{
  "id": 1038,
  "url": "https://arxiv.org/abs/2606.15038v1",
  "title": "Fusion is not one-size-fits-all: Cross-Modal Representation Alignment for Time-to-Event Modeling",
  "summary": "Accurate time-to-event (TTE) prediction from multimodal clinical data remains challenging due to modality imbalance and distribution shift. We introduce a foundation model-driven framework for cross-modal representation alignment between CT imaging and longitudinal EHR data, designed to generalize across tasks and institutions. CT and EHR modalities are encoded independently using domain-specific foundation models and aligned in a shared latent space through four principled fusion strategies: la",
  "authors": "Zhemin Zhang, Weijie Chen, David Le, Amara Tariq, Alex Wallace, Matthew Stib et al.",
  "category": "research",
  "topics": "safety-alignment,healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-13T00:44:59.000Z",
  "fetched_at": "2026-07-14T14:14:59.014Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/1038",
  "original_url": "https://arxiv.org/abs/2606.15038v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}