New Epochs in AI Supervision: Design and Implementation of an Autonomous Radiology AI Monitoring System

TL;DR

This paper presents a real-time monitoring system for radiology AI models that uses novel metrics to detect performance changes, ensuring safety and reliability in clinical practice.

Contribution

It introduces two innovative metrics, predictive divergence and temporal stability, for continuous AI performance monitoring in healthcare settings.

Findings

Effective detection of model performance changes in chest X-ray data

Demonstrated system's ability to identify data drift and model decay

Supports safer integration of AI in clinical workflows

Abstract

With the increasingly widespread adoption of AI in healthcare, maintaining the accuracy and reliability of AI models in clinical practice has become crucial. In this context, we introduce novel methods for monitoring the performance of radiology AI classification models in practice, addressing the challenges of obtaining real-time ground truth for performance monitoring. We propose two metrics - predictive divergence and temporal stability - to be used for preemptive alerts of AI performance changes. Predictive divergence, measured using Kullback-Leibler and Jensen-Shannon divergences, evaluates model accuracy by comparing predictions with those of two supplementary models. Temporal stability is assessed through a comparison of current predictions against historical moving averages, identifying potential model decay or data drift. This approach was retrospectively validated using chest X-ray data from a single-center imaging clinic, demonstrating its effectiveness in maintaining AI model reliability. By providing continuous, real-time insights into model performance, our system ensures the safe and effective use of AI in clinical decision-making, paving the way for more robust AI integration in healthcare