{
  "id": 8673,
  "url": "https://doi.org/10.1186/s12911-020-01332-6",
  "title": "Explainability for artificial intelligence in healthcare: a multidisciplinary perspective",
  "summary": "BACKGROUND: Explainability is one of the most heavily debated topics when it comes to the application of artificial intelligence (AI) in healthcare. Even though AI-driven systems have been shown to outperform humans in certain analytical tasks, the lack of explainability continues to spark criticism. Yet, explainability is not a purely technological issue, instead it invokes a host of medical, legal, ethical, and societal questions that require thorough exploration. This paper provides a compreh",
  "authors": "Julia Amann, Alessandro Blasimme, Effy Vayena, Dietmar Frey, Vince I. Madai",
  "category": "research",
  "topics": "healthcare,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2020-11-30T00:00:00.000Z",
  "fetched_at": "2026-07-14T16:33:43.485Z",
  "source_slug": "openalex",
  "source_name": "OpenAlex",
  "source_homepage": "https://openalex.org",
  "ethics_ai_record_url": "https://ethics.ai/record/8673",
  "original_url": "https://doi.org/10.1186/s12911-020-01332-6",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}