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AI Transparency Model

Chorus emphasizes transparency and explainability in AI-assisted monitoring.

AI-generated insights include:

  • Risk classification indicators
  • Confidence levels associated with model predictions
  • References to contributing physiological signals
  • Timestamped analysis results

These outputs are stored as traceable records linked to the underlying physiological observations.

Clinicians can review both the AI-generated interpretation and the original biosignal data before confirming clinical decisions.

This approach ensures that machine learning enhances clinical insight while maintaining transparency and accountability.