{
  "id": 14028,
  "url": "https://arxiv.org/abs/2607.24730v1",
  "title": "KANEx: Translating Kolmogorov-Arnold Networks' Interpretability to Medical Explainability",
  "summary": "Computer vision models have become highly effective for medical applications, yet their black-box nature continues to undermine clinician trust. In clinical workflows, chest X-ray classifiers are increasingly paired with Vision-Language Models (VLMs) to generate natural-language explanations. However, these systems add linguistic fluency without addressing the underlying opacity of the visual model. With the emergence of Kolmogorov-Arnold Networks (KANs), whose spline-based components provide in",
  "authors": "Krithi Shailya, Ananya Lakshmi Ravi, Venkatanathan K. V., Sowmya S. Sundaram, Gokul S. Krishnan, Aditi Anand, Balaraman Ravindran",
  "category": "research",
  "topics": "safety-alignment,healthcare,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-27T17:57:02.000Z",
  "fetched_at": "2026-07-28T05:10:12.325Z",
  "source_slug": "x-arxiv-cs-ai",
  "source_name": "arXiv cs.AI",
  "source_homepage": "https://arxiv.org/list/cs.AI/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/14028",
  "original_url": "https://arxiv.org/abs/2607.24730v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}