EVL-ECG: Efficient ECG Interpretation With Multi-Aspect Heterogeneous Knowledge Distillation
arXiv:2605.29977v1 Announce Type: cross Abstract: High-fidelity ECG interpretation is increasingly reliant on massive foundation models, yet their deployment in clinical edge-care remains hindered by extreme computational demands. While knowledge distillation (KD) is a promising solution, traditional methods fail to capture the complex spatio-temporal dependencies of ECG signals when transferring knowledge across heterogeneous architectures. In this paper, we propose EVL-ECG, a framework specifically designed for cross-architecture distillation of cardiac diagnostic logic. EVL-ECG introduces t
The proliferation of massive foundation models in medical AI necessitates solutions for efficient deployment in real-world clinical settings, addressing the current computational bottlenecks.
This development can significantly improve the accessibility and practicality of advanced ECG interpretation, enabling wider adoption of AI in critical healthcare diagnostics, especially at the edge.
The ability to distil complex AI models into more efficient, deployable versions for medical use will allow sophisticated diagnostic capabilities to move from cloud-based systems to local, real-time clinical devices.
- · Healthcare providers
- · Medical device manufacturers
- · AI model developers
- · Patients with cardiac conditions
- · Developers focused solely on large, computationally intensive models
- · Traditional ECG interpretation methods
More accurate and faster cardiac diagnoses become available in diverse clinical settings, including remote and under-resourced areas.
The cost of deploying high-fidelity AI diagnostics decreases, fostering greater innovation in point-of-care medical AI applications.
Personalized, continuous cardiac monitoring powered by efficient AI could become widespread, preempting health crises and shifting towards preventive medicine models.
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Read at arXiv cs.LG