{
  "id": 5333,
  "url": "https://arxiv.org/abs/2604.23954v1",
  "title": "An empirical evaluation of the risks of AI model updates using clinical data: stability, arbitrariness, and fairness",
  "summary": "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",
  "authors": "Ioannis Bilionis, Ricardo C. Berrios, Luis Fernandez-Luque, Carlos Castillo",
  "category": "research",
  "topics": "bias-fairness,healthcare,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-27T01:59:04.000Z",
  "fetched_at": "2026-07-14T16:31:40.221Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/5333",
  "original_url": "https://arxiv.org/abs/2604.23954v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}