多尺度数字双胞胎用于个性化医疗.
1Department of Epidemiology and Public Health, Foch Hospital, Suresnes, France.
Frontiers in digital health
|March 4, 2026
概括
多尺度数字双胞胎 (MSDTs) 集成各种健康数据,用于个性化医疗. 未来的MSDT承诺动态的健康建模和定制的干预措施,克服当前的局限性.
科学领域:
- 生物医学信息学 生物医学信息学
- 计算生物学 计算生物学
- 个性化医疗是个性化的医疗.
背景情况:
- 个性化医疗需要整合的健康数据,而不仅仅是孤立的流.
- 数字双胞胎 (DTs) 提供个性化的生物模拟,但在狭窄的数据源中面临局限性.
- 下一代DT需要多尺度的数据集成,以准确地建模健康和疾病.
研究的目的:
- 审查多尺度数字双胞胎 (MSDTs) 的概念,技术和临床方面.
- 探索支持技术和MSDT在动态健康干预中的应用.
- 确定MSDT开发和实施中持续存在的挑战.
主要方法:
- 对MSDT的概念基础和技术架构的审查.
- 支持技术的综合:多式联网数据融合,图形神经网络,因果推理,强化学习和混合AI机制模型.
- 临床应用的分析和伦理考虑.
主要成果:
- MSDT整合了分子,细胞,组织,器官,临床,行为和环境数据.
- 关键技术促进了动态健康轨迹建模和个性化干预.
- 重要障碍包括数据整合,道德治理,偏见减轻和监管适应.
结论:
- MSDTs代表了个性化医学的关键进步,使全面的健康模拟成为可能.
- 克服技术和道德挑战对于实现MSDT的全部临床潜力至关重要.
- 未来的研究应该专注于强大的数据集成和对MSDT负责的AI实施.
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