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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Are Artificial Intelligence Models Listening Like Cardiologists? Bridging the Gap Between Artificial Intelligence and

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Summary

Explainable AI (XAI) and attention mechanisms enhance deep learning for heart-sound classification. Integrating multi-head attention with ResNet50 improved accuracy to 97.3% and interpretability, focusing on clinically relevant heart sound features.

Keywords:
Grad-CAMattention mechanismdeep learningexplainable AI (XAI)heart-sound classification

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Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Biomedical Signal Processing

Background:

  • Deep learning automates heart-sound classification but struggles with rare conditions and relies on clinician expertise.
  • Current automatic segmentation methods introduce variability, impacting classification accuracy and trust.
  • Pretrained models show inconsistent accuracy, necessitating interpretable methods to validate results and understand clinical relevance.

Purpose of the Study:

  • To assess if deep learning models focus on clinically relevant heart sound features using explainable AI (XAI).
  • To investigate if attention mechanisms improve classification performance and focus on meaningful signal segments.
  • To evaluate a deep learning model on a manually segmented dataset using XAI and attention mechanisms.

Main Methods:

  • Applied explainable AI (XAI) techniques, specifically Grad-CAM, to visualize model attention.
  • Integrated multi-head attention mechanisms with pretrained models like ResNet50.
  • Utilized a manually segmented dataset for objective evaluation of model behavior and performance.

Main Results:

  • Integrating multi-head attention significantly improved classification accuracy and interpretability.
  • ResNet50 with multi-head attention achieved 97.3% accuracy, surpassing baseline and SE-enhanced models.
  • Mean intersection over union (mIoU) for interpretability increased from 75.7% to 82.0%, indicating better focus on diagnostically relevant regions.

Conclusions:

  • Explainable AI (XAI) is crucial for validating deep learning heart-sound classification by ensuring focus on clinical features.
  • Multi-head attention mechanisms enhance both the accuracy and interpretability of heart sound classification models.
  • This study demonstrates the effectiveness of combining XAI and attention mechanisms on manually segmented data for reliable cardiac diagnostics.