{
  "id": 11072,
  "url": "https://www.frontiersin.org/articles/10.3389/frobt.2026.1835473",
  "title": "Structural predictors and latent maturity regimes of robotic readiness in global health systems: evidence from machine learning-based latent clustering and class prediction",
  "summary": "BackgroundThe systematic integration of robotics into health service delivery systems requires periodic assessment of robotic readiness in terms of digital-health maturity regimes across countries. The current study aims to cluster 169 countries into maturity regimes and classify and predict cluster membership accuracy based on digital-health maturity dimensions determining the system’s perception and interoperability, coordination, and workforce–regulatory reliability readiness. These country-l",
  "authors": "Moumita Mukherjee",
  "category": "research",
  "topics": "regulation,jobs-economy,healthcare,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-16T00:00:00.000Z",
  "fetched_at": "2026-07-17T05:10:53.887Z",
  "source_slug": "x-frontiers-in-robotics-and-ai",
  "source_name": "Frontiers in Robotics and AI",
  "source_homepage": "https://www.frontiersin.org/journals/robotics-and-ai",
  "ethics_ai_record_url": "https://ethics.ai/record/11072",
  "original_url": "https://www.frontiersin.org/articles/10.3389/frobt.2026.1835473",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}