{
  "id": 12737,
  "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1869820",
  "title": "On the fragility of neural architecture search: the role of overfitting and task complexity in medical image analysis",
  "summary": "IntroductionNeural Architecture Search (NAS) effectively automates Deep Learning pipeline design but is prone to validation overfitting when applied to complex tasks, such as medical image analysis. To mitigate this and enhance generalization, researchers frequently integrate Deep Ensemble Learning (DEL) and data augmentation into the NAS workflow. However, the assumption that these methodologies do not negatively interfere in high-overfitting scenarios remains unproven.MethodsWe evaluated NAS,",
  "authors": "A. Gómez",
  "category": "research",
  "topics": "healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-22T00:00:00.000Z",
  "fetched_at": "2026-07-23T05:10:49.458Z",
  "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/12737",
  "original_url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1869820",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}