{
  "id": 10566,
  "url": "https://arxiv.org/abs/2607.10522",
  "title": "Towards Autonomous and Auditable Medical Imaging Model Development",
  "summary": "Large language model (LLM) agents are beginning to automate machine learning engineering (MLE) by coupling planning, code execution, debugging, and empirical feedback. Translating this capability to medical imaging remains difficult because each task imposes modality-specific experimentation and strict requirements for validation protocols and prediction artifacts. Here we introduce AMID, an autonomous multi-agent framework for medical imaging model development. AMID first proposes Data-Conditio",
  "authors": "Shengyuan Liu, Jia-Xuan Jiang, Boyun Zheng, Cheng Wang, Zipei Wang, Wentao Pan",
  "category": "research",
  "topics": "healthcare,agents-autonomy,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-11T20:00:00.000Z",
  "fetched_at": "2026-07-16T05:10:56.605Z",
  "source_slug": "hf-daily",
  "source_name": "HuggingFace Daily Papers",
  "source_homepage": "https://huggingface.co/papers",
  "ethics_ai_record_url": "https://ethics.ai/record/10566",
  "original_url": "https://arxiv.org/abs/2607.10522",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}