{
  "id": 13878,
  "url": "https://www.jmir.org/2026/1/e93950",
  "title": "Mapping Machine Learning–Driven Cybersecurity Solutions in Health Care: Scoping Literature Review",
  "summary": "Background: Health care systems face escalating cyberattacks, including the UK Synnovis ransomware attack, which halted pathology services for 14 weeks; the Ascension Health breach affecting 5.6 million patients; and the Change Healthcare breach costing US $2.5 billion. Conventional cybersecurity measures in health care remain reactive and inadequate against evolving threats. Machine learning (ML) offers adaptive, predictive, real-time cyber defense; yet, there is limited clarity on how ML tools",
  "authors": "Kunal Rajput, Sharukh Zuberi, Mireille Elhajj, Washington Ochieng, Ara Darzi, Saira Ghafur",
  "category": "research",
  "topics": "healthcare,military-security",
  "orgs": null,
  "regions": "uk",
  "published_at": "2026-07-27T20:30:13.000Z",
  "fetched_at": "2026-07-28T05:10:12.325Z",
  "source_slug": "x-jmir-journal-of-medical-internet-researc",
  "source_name": "JMIR (Journal of Medical Internet Research)",
  "source_homepage": "https://www.jmir.org",
  "ethics_ai_record_url": "https://ethics.ai/record/13878",
  "original_url": "https://www.jmir.org/2026/1/e93950",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}