{
  "id": 6018,
  "url": "https://arxiv.org/abs/2604.10386v1",
  "title": "TrajOnco: a multi-agent framework for temporal reasoning over longitudinal EHR for multi-cancer early detection",
  "summary": "Accurate estimation of cancer risk from longitudinal electronic health records (EHRs) could support earlier detection and improved care, but modeling such complex patient trajectories remains challenging. We present TrajOnco, a training-free, multi-agent large language model (LLM) framework designed for scalable multi-cancer early detection. Using a chain-of-agents architecture with long-term memory, TrajOnco performs temporal reasoning over sequential clinical events to generate patient-level s",
  "authors": "Sihang Zeng, Young Won Kim, Wilson Lau, Ehsan Alipour, Ruth Etzioni, Meliha Yetisgen et al.",
  "category": "research",
  "topics": "healthcare,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-12T00:16:38.000Z",
  "fetched_at": "2026-07-14T16:32:11.183Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/6018",
  "original_url": "https://arxiv.org/abs/2604.10386v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}