{
  "id": 6744,
  "url": "https://arxiv.org/abs/2603.24772v1",
  "title": "Evaluating Fine-Tuned LLM Model For Medical Transcription With Small Low-Resource Languages Validated Dataset",
  "summary": "Clinical documentation is a critical factor for patient safety, diagnosis, and continuity of care. The administrative burden of EHRs is a significant factor in physician burnout. This is a critical issue for low-resource languages, including Finnish. This study aims to investigate the effectiveness of a domain-aligned natural language processing (NLP); large language model for medical transcription in Finnish by fine-tuning LLaMA 3.1-8B on a small validated corpus of simulated clinical conversat",
  "authors": "Mohammed Nowshad Ruhani Chowdhury, Mohammed Nowaz Rabbani Chowdhury, Sakari Lukkarinen",
  "category": "research",
  "topics": "healthcare,finance-investment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-03-25T19:44:49.000Z",
  "fetched_at": "2026-07-14T16:32:41.669Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/6744",
  "original_url": "https://arxiv.org/abs/2603.24772v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}