{
  "id": 15881,
  "url": "https://arxiv.org/abs/2608.02238v1",
  "title": "Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability",
  "summary": "Ensuring trust in AI systems is essential for the safe and ethical integration of machine learning systems into high-stakes domains such as digital health. Key dimensions, including robustness, explainability, fairness, accountability, and privacy, need to be addressed throughout the AI lifecycle, from problem formulation and data collection to model deployment and human interaction. While various contributions address different aspects of trustworthy AI, a focused synthesis on robustness and ex",
  "authors": "Abdullah Mamun, Shovito Barua Soumma, Hassan Ghasemzadeh",
  "category": "research",
  "topics": "bias-fairness,privacy-surveillance,healthcare,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-03T13:51:12.000Z",
  "fetched_at": "2026-08-04T05:10:21.797Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/15881",
  "original_url": "https://arxiv.org/abs/2608.02238v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}