An empirical evaluation of the risks of AI model updates using clinical data: stability, arbitrariness, and fairness
Artificial Intelligence (AI) and Machine Learning (ML) models used in clinical settings are increasingly deployed to support clinical decision-making. However, when training data become stale due to changes in demographics, environment, or patient behaviors, model performance can degrade substantially. While updating models with new training data is necessary, such updates may also introduce new risks. We evaluated the proposed monitoring framework on four publicly available U.S.-based Type 1 Diabetes datasets containing high-resolution continuous glucose monitoring (CGM) data, comprising approximately 11,300 weekly observations from 496 participants younger than 20 years. All datasets in...
| Authors | Ioannis Bilionis, Ricardo C. Berrios, Luis Fernandez-Luque et al. |
|---|---|
| Primary category | cs.AI |
| Published | 27 April 2026 |
| Last updated at source | 4 August 2026 |
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