{
  "id": 3798,
  "url": "https://arxiv.org/abs/2605.24792v1",
  "title": "Parameter-Efficient VLMs for Gastrointestinal Endoscopy: Medical Image Generation and Clinical Visual Question Answering",
  "summary": "The major limitations of gastrointestinal (GI) endoscopy AI systems arise from a shortage of annotated data, strict privacy policies, and significant bottlenecks in conventional model fine-tuning. Such limitations impede the successful application of sophisticated AI models in clinical practice, particularly affecting the reliability and scalability of diagnosis. In this paper, we present a dual-pipeline PEFT model that addresses two fundamental problems: medical Visual Question Answering (VQA) ",
  "authors": "Ojonugwa Oluwafemi Ejiga Peter, Frederick Akor Ejiga, Fahmi Khalifa, Md Mahmudur Rahman",
  "category": "research",
  "topics": "privacy-surveillance,healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-24T00:33:54.000Z",
  "fetched_at": "2026-07-14T16:30:31.921Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/3798",
  "original_url": "https://arxiv.org/abs/2605.24792v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}