{
  "id": 6321,
  "url": "https://arxiv.org/abs/2604.04563v2",
  "title": "Temporal Inversion for Learning Interval Change in Chest X-Rays",
  "summary": "Recent advances in vision--language pretraining have enabled strong medical foundation models, yet most analyze radiographs in isolation, overlooking the key clinical task of comparing prior and current images to assess interval change. For chest radiographs (CXRs), capturing interval change is essential, as radiologists must evaluate not only the static appearance of findings but also how they evolve over time. We introduce TILA (Temporal Inversion-aware Learning and Alignment), a simple yet ef",
  "authors": "Hanbin Ko, Kyungmin Jeon, Doowoong Choi, Chang Min Park",
  "category": "research",
  "topics": "safety-alignment,healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-06T09:52:26.000Z",
  "fetched_at": "2026-07-14T16:32:24.291Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/6321",
  "original_url": "https://arxiv.org/abs/2604.04563v2",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}