{
  "id": 9132,
  "url": "https://doi.org/10.1038/s41746-022-00611-y",
  "title": "Clinical artificial intelligence quality improvement: towards continual monitoring and updating of AI algorithms in healthcare",
  "summary": "Machine learning (ML) and artificial intelligence (AI) algorithms have the potential to derive insights from clinical data and improve patient outcomes. However, these highly complex systems are sensitive to changes in the environment and liable to performance decay. Even after their successful integration into clinical practice, ML/AI algorithms should be continuously monitored and updated to ensure their long-term safety and effectiveness. To bring AI into maturity in clinical care, we advocat",
  "authors": "Jean Feng, Rachael V. Phillips, Ivana Malenica, Andrew Bishara, Alan Hubbard, Leo Anthony Celi",
  "category": "research",
  "topics": "healthcare,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2022-05-31T00:00:00.000Z",
  "fetched_at": "2026-07-14T16:33:50.746Z",
  "source_slug": "openalex",
  "source_name": "OpenAlex",
  "source_homepage": "https://openalex.org",
  "ethics_ai_record_url": "https://ethics.ai/record/9132",
  "original_url": "https://doi.org/10.1038/s41746-022-00611-y",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}