{
  "id": 11441,
  "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1803913",
  "title": "Prediction of female reproductive tract infections risk among college-going young adult women in Delhi using explainable artificial intelligence",
  "summary": "IntroductionReproductive tract infections (RTIs) and sexually transmitted infections (STIs) pose a substantial economic burden and public health concern in developing countries such as India, where inadequate early detection and prevention strategies often lead to increased morbidity, mortality, stigma, cancer and adverse reproductive health outcomes in both men and women.MethodsThe present cross-sectional study employed machine-learning models to predict the risk of RTI/STI among young women in",
  "authors": "Joyeta Ghosh",
  "category": "research",
  "topics": "healthcare,transparency",
  "orgs": null,
  "regions": "india",
  "published_at": "2026-07-17T00:00:00.000Z",
  "fetched_at": "2026-07-18T05:10:55.931Z",
  "source_slug": "x-frontiers-in-artificial-intelligence",
  "source_name": "Frontiers in Artificial Intelligence",
  "source_homepage": "https://www.frontiersin.org/journals/artificial-intelligence",
  "ethics_ai_record_url": "https://ethics.ai/record/11441",
  "original_url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1803913",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}