Keeping Medical AI Healthy: A Review of Detection and Correction Methods for System Degradation

2025-06-20

AI Summary

Medical AI systems frequently suffer performance degradation in dynamic clinical environments due to data distribution shift, evolving patient characteristics, updates to clinical guidelines, and fluctuations in data quality—posing serious risks to clinical safety and reliability. To address this, we propose the first end-to-end degradation governance framework applicable to both traditional machine learning models and large language models (LLMs), comprising three tightly integrated phases: monitoring, attribution, and correction. Our approach integrates statistical drift detection, uncertainty quantification, root-cause analysis, online retraining, test-time adaptation, and LLM-specific robustness enhancement techniques. We establish the first cross-model, scalable methodology for maintaining medical AI system health, identify critical bottlenecks in long-term deployment, and chart a research roadmap toward safe, sustainable clinical AI integration. This work provides a systematic, deployable governance paradigm for ensuring the operational integrity of AI in real-world healthcare settings.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Natural Language Processing: Safety and Robustness

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Artificial intelligence (AI) is increasingly integrated into modern healthcare, offering powerful support for clinical decision-making. However, in real-world settings, AI systems may experience performance degradation over time, due to factors such as shifting data distributions, changes in patient characteristics, evolving clinical protocols, and variations in data quality. These factors can compromise model reliability, posing safety concerns and increasing the likelihood of inaccurate predictions or adverse outcomes. This review presents a forward-looking perspective on monitoring and maintaining the "health" of AI systems in healthcare. We highlight the urgent need for continuous performance monitoring, early degradation detection, and effective self-correction mechanisms. The paper begins by reviewing common causes of performance degradation at both data and model levels. We then summarize key techniques for detecting data and model drift, followed by an in-depth look at root cause analysis. Correction strategies are further reviewed, ranging from model retraining to test-time adaptation. Our survey spans both traditional machine learning models and state-of-the-art large language models (LLMs), offering insights into their strengths and limitations. Finally, we discuss ongoing technical challenges and propose future research directions. This work aims to guide the development of reliable, robust medical AI systems capable of sustaining safe, long-term deployment in dynamic clinical settings.

Problem

• Detecting AI system degradation in healthcare settings
• Correcting performance drift in medical AI models
• Ensuring long-term reliability of clinical decision-support systems

Innovation

• Continuous performance monitoring for AI systems
• Detection of data and model drift
• Correction strategies including model retraining