Learning Cardiac Electrophysiology Digital Twins Through Agentic Discovery of Hybrid Structure
arXiv:2606.18154v1 Announce Type: new Abstract: Building personalized cardiac electrophysiology (EP) digital twins requires identifying the appropriate model structure for each patient, not merely fitting parameters. Traditional methods rely on experts to manually prescribe hybrid physics-neural architectures, which requires deep domain expertise and does not transfer across patients. Recent works have applied large language models (LLMs) to generate or act as hybrid models. However, despite their promising generalization capacity, these LLM-based methods lack the structural priors needed for
The rapid advancement in large language models and foundation models is enabling their application to complex scientific and engineering problems previously requiring extensive human expertise.
This development allows for the creation of highly personalized and adaptive digital twins across various complex systems, moving beyond static models to dynamic, AI-driven simulations.
The process of building patient-specific models shifts from expert-driven manual prescription to automated, agentic discovery of hybrid structures using AI, accelerating personalized medicine and engineering design.
- · Personalized Medicine Sector
- · AI/ML Research & Development
- · Digital Twin Developers
- · Healthcare Practitioners
- · Traditional Manual Modeling Approaches
- · Companies reliant on static simulation tools
Personalized treatment plans for cardiac conditions become more accurate and accessible through AI-driven digital twins.
The methodology could generalize to other complex biological and engineered systems, accelerating research and development in diverse fields.
This could lead to a 'democratization' of advanced modeling, allowing smaller teams or less specialized experts to develop sophisticated simulations.
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Read at arXiv cs.AI