A digital twin (DT) of a patient-specific heart offers significant potential in personalized medicine. However, its rapid and dynamic adaptation to an individual's live data and its predictive capability after adaptation remains central challenges. We examine this challenge from its two building blocks: DT formulation where mechanistic and data-driven models show competing merits and limitations, and DT optimization strategies that are largely driven by a reconstruction objective leading to un-identifiable models. We address both bottlenecks via HAPI -- an AI framework for building hybrid, adaptive, and predictive DTs with three key enablers. First, HAPI constructs a physics-integrated gray-box model in which an interpretable mechanistic backbone is augmented by a neural component that models its residual to the observed data. Second, rather than attempting to pre-encode all possible variations in a static hybrid model, HAPI enables rapid on-the-fly adaptation of the hybrid model to few-shot live data, achieved by feedforward meta-learners realizing amortized inference of both mechanistic and neural parameters of the hybrid model trained with predictive objectives. Finally, we show that this adaptivity corresponds to the construction of a conditional generative model (i.e., the hybrid DT) that endows it with theoretical identifiability and thus strong performance in predictive scenarios. We demonstrate the proof-of-concept of HAPI in cardiac electrophysiology using a hybrid monodomain model with mechanistic reaction kinetics and neural graph diffusion. Across synthetic and real-data studies, we show that HAPI's mechanistic-neural hybridization and predictive adaptation are critical for obtaining identifiable DTs with strong predictive and out-of-distribution capabilities.

翻译：患者特异性心脏的数字孪生（DT）在个性化医疗中具有巨大潜力。然而，其快速动态适应个体实时数据的能力以及适应后的预测能力仍是核心挑战。我们从两个构建模块审视此挑战：DT构建中，机制模型与数据驱动模型展现出相互竞争的优劣与局限；DT优化策略则主要受重建目标驱动，导致模型不可辨识。我们通过HAPI（一个用于构建混合、自适应与预测性DT的AI框架）及三项关键赋能技术来解决这两大瓶颈。首先，HAPI构建了一个物理集成的灰箱模型，其中可解释的机制主干通过神经组件进行增强，该组件对模型与观测数据之间的残差进行建模。其次，HAPI并非试图在静态混合模型中预编码所有可能变化，而是通过前馈元学习器实现混合模型机制参数与神经参数的摊销推理，并基于预测性目标进行训练，从而实现对少样本实时数据的快速即时适应。最后，我们证明这种适应性对应于条件生成模型（即混合DT）的构建，赋予其理论可辨识性，进而在预测场景中表现出强劲性能。我们利用带有机制反应动力学与神经图扩散的混合单域模型，在心脏电生理学中展示了HAPI的概念验证。通过合成数据与真实数据研究，我们表明HAPI的机制-神经混合化与预测性自适应对于获得具有强大预测与分布外能力的可辨识DT至关重要。