医学中的人工智能:是时候把耳语镜挂起来了吗?
1Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, New South Wales, Australia.
Internal medicine journal
|September 8, 2023
概括
由于与临床环境不匹配,医疗保健中的人工智能 (AI) 承诺很难实现. 专注于特定的机器学习 (ML) 工具及其整合到临床实践中对于实现AI至关重要.
科学领域:
- 医疗信息学 医疗信息学
- 临床决策支持系统 临床决策支持系统
- 医疗保健中的人工智能
背景情况:
- 人工智能 (AI) 在医疗保健中的广泛承诺尚未完全实现.
- 人工智能的技术能力与其在现实世界临床环境中的实际应用之间存在很大的差距.
- 目前的讨论往往集中在通用AI上,忽视了其基础方法的具体集成和实用性.
研究的目的:
- 将话语从AI的抽象承诺转移到机器学习 (ML) 在临床环境中的具体应用.
- 为评估医疗保健中的ML工具提出一个由四个关键问题组成的框架.
- 评估ML工具在支持临床任务和决策方面的实际贡献和可靠性.
主要方法:
- 分析将机器学习 (ML) 算法集成到临床工具中的分析.
- 评估ML输出如何影响临床任务和决策过程.
- 使用四个问题框架检查两个示例的ML工具: (1) 算法功能, (2) 输出集成, (3) 临床贡献和 (4) 临床医生依赖.
主要成果:
- 机器学习 (ML) 是一种适用于特定,狭义的临床任务的自动化方法.
- 机器学习工具可以有效地自动化某些临床过程,提高效率.
- 该研究表明,ML目前无法自动化复杂的临床决策和任务的全部范围.
结论:
- 关于医疗保健中的AI讨论必须超越一般承诺,专注于机器学习 (ML) 工具的具体实施和评估.
- 使用有针对性的问题进行结构化的方法是必要的,以了解ML在临床实践中的真正价值和可靠性.
- 虽然机器学习为特定任务提供了有价值的自动化,但它不太可能取代临床医生的全面决策能力.
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