{
  "id": 5602,
  "url": "https://arxiv.org/abs/2604.18302v1",
  "title": "Toward Zero-Egress Psychiatric AI: On-Device LLM Deployment for Privacy-Preserving Mental Health Decision Support",
  "summary": "Privacy represents one of the most critical yet underaddressed barriers to AI adoption in mental healthcare -- particularly in high-sensitivity operational environments such as military, correctional, and remote healthcare settings, where the risk of patient data exposure can deter help-seeking behavior entirely. Existing AI-enabled psychiatric decision support systems predominantly rely on cloud-based inference pipelines, requiring sensitive patient data to leave the device and traverse externa",
  "authors": "Eranga Bandara, Asanga Gunaratna, Ross Gore, Anita H. Clayton, Christopher K. Rhea, Sachini Rajapakse et al.",
  "category": "research",
  "topics": "privacy-surveillance,healthcare,military-security,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-20T14:09:01.000Z",
  "fetched_at": "2026-07-14T16:31:53.165Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/5602",
  "original_url": "https://arxiv.org/abs/2604.18302v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}