{
  "id": 16189854,
  "title": "Who decides when medical AI may be updated?",
  "url": "https://urgent.news/2026/10/23/who-decides-when-medical-ai-may-be-updated",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-23T13:40:11.000Z",
  "source": {
    "name": "Medical Xpress",
    "slug": "medical-xpress",
    "url": "https://medicalxpress.com/news/2026-10-medical-ai.html"
  },
  "original_language": "en",
  "account": "When medical AI models are updated, a framework called AEGIS determines whether the new model can be deployed and if existing models need closer examination. AEGIS follows preapproved change-plan rules determined before the device reached the market. The framework was developed by researchers at Karolinska Institutet and is detailed in a study published in the journal npj Digital Medicine. While AI can improve with new patient data, every update must first be checked to ensure it hasn't become worse due to outdated data. The two models tested with AEGIS were one for identifying sepsis cases from past intensive care data and another for outlining brain tumors on MRI scans. The demonstrations focused on decision rules rather than clinical safety or effectiveness, as each clinical application requires its own validation and regulatory assessment. AEGIS currently detects changes in incoming data but does not yet determine if the relationship between data and outcomes has changed. The next step is to test the framework in real-world use with clinical partners and authorities.",
  "summary": "Medical AI can improve with new patient data, but every update must be checked first. Patients, equipment and ways of working change, and a model trained on older data can gradually get worse without anyone noticing.",
  "key_points": [
    "AEGIS framework decides medical AI updates",
    "Developed by researchers at Karolinska Institutet",
    "Tests focus on decision rules, not clinical safety"
  ],
  "editors_take": "Standardizing update protocols through preapproved change-plan rules provides a structured method for maintaining medical AI integrity as models evolve with new patient data.",
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}