{
  "id": 396,
  "url": "https://arxiv.org/abs/2606.31616v1",
  "title": "Scientific Explanations in Health Sciences: Causality, Trust, and Epistemic Adequacy",
  "summary": "Medical Artificial Intelligence (AI) is widely expected to transform clinical practice, yet the decision-making processes of many Machine Learning (ML) models remain opaque. Explainability has been advanced as a partial remedy to clarify why AI generates predictions, particularly in high-stakes contexts. Despite ongoing efforts, debates on what constitutes an adequate medical explanation remain unsettled. Yet, explanation has long been a central topic of inquiry in the philosophy of science and ",
  "authors": "Martina Mattioli, Marcello Pelillo",
  "category": "research",
  "topics": "healthcare,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-30T13:05:00.000Z",
  "fetched_at": "2026-07-14T14:14:28.439Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/396",
  "original_url": "https://arxiv.org/abs/2606.31616v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}