Modern healthcare systems rely increasingly on AI-based diagnostic tools to support physicians in identifying diseases. However, as these models become more complex, it's crucial to monitor their accuracy, stability, and fairness in real time — especially when applied to critical care, radiology, or chronic condition detection.

The Challenge:

• Diagnostic models degrade over time if data shifts (e.g., new variants, imaging equipment upgrades)

• Traditional monitoring methods react after failures, not before

• There's often no real-time alert system for anomalies or drift

• Misclassifications may not be detected until it’s too late

Quantum-Inspired Solution

Continuous AI Behavior Monitoring

HessQ uses QUBO to track subtle changes in diagnostic outputs and flag when the model is drifting or misclassifying.

Quantum-inspired kernels detect shifts across patient subgroups, ensuring fairness and performance across demographics.

Triggers automatic warnings when diagnostics diverge from expected patterns — before clinical impact occurs.

Continuous AI Behavior Monitoring

Real-Time Risk Alerts

The Results:

• Early detection of drift (weeks before clinical impact)

• Improved diagnostic reliability by 20–30%

• More equitable outcomes across patient groups

• Increased confidence in AI support tools among physicians