Title: Keeping Medical AI Healthy: A Review of Detection and Correction Methods for System Degradation Show Chinese title

Title: 保持医疗人工智能健康：系统退化检测与纠正方法综述

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. Show Chinese abstract

Abstract: 人工智能（AI）正日益融入现代医疗保健，为临床决策提供强大的支持。 然而，在现实环境中，由于数据分布的变化、患者特征的改变、临床协议的演变以及数据质量的波动等因素，AI系统可能会随时间推移出现性能下降。 这些因素可能影响模型的可靠性，带来安全问题，并增加预测不准确或不良结果的可能性。 本综述从前瞻性角度探讨了在医疗领域监控和维护AI系统“健康”的方法。 我们强调了持续性能监控、早期性能退化检测和有效自我校正机制的迫切需求。 本文首先回顾了数据层面和模型层面常见的性能退化原因。 随后总结了检测数据和模型漂移的关键技术，并深入分析了根本原因。 进一步回顾了纠正策略，包括模型重新训练到测试时适应等方法。 我们的调查涵盖了传统机器学习模型和最先进的大型语言模型（LLMs），并对其优势和局限性提供了见解。 最后，我们讨论了当前的技术挑战，并提出了未来的研究方向。 本研究旨在指导开发出能够在动态临床环境中实现安全、长期部署的可靠、稳健的医疗AI系统。