{
  "id": 7380,
  "url": "https://arxiv.org/abs/2603.10677v1",
  "title": "Emulating Clinician Cognition via Self-Evolving Deep Clinical Research",
  "summary": "Clinical diagnosis is a complex cognitive process, grounded in dynamic cue acquisition and continuous expertise accumulation. Yet most current artificial intelligence (AI) systems are misaligned with this reality, treating diagnosis as single-pass retrospective prediction while lacking auditable mechanisms for governed improvement. We developed DxEvolve, a self-evolving diagnostic agent that bridges these gaps through an interactive deep clinical research workflow. The framework autonomously req",
  "authors": "Ruiyang Ren, Yuhao Wang, Yunsen Liang, Lan Luo, Jing Liu, Haifeng Wang et al.",
  "category": "research",
  "topics": "safety-alignment,healthcare,agents-autonomy,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-03-11T11:41:51.000Z",
  "fetched_at": "2026-07-14T16:33:12.389Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/7380",
  "original_url": "https://arxiv.org/abs/2603.10677v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}