Vallée Alexandre
Department of Epidemiology and Public Health, Foch Hospital, Suresnes, France.
J Med Internet Res. 2025 Aug 5;27:e72411. doi: 10.2196/72411.
Digital twin (DT) technology is revolutionizing clinical practice by integrating diverse epidemiological data sources to create dynamic, patient-specific simulations. By leveraging data from genomics, proteomics, imaging, sociodemographics, and real-world behaviors, DTs provide a computational framework to model disease progression, optimize treatments, and personalize health care interventions. Through artificial intelligence (AI) and mathematical modeling, DTs facilitate predictive analytics for disease risk assessment, early diagnosis, and treatment response forecasting. This viewpoint explores the mathematical foundations of DTs, including differential equations for health trajectory modeling, Bayesian networks for multiomics integration, Markov models for disease progression, and reinforcement learning for treatment optimization. In addition, machine learning techniques such as recurrent neural networks and transformers enhance the predictive power of DTs by analyzing time-series clinical data and predicting future health events. The potential applications of DTs extend beyond individual patient care to public health surveillance, hospital resource management, and epidemiological modeling. However, several challenges persist, including data privacy concerns, computational infrastructure requirements, validation of predictive models, and regulatory compliance. Addressing these limitations requires interdisciplinary collaboration among health care providers, data scientists, and policy makers. With advancements in AI, wearable technology, and multiomics data integration, DTs are poised to reshape precision medicine. Future research should focus on refining computational efficiency, standardizing data interoperability, and ensuring ethical AI-driven decision-making. The continued evolution of DTs offers a transformative approach to proactive and personalized health care, reducing disease burden and enhancing patient outcomes.
数字孪生（DT）技术正在通过整合各种流行病学数据源来创建动态的、针对患者的模拟，从而彻底改变临床实践。通过利用基因组学、蛋白质组学、成像、社会人口统计学和现实世界行为的数据，数字孪生提供了一个计算框架，用于对疾病进展进行建模、优化治疗并使医疗保健干预措施个性化。通过人工智能（AI）和数学建模，数字孪生有助于进行疾病风险评估、早期诊断和治疗反应预测的预测分析。本文探讨了数字孪生的数学基础，包括用于健康轨迹建模的微分方程、用于多组学整合的贝叶斯网络、用于疾病进展的马尔可夫模型以及用于治疗优化的强化学习。此外，诸如循环神经网络和变换器等机器学习技术通过分析时间序列临床数据和预测未来健康事件来增强数字孪生的预测能力。数字孪生的潜在应用不仅限于个体患者护理，还扩展到公共卫生监测、医院资源管理和流行病学建模。然而，仍然存在一些挑战，包括数据隐私问题、计算基础设施要求、预测模型的验证以及法规合规性。解决这些限制需要医疗保健提供者、数据科学家和政策制定者之间的跨学科合作。随着人工智能、可穿戴技术和多组学数据整合的进步，数字孪生有望重塑精准医学。未来的研究应专注于提高计算效率、规范数据互操作性以及确保符合道德的人工智能驱动决策。数字孪生的持续发展为主动和个性化医疗保健提供了一种变革性方法，可减轻疾病负担并改善患者预后。
