{
  "id": 161,
  "url": "https://arxiv.org/abs/2607.06163v1",
  "title": "X-FEMR: A Token-level Explainable Approach for Electronic Health Records Foundation Models using Transformer-based Models",
  "summary": "Foundation Models for Electronic Health Records (FEMRs) are pretrained on large-scale structured patient data, enabling them to convert longitudinal patient trajectories into generalizable representations for diverse clinical prediction tasks. Despite their effectiveness, FEMRs remain black-box models, raising concerns about bias, interpretability, and clinical trust. To address this, we propose the first token-level explainability approach for FEMRs. We train a Transformer-based surrogate model",
  "authors": "Jie Huang, Pengfei Yin, Zihan Xu, Daniel Capurro, Mike Conway, Ting Dang",
  "category": "research",
  "topics": "bias-fairness,safety-alignment,healthcare,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-07T11:37:43.000Z",
  "fetched_at": "2026-07-14T14:14:19.970Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/161",
  "original_url": "https://arxiv.org/abs/2607.06163v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}