📋 Continuous Learning AI Model Drift Monitoring Simulator

Locking a Validated Performance Baseline

Placeholder: baseline accuracy is fixed at clearance, before any drift.

• 94.5%: Baseline Accuracy (validated at clearance)
• 92.1%: Baseline Sensitivity (locked reference value)
• 96.0%: Baseline Specificity (locked reference value)
• ±2.5 pp: Acceptable Margin (predefined drift tolerance)

Why a locked baseline matters

Placeholder line — baseline is the fixed yardstick every future measurement compares against.

Placeholder line — set once, during formal validation, not re-tuned after deployment.

Real-World Inputs Drift From Training Data

Placeholder: population, equipment, and workflow shifts change model inputs.

• Demographic mix: Population Shift (ages, comorbidities change)
• New scanner/sensor: Equipment Shift (different input signal)
• Changed clinical practice: Workflow Shift (new ordering patterns)
• 1–10 scale: Shift Rate Slider (slow-stable to fast-significant)

Sources of real-world drift

Placeholder line — covariate shift, label shift, and concept shift all degrade silently.

Placeholder line — none of these trigger an error; the model just gets quietly worse.

Tracking Live Accuracy Against Baseline

Placeholder: continuous surveillance compares live accuracy to baseline over time.

• Continuous: Monitoring Cadence (rolling performance window)
• Accuracy / Sens / Spec: Metric Tracked (vs locked baseline)
• Live-updating: Current Live Accuracy (see metrics panel)
• Statistical control chart: Detection Method (flags sustained deviation)

What ongoing monitoring looks for

Placeholder line — sustained deviation, not single noisy data points, triggers concern.

Placeholder line — dashboards plot live accuracy against the baseline reference line.

Performance Exits the Acceptable Margin

Placeholder: live accuracy falls below the predefined drift tolerance band.

• Live-updating: Drift Magnitude (baseline minus live, pp)
• ±2.5 pp: Warning Threshold (first alert boundary)
• ±6.0 pp: Critical Threshold (second alert boundary)
• Live-updating: Alert Status (normal / warning / critical)

Why thresholds are set in advance

Placeholder line — margins are predefined so alerts fire on evidence, not judgment calls.

Placeholder line — crossing the red zone is the trigger for mandatory action.

Placeholder highlight — silent decay past this line risks undetected patient harm.

Intervention Prevents Silent Decay

Placeholder: detection triggers retraining, recalibration, or takedown/resubmission.

• Model update: Retrain (on newer real-world data)
• Threshold adjustment: Recalibrate (without full retrain)
• Withdraw / resubmit: Takedown (if degradation is severe)
• Harm prevented: Outcome (via caught, addressed drift)

Closing the monitoring loop

Placeholder line — the chosen intervention restores performance toward baseline.

Placeholder line — monitoring then resumes against the same or an updated baseline.

Placeholder highlight — the point of monitoring is catching drift before harm, not after.