{
  "id": 5289,
  "url": "https://arxiv.org/abs/2605.00893v1",
  "title": "Retrieval-Guided Generation for Safer Histopathology Image Captioning",
  "summary": "Generative vision-language models can produce fluent medical image captions but remain prone to hallucination, over-specific diagnostic claims, and factual inconsistency-serious issues in pathology. We investigate retrieval-guided generation (RGG) as a safer alternative, where captions are formed by summarizing expert text from visually similar cases rather than generated de novo. On the ARCH histopathology dataset, RGG improves semantic alignment with ground truth, achieving cosine similarity o",
  "authors": "Md. Enamul Hoq, Wataru Uegami, Saghir Alfasly, Ghazal Alabtah, Sahar Rahimi Malakshan, Armita Kazemi et al.",
  "category": "research",
  "topics": "safety-alignment,healthcare,finance-investment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-27T22:04:51.000Z",
  "fetched_at": "2026-07-14T16:31:40.219Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/5289",
  "original_url": "https://arxiv.org/abs/2605.00893v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}