与LLM整合,可能会失去主动性.
研究的目的:
- 强调需要积极参与塑造LLM在医学中的应用.
- 强调负责开发和部署LLM医疗工具的重要性.
- 倡导将人工智能整合到医疗保健中的积极做法.
主要方法:
- 分析目前在医疗任务中使用LLM的趋势.
- 识别医疗保健LLM培训和验证方面的差距.
- 为积极塑造LLM开发和部署提出框架建议.
主要成果:
- 在没有足够的医疗数据培训或福利验证的情况下,LLM应用程序越来越多地用于医疗任务.
- 消极观察可能会让人无法控制LLM如何重塑医疗实践.
- 积极塑造需要提供相关数据,定义好处,并对现实世界进行评估.
结论:
- 积极参与对于负责任地利用LLM在医学中的好处至关重要.
- 提供医疗数据,说明好处,实践测试对于安全的LLM整合至关重要.
- 积极的塑造确保了LLM的作用是增强而不是破坏医疗实践.
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