医学中的人工智能:它是如何工作的,它是如何失败的
Rick Rejeleene1, Neil B Mehta2
1Special Fellow for Artificial Intelligence, Cleveland Clinic, Cleveland, OH rick.rejeleene@gmail.com.
Cleveland Clinic journal of medicine
|February 2, 2026
概括
人工智能 (AI),特别是大型语言模型,在医疗保健领域提供了巨大的潜力. 了解人工智能的能力和局限性对于安全有效的临床应用至关重要.
科学领域:
- 医疗保健技术 医疗保健技术 医疗保健技术
- 医疗人工智能的人工智能
- 临床信息学是一种临床信息学.
背景情况:
- 人工智能 (AI) 越来越多地融入医疗保健系统.
- 大型语言模型 (LLM) 代表了人工智能的重大进步,在医学中应用越来越多.
研究的目的:
- 为医疗保健专业人员阐明大型语言模型 (LLM) 的基本原则.
- 突出LLMs在临床环境中的实际能力和固有的局限性.
- 促进在医疗保健中安全有效地采用人工智能工具.
主要方法:
- 解释AI原则,重点关注法学士.
- 使用实用的临床示例来说明概念.
- 讨论LLM的功能和故障模式.
主要成果:
- 人工智能,特别是LLM,是医疗保健提供的一个变革力量.
- 有效和安全地使用LLM需要对其操作机制有充分的了解.
- 临床示例展示了当前LLM技术的实用性和限制.
结论:
- 医疗保健专业人员必须掌握LLMs的复杂性,以实现最佳整合.
- 意识到LLM的局限性对于减轻临床实践中的风险至关重要.
- 在医学中对人工智能的进一步教育对于推进医疗保健创新至关重要.
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