{
  "id": 9200,
  "url": "https://doi.org/10.1038/s41746-022-00699-2",
  "title": "Explainable medical imaging AI needs human-centered design: guidelines and evidence from a systematic review",
  "summary": "Transparency in Machine Learning (ML), often also referred to as interpretability or explainability, attempts to reveal the working mechanisms of complex models. From a human-centered design perspective, transparency is not a property of the ML model but an affordance, i.e., a relationship between algorithm and users. Thus, prototyping and user evaluations are critical to attaining solutions that afford transparency. Following human-centered design principles in highly specialized and high stake",
  "authors": "Haomin Chen, Catalina Gómez, Chien‐Ming Huang, Mathias Unberath",
  "category": "research",
  "topics": "safety-alignment,healthcare,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2022-10-19T00:00:00.000Z",
  "fetched_at": "2026-07-14T16:33:50.749Z",
  "source_slug": "openalex",
  "source_name": "OpenAlex",
  "source_homepage": "https://openalex.org",
  "ethics_ai_record_url": "https://ethics.ai/record/9200",
  "original_url": "https://doi.org/10.1038/s41746-022-00699-2",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}