一个可扩展的多层AI采用模型,以支持6P医学的综合目标
1Department of Artificial Intelligence and Informatics, Mayo Clinic, 200 1St Street SW, Rochester, Minnesota, 55905, USA.
Studies in health technology and informatics
|October 3, 2025
概括
这项研究引入了一个可扩展的多层AI采用模型,用于6P医学. 该架构确保了各种医疗保健AI应用的灵活性,可扩展性,安全性和效率.
科学领域:
- 医疗保健中的人工智能
- 数字化健康转型
- 计算医学是一种计算医学.
背景情况:
- 人工智能 (AI) 融入6P医学 (预测,预防,个性化,参与性,精准导向和以公众为中心) 提出了复杂的挑战.
- 现有的框架往往缺乏各种医疗保健应用所需的可扩展性和灵活性.
- 解决计算基础设施,数据集成,安全和互操作性对于成功采用人工智能至关重要.
研究的目的:
- 为6P医学量身定制的可扩展多层AI采用模型提出建议.
- 为在各种医疗保健领域实施人工智能提供灵活的概念框架.
- 确保人工智能驱动的医疗保健解决方案的可扩展性,安全性,治理和效率.
主要方法:
- 为人工智能采用多层架构模型的开发.
- 考虑关键因素:计算基础设施,数据处理,医疗保健要求,安全,隐私,性能和互操作性.
- 概念设计侧重于灵活性和适应性.
主要成果:
- 一个灵活的,多层次的概念模型,用于在6P医学中采用人工智能.
- 该模型解决了关键的实施因素,包括基础设施,数据,安全和互操作性.
- 显示了可扩展性,安全性,治理和效率的潜力.
结论:
- 拟议的多层人工智能架构为6P医学提供了强大而适应性的解决方案.
- 在医疗保健中成功实施人工智能需要考虑技术和运营方面的整体方法.
- 该模型促进了AI在各种医疗保健环境中的集成,同时确保了关键的性能和安全指标.
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