{
  "id": 5014,
  "url": "https://arxiv.org/abs/2605.02707v1",
  "title": "SAIL: Structure-Aware Interpretable Learning for Anatomy-Aligned Post-hoc Explanations in OCT",
  "summary": "Optical coherence tomography (OCT), a commonly used retinal imaging modality, plays a central role in retinal disease diagnosis by providing high-resolution visualization of retinal layers. While deep learning (DL) has achieved expert-level accuracy in OCT-based retinal disease detection, its \"black box\" nature poses challenges for clinical adoption, where explainability is essential for clinical trust and regulatory approval. Existing post-hoc explainable AI (XAI) methods often struggle to deli",
  "authors": "Tienyu Chang, Tianhao Li, Ruogu Fang, Jiang Bian, Yu Huang",
  "category": "research",
  "topics": "regulation,healthcare,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-04T15:13:47.000Z",
  "fetched_at": "2026-07-14T16:31:26.336Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/5014",
  "original_url": "https://arxiv.org/abs/2605.02707v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}