MambaCapsule: Towards Transparent Cardiac Disease Diagnosis with Electrocardiography Using Mamba Capsule Network
arXiv:2407.20893v2 Announce Type: replace-cross Abstract: Cardiac arrhythmia, a condition characterized by irregular heartbeats, often serves as an early indication of various heart ailments. With the advent of deep learning, numerous innovative models have been introduced for diagnosing arrhythmias using Electrocardiogram (ECG) signals. However, recent studies solely focus on the performance of models, neglecting the interpretation of their results. This leads to a considerable lack of transparency, posing a significant risk in the actual diagnostic process. To solve this problem, this paper
The proliferation of deep learning models in critical applications like medical diagnosis is increasingly highlighting the need for interpretability and transparency, pushing researchers to develop explainable AI approaches.
This development is crucial for bridging the gap between advanced AI performance and its practical, trustworthy deployment in highly regulated and sensitive sectors like healthcare, ensuring diagnostic confidence and ethical application.
The focus shifts from solely maximizing diagnostic accuracy to integrating transparency and interpretability into AI models for medical diagnosis, potentially accelerating regulatory approval and clinical adoption.
- · Healthcare providers
- · Patients
- · AI ethics researchers
- · Explainable AI (XAI) developers
- · Black box AI model developers
- · Companies neglecting interpretability standards
More transparent and trustworthy AI diagnostic tools will become available for cardiac care.
Increased patient and physician confidence in AI-driven medical diagnoses could lead to faster adoption and better healthcare outcomes.
The demand for explainable AI principles could expand to other critical sectors, influencing regulatory frameworks for general AI deployment.
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Read at arXiv cs.AI