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Explainable AI

Chorus applies machine learning models to assist in the interpretation of biosignals while maintaining transparency and clinician oversight.

All AI outputs include:

  • Risk classification indicators
  • Confidence scoring
  • References to contributing signals
  • Timestamped analysis results

These outputs are stored as traceable FHIR resources linked to the originating observations.

Providers can review both the AI conclusion and the supporting signal data before confirming clinical interpretation.

This explainable approach helps build trust in AI-assisted monitoring while preserving clinician authority.