Learning Cardiac Electrophysiology Digital Twins Through Agentic Discovery of Hybrid Structure

2026-06-16 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation · Depth: Expert, quick

Summary

The LEADS framework is proposed for building personalized cardiac electrophysiology (EP) digital twins by automatically identifying appropriate model structures. Traditional methods require expert manual prescription of hybrid physics-neural architectures, which is labor-intensive and lacks transferability. While recent large language model (LLM)-based approaches show generalization, they often lack the structural priors for stable cardiac simulations. LEADS addresses this by formulating cardiac EP domain knowledge as a structured action space, enabling an LLM agent to discover hybrid models through an iterative reasoning-and-action loop. This process ensures candidate models are physically grounded, interpretable, and numerically stable, while gradient descent handles parameter fitting. Validated on synthetic data with three ground-truth reaction models and real cardiac EP data, LEADS significantly outperforms both human-designed hybrid models and other LLM-based hybrid modeling techniques.

Key takeaway

For AI Scientists and Research Scientists developing personalized physiological models, LEADS offers a novel approach to overcome the limitations of manual architecture design. You should consider adopting agentic discovery frameworks that leverage structured domain knowledge to automatically generate robust and stable hybrid models. This method promises to accelerate the development of accurate digital twins, reducing reliance on extensive expert intervention and improving model transferability across patients.

Key insights

An LLM agent discovers stable, personalized cardiac electrophysiology digital twin models using structured domain knowledge.

Principles

• Model structure identification is crucial for personalized digital twins.
• Domain knowledge can be structured as an LLM agent's action space.
• Hybrid models benefit from physically grounded design.

Method

LEADS employs an LLM agent in an iterative reasoning-and-action loop to select, combine, and refine hybrid models, with gradient descent handling parameter fitting for physically grounded and stable designs.

In practice

• Apply LLM agents for automated model structure discovery.
• Integrate domain knowledge into structured action spaces.
• Validate hybrid models on synthetic and real physiological data.

Topics

• Cardiac Electrophysiology
• Digital Twins
• LLM Agents
• Hybrid Models
• Model Discovery
• Personalized Medicine

Best for: AI Scientist, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.