{
  "id": 12348,
  "url": "https://arxiv.org/abs/2607.18884v1",
  "title": "Public perceptions of AI-driven decision-making in healthcare: A structural equation modeling approach",
  "summary": "Artificial intelligence (AI) is increasingly integrated into healthcare to support diagnostics, decision-making, and administrative processes. However, the successful implementation of AI depends not only on technical performance but also on public perceptions of its helpfulness, riskiness, and fairness. This study examines public perceptions of automated decision-making (ADM) in healthcare. Data were drawn from the first wave of an ongoing longitudinal survey panel. The final sample consisted o",
  "authors": "Leonie Westerbeek, Ernesto de Leon, Julia C. M. van Weert",
  "category": "research",
  "topics": "bias-fairness,healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-21T09:14:00.000Z",
  "fetched_at": "2026-07-22T05:10:49.469Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/12348",
  "original_url": "https://arxiv.org/abs/2607.18884v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}