Learning, Potential, and Retention: An Approach for Evaluating Adaptive AI-Enabled Medical Devices

1. 1
   Define Learning Metrics

   Establish clear metrics (e.g., accuracy, F1-score, AUC) to quantify how your adaptive AI model improves its performance over time as new data is incorporated or new training cycles complete. Track these deltas after each update.
2. 2
   Assess Model Potential

   Develop methods to evaluate the model's future capabilities and adaptability. This might involve stress testing with simulated novel data distributions, analyzing architectural capacity for growth, or predicting performance on unseen data categories.
3. 3
   Measure Performance Retention

   Design a strategy to continuously monitor the model's ability to maintain performance on previously learned tasks. This typically involves maintaining a representative historical test set or using a replay buffer to prevent 'catastrophic forgetting' after updates.
4. 4
   Integrate into MLOps Pipeline

   Embed these learning, potential, and retention (LPR) evaluations into your continuous integration/continuous deployment (CI/CD) and MLOps workflows. Automate the collection and analysis of LPR metrics post-deployment and after every model update.
5. 5
   Establish Monitoring & Alerting

   Set up dashboards and alerting systems to visualize LPR metrics over time. Define thresholds for acceptable performance drops (retention) or insufficient improvement (learning/potential) to trigger re-evaluation or intervention.

import datetime

class AdaptiveAI_Monitor:
    def __init__(self, model_version):
        self.model_version = model_version
        self.metrics_log = []

    def log_evaluation(self, current_performance, learning_delta, potential_score, retention_score):
        log_entry = {
            "timestamp": datetime.datetime.now().isoformat(),
            "model_version": self.model_version,
            "current_performance": current_performance, # e.g., {'accuracy': 0.92, 'f1': 0.91}
            "learning_delta": learning_delta,         # e.g., {'accuracy_change': 0.01}
            "potential_score": potential_score,       # e.g., 0.85 (on novel data sim)
            "retention_score": retention_score        # e.g., 0.90 (on historical data)
        }
        self.metrics_log.append(log_entry)
        print(f"Logged evaluation for {self.model_version}: {log_entry}")

# Example Usage:
monitor = AdaptiveAI_Monitor(model_version="v1.2.3")
monitor.log_evaluation(
    current_performance={'accuracy': 0.92, 'f1': 0.91},
    learning_delta={'accuracy_change': 0.01},
    potential_score=0.85,
    retention_score=0.90
)

monitor_v2 = AdaptiveAI_Monitor(model_version="v1.2.4")
monitor_v2.log_evaluation(
    current_performance={'accuracy': 0.93, 'f1': 0.92},
    learning_delta={'accuracy_change': 0.015},
    potential_score=0.87,
    retention_score=0.89 # Slight drop in retention
)

• Paperarxiv.org