重新思考医疗人工智能的临床试验,并动态部署适应性系统
Jacob T Rosenthal1,2, Ashley Beecy3, Mert R Sabuncu4,5
1Tri-Institutional MD-PhD program of Weill Cornell/Rockefeller/Sloan Kettering, New York, NY, USA. jtr4002@med.cornell.edu.
NPJ digital medicine
|May 6, 2025
概括
临床试验对于安全部署人工智能 (AI) 是至关重要的. 动态部署是人工智能临床试验的新框架,可以实现医疗人工智能系统的自适应学习和实时验证.
科学领域:
- 医疗人工智能 医疗人工智能
- 临床试验设计 临床试验设计
- 医疗信息学 医疗信息学
背景情况:
- 越来越需要严格的临床试验,以确保在医疗机构安全有效地部署人工智能 (AI).
- 现有的临床试验框架可能无法充分解决先进人工智能的适应性和持续学习性质,特别是大型语言模型 (LLM).
研究的目的:
- 引入一个新的框架,动态部署,专门为涉及大型语言模型的AI临床试验设计.
- 能够开发和验证复杂的医疗人工智能系统,这些系统可以在临床环境中实时学习和适应.
主要方法:
- 提出AI临床试验的动态部署框架.
- 从新数据和用户交互中整合持续的现场学习.
- 在试验设计中实施实时监测和临床验证机制.
主要成果:
- 动态部署框架有助于创建适应性医疗人工智能系统.
- 使用真实世界的临床数据实现AI模型的持续学习和适应.
- 支持整个AI系统生命周期的持续实时监控和验证.
结论:
- 动态部署提供了一个可行的框架,用于进行适应性AI的临床试验,包括大型语言模型.
- 这种方法确保了人工智能的安全,有效和持续验证的整合到临床实践中.
- 解决了在医疗保健环境中不断学习人工智能系统所带来的独特挑战.
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