{
  "id": 2221,
  "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1853584",
  "title": "Interpretable polyp classification via end-to-end Concept Bottleneck Models with vision-language concept alignment",
  "summary": "Background and objectiveArtificial intelligence has improved adenoma detection during colonoscopy, but most models remain black boxes, limiting clinical trust, especially for sessile serrated lesions (SSLs). Concept Bottleneck Models (CBMs) route predictions through human-understandable concepts. We propose an end-to-end CBM for colonoscopic polyp classification with concept-level explanations aligned with clinical standards.MethodsWe defined 62 concepts from NBI International Colorectal Endosco",
  "authors": "Qiunan Ji",
  "category": "research",
  "topics": "safety-alignment,healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-10T00:00:00.000Z",
  "fetched_at": "2026-07-14T16:11:46.979Z",
  "source_slug": "x-frontiers-in-artificial-intelligence",
  "source_name": "Frontiers in Artificial Intelligence",
  "source_homepage": "https://www.frontiersin.org/journals/artificial-intelligence",
  "ethics_ai_record_url": "https://ethics.ai/record/2221",
  "original_url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1853584",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}