Detecting Drift in Healthcare AI Models Based on Data Availability

ABSTRACT

There is an increasing interest in the use of AI in healthcare due to its potential for diagnosis or disease prediction. However, healthcare data is not static and is likely to change over time leading a non-adaptive model to poor decision-making. The need of a drift detector in the overall learning framework is therefore essential to guarantee reliable products on the market. Most drift detection algorithms consider that ground truth labels are available immediately after prediction since these methods often work by monitoring the model performance. However, especially in real-world clinical contexts, this is not always the case as collecting labels is often more time consuming as requiring experts' input. This paper investigates methodologies to address drift detection depending on which information is available during the monitoring process. We explore the topic within a regulatory standpoint, showing challenges and approaches to monitoring algorithms in healthcare with subsequent batch updates of data. This paper explores three different aspects of drift detection drift based on performance (when labels are available), drift based on model structure (indicating causes of drift) and drift based on change in underlying data characteristics (distribution and correlation) when labels are not available. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.