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超越黑盒人工智能:用于痴呆症护理的可解释混合系统
Matthew J Y Kang1,2, Wenli Yang3, Monica R Roberts4
1Neuropsychiatry Centre The Royal Melbourne Hospital Melbourne Victoria Australia.
基金会模型 (FMs) 对阿尔茨海默病和相关痴呆症 (ADRD) 护理有希望. 建议采用混合人工智能 (AI) 方法,将机器学习与临床知识和监督相结合,以提高床边采用的可解释性和可靠性.
科学领域:
- 神经科学是一个神经科学.
- 人工智能的人工智能
- 临床信息学 临床信息学
背景情况:
- 基础模型 (FMs) 越来越多地应用于阿尔茨海默病和相关痴呆症 (ADRD).
- 目前在床边的FM采用受到解释性和可靠性问题的限制,包括不透明的推断和幻觉.
- 在FM的弱因果依据阻碍了它们在复杂的神经疾病中的临床实用性.
研究的目的:
- 分析阻碍ADRD护理基础模型在临床采用的差距.
- 提出混合人工智能 (AI) 框架,将统计学学习与临床知识和监督相结合.
- 概述一个路线图,为ADRD开发可问责和可解释的AI工具.
主要方法:
- 对当前用于ADRD的AI中可解释性和可靠性差距的分析.
- 提出一个三层次的混合AI整合框架:知识检索,上下文决策支持和自适应优化.
- 使用临床示例和多式联络数据展示混合人工智能潜力.
主要成果:
- 确定了关键的挑战:不透明的推断,幻觉和弱的因果依据.
- 提出了一种混合人工智能框架,将统计学学习与可计算的临床知识和临床医生监督相结合.
- 展示了生物标志物解释,多式联络数据集成和数字治疗方面的潜在应用.
结论:
- 混合人工智能为克服ADRD当前FM的局限性提供了一条道路.
- 结构化的整合框架和务实的评估对于临床AI采用至关重要.
- 拟议的路线图旨在将AI进步转化为安全,公平和有效的ADRD护理工具.
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