arXiv:2607.01282v1 Announce Type: new Abstract: In light of strides in Arti cial Intelligence (AI) and its wide spread application, challenges persist in the interpretability of AI models, particularly within specialized domains like healthcare, such as electro cardiograph (ECG) recognition. Rather than relying solely on end-to-end convolutional neural networks, this paper introduces a novel approach using a domain knowledge-based graph convolution network for ECG recognition. Key landmarks points of PRQST, vital to ECG interpreta tion, are incorporated as domain knowledge. The double-stream d
The continuous advancements in AI research, particularly in specialized medical applications, are driving the development of more interpretable and robust models for critical tasks like ECG recognition.
This paper highlights a growing trend towards integrating domain-specific knowledge into AI models, which is crucial for improving trustworthiness and adoption in highly regulated and sensitive sectors like healthcare.
The focus is shifting from purely black-box end-to-end AI models to hybrid approaches that leverage expert domain knowledge, potentially leading to more reliable and explainable diagnostic tools.
- · AI in healthcare
- · Medical diagnostics
- · Deep learning researchers
- · Patients with cardiovascular conditions
- · Purely black-box AI models
- · Traditional ECG analysis methods
Improved accuracy and interpretability of AI-driven medical diagnostics, particularly for cardiovascular health.
Increased regulatory confidence and faster adoption of AI tools within clinical settings due to enhanced transparency.
A potential shift in AI research towards more human-in-the-loop and domain-expert informed model designs across various industries.
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