{
  "id": 5016,
  "url": "https://arxiv.org/abs/2605.02669v2",
  "title": "An explainable hypothesis-driven approach to Drug-Induced Liver Injury with HADES",
  "summary": "Drug-induced liver injury (DILI) remains a leading cause of late-stage clinical trial attrition. However, existing computational predictors primarily rely on binary classification, a framing that limits generalization and yields no mechanistic insight to guide translational decisions. We argue that DILI prediction is better posed as an explainable hypothesis-generation problem. To support this shift, we introduce the DILER Benchmark, a dataset that extends beyond binary labels by augmenting a cu",
  "authors": "Maciej Wisniewski, Bartosz Topolski, Pawel Dabrowski-Tumanski, Dariusz Plewczynski, Tomasz Jetka",
  "category": "research",
  "topics": "healthcare,transparency,biotech",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-04T14:50:28.000Z",
  "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/5016",
  "original_url": "https://arxiv.org/abs/2605.02669v2",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}