数字双胞胎作为全球学习健康和疾病模型,用于预防和个性化医学
Xinxiu Li1, Joseph Loscalzo2, A K M Firoj Mahmud3
1Medical Digital Twin Research Group, Department of Clinical Sciences Intervention and Technology, Karolinska Institute, Stockholm, Sweden.
Genome medicine
|February 7, 2025
概括
数字双胞胎通过创建虚拟患者模型,为无效药物提供了一个有希望的解决方案. 这些数字双胞胎集成实时数据以个性化治疗和改善医疗保健结果.
科学领域:
- 生物医学工程 生物医学工程
- 计算生物学 计算生物学
- 数字健康数字健康
背景情况:
- 不有效的药物由于疾病的复杂性和患者的变异性而带来了重大的医疗保健挑战.
- 疾病涉及复杂的遗传和环境相互作用,跨越多个生物尺度.
- 数字双胞胎 (DTs) 正在成为应对这些挑战的变革性方法.
研究的目的:
- 探索数字双胞胎在革命性预测,预防和个性化医学的潜力.
- 确定数字双胞胎临床实施的关键挑战和机会.
主要方法:
- 审查数字双胞胎在医疗保健中的概念和早期应用.
- 分析DT开发的多尺度生物和计算要求.
- 识别DT广泛采用和临床整合的障碍.
主要成果:
- 数字双胞胎在人工器官,瘤学,心脏病学和医院工作流程优化等领域表现有前途.
- 早期的应用表明了DT集成实时数据和模拟用于个性化治疗的潜力.
- 仍然存在重大挑战,包括数据集成,互操作性,用户界面设计和道德考虑.
结论:
- 克服实施挑战对于实现数字双胞胎在医疗保健中的全部潜力至关重要.
- 数字双胞胎可以从根本上改变医疗保健提供,以更具预测性,预防性和个性化的方法.
- 解决技术,伦理和监管方面的障碍对于全球平等地获得数据技术技术至关重要.
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