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Retrieval-augmented generation for generative artificial intelligence in health care
57
Zitationen
9
Autoren
2025
Jahr
Abstract
Abstract Generative artificial intelligence has brought disruptive innovations in health care but faces certain challenges. Retrieval-augmented generation (RAG) enables models to generate more reliable content by leveraging the retrieval of external knowledge. In this perspective, we analyze the possible contributions that RAG could bring to health care in equity, reliability, and personalization. Additionally, we discuss the current limitations and challenges of implementing RAG in medical scenarios.
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Autoren
Institutionen
- Duke-NUS Medical School(SG)
- University of Toronto(CA)
- Duke University(US)
- Intel (United States)(US)
- Harvard University(US)
- Mass General Brigham(US)
- Singapore General Hospital(SG)
- Singapore National Eye Center(SG)
- Singapore Eye Research Institute(SG)
- Stanford University(US)
- National University of Singapore(SG)