Background: Medical Digital Twins (MDTs) are computational representations of individual patients that integrate clinical, genomic, and physiological data to support diagnosis, treatment planning, and outcome prediction. However, most MDTs remain static or passively updated, creating a critical synchronization gap, especially in rare genetic disorders where phenotypes, genomic interpretations, and care guidelines evolve over time. Methods: We propose an agent-orchestrated digital twin framework using OpenClaw's proactive "heartbeat" mechanism and modular Agent Skills. This Autonomous Agent-orchestrated Digital Twin (AADT) system continuously monitors local and external data streams (e.g., patient-reported phenotypes and updates in variant classification databases) and executes automated workflows for data ingestion, normalization, state updates, and trigger-based analysis. Results: A prototype implementation demonstrates that agent orchestration can continuously synchronize MDT states with both longitudinal phenotype updates and evolving genomic knowledge. In rare disease settings, this enables earlier diagnosis and more accurate modeling of disease progression. We present two case studies, including variant reinterpretation and longitudinal phenotype tracking, highlighting how AADTs support timely, auditable updates for both research and clinical care. Conclusion: The AADT framework addresses the key bottleneck of real-time synchronization in MDTs, enabling scalable and continuously updated patient models. We also discuss data security considerations and mitigation strategies through human-in-the-loop system design.

翻译：摘要：背景：医学数字孪生（MDT）是整合临床、基因组和生理数据以支持诊断、治疗规划和预后预测的单个患者计算模型。然而，大多数MDT仍处于静态或被动更新状态，存在严重的同步滞后问题，尤其在罕见遗传病中，其表型、基因组解读及诊疗指南会随时间演变。方法：我们提出一种基于智能体编排的数字孪生框架，采用OpenClaw的主动式"心跳"机制及模块化智能体技能。该自主智能体编排的数字孪生（AADT）系统持续监控本地与外部数据流（如患者报告的表型及变异分类数据库更新），并执行自动化工作流以完成数据摄取、标准化、状态更新及触发式分析。结果：原型实现表明，智能体编排可同步持续更新MDT状态，涵盖纵向表型更新与演化的基因组知识。在罕见病场景中，该方法可实现更早诊断并更准确建模疾病进展。我们通过变异重新解读与纵向表型追踪两个案例研究，展示了AADT如何为研究与临床诊疗提供及时、可审计的更新。结论：AADT框架解决了MDT实时同步的关键瓶颈，可实现可扩展且持续更新的患者模型。同时通过人机协同系统设计讨论数据安全保障策略与缓解措施。