向临床生成人工智能发展:概念框架
Nicola Luigi Bragazzi1, Sergio Garbarino2
1Human Nutrition Unit, Department of Food and Drugs, University of Parma, Parma, Italy.
JMIR AI
|June 14, 2024
概括
生成型人工智能 (AI) 可以通过分析数据来改善诊断和治疗来增强临床决策. 本文提出了11个验证范式和一个负责任的AI整合框架,以确保可靠性和患者安全.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 临床决策支持 临床决策支持
背景情况:
- 临床决策整合了证据,判断,伦理和患者的意见,以获得最佳的结果.
- 生成型人工智能 (AI) 为疾病诊断,治疗量身定制和患者管理提供了先进的能力.
- 人工智能产生的临床见解的可靠性和准确性存在担忧.
研究的目的:
- 提出验证AI在临床决策中的基于证据的性质的方法.
- 为临床上可解释,公平和负责任的AI引入一个模型.
- 倡导人工智能作为一种增强工具,增强,而不是取代临床医生的判断.
主要方法:
- 开发11个不同的"验证范式"来评估AI的基于证据的贡献.
- 为协作决策制定"临床医生,专家和患者循环人工智能"的概念.
- 强调人工智能系统的透明度,可理解性和道德基础.
主要成果:
- 拟议的人工智能集成通过数据分析来增强诊断,治疗和患者管理.
- 引入了11个验证范式,以确保AI可靠性和基于证据的应用.
- 对负责任的人工智能的框架促进了人工智能,临床医生和患者之间的透明度和合作.
结论:
- 生成型人工智能在改善临床决策和患者护理方面显示出重大前景.
- 验证范式和负责任的AI框架对于安全地利用AI的潜力至关重要.
- 人工智能应该增强临床判断,确保持续学习,道德遵守和患者福祉.
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