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Evidence record 5333 · automatically gathered

An empirical evaluation of the risks of AI model updates using clinical data: stability, arbitrariness, and fairness

Artificial Intelligence and Machine Learning (AI/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 Diab

Record details

Published: 27 April 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Healthcare · Environment
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (27 April 2026), “An empirical evaluation of the risks of AI model updates using clinical data: stability, arbitrariness, and fairness,” evidence record 5333, https://ethics.ai/record/5333 (originally published by arXiv).

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