为临床证据综合利用生成性AI需要确保可靠性
Gongbo Zhang1, Qiao Jin2, Denis Jered McInerney3
1Columbia University, Department of Biomedical Informatics, New York, 10032, US.
ArXiv
|October 1, 2025
概括
生成型人工智能可以通过合成临床数据来帮助基于证据的医学. 然而,确保人工智能模型是值得信赖的,公平的和包容性的,对于可靠的自动化证据合成至关重要.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能的人工智能
- 基于证据的医学基于证据的医学.
背景情况:
- 基于证据的医学 (EBM) 通过将最佳证据整合到临床实践中来提高医疗保健质量.
- 临床证据的指数增长在数据收集,评估和合成方面带来了重大挑战.
- 包括大型语言模型 (LLM) 在内的生成人工智能 (AI) 为管理这种证据爆炸提供了潜在的解决方案.
研究的目的:
- 探索生成AI在自动化EBM证据合成中的作用.
- 讨论在这个领域开发可靠的人工智能模型的挑战和考虑因素.
主要方法:
- 这一观点回顾了与证据综合相关的生成人工智能的当前进展.
- 它分析了用于临床决策支持的AI模型中对问责制,公平性和包容性的要求.
主要成果:
- 生成型人工智能在简化临床证据的收集,评估和合成方面表现有前途.
- 在开发负责任,公平和包容的AI系统方面,仍然存在重大挑战.
结论:
- 生成型人工智能有可能在EBM中彻底改变证据合成.
- 需要进一步的研究和开发,以确保AI在医疗保健中的可靠性和伦理应用.
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