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Transforming nursing with large language models: from concept to practice.
Woo, Brigitte; Huynh, Tom; Tang, Arthur; Bui, Nhat; Nguyen, Giang; Tam, Wilson.
Afiliação
  • Woo B; Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
  • Huynh T; School of Science, Engineering and Technology, RMIT University, 702 Nguyen Van Linh Blvd., District 7, Ho Chin Minh 756000, Ho Chin Minh City, Vietnam.
  • Tang A; School of Science, Engineering and Technology, RMIT University, 702 Nguyen Van Linh Blvd., District 7, Ho Chin Minh 756000, Ho Chin Minh City, Vietnam.
  • Bui N; School of Science, Engineering and Technology, RMIT University, 702 Nguyen Van Linh Blvd., District 7, Ho Chin Minh 756000, Ho Chin Minh City, Vietnam.
  • Nguyen G; School of Science, Engineering and Technology, RMIT University, 702 Nguyen Van Linh Blvd., District 7, Ho Chin Minh 756000, Ho Chin Minh City, Vietnam.
  • Tam W; Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
Eur J Cardiovasc Nurs ; 23(5): 549-552, 2024 Jul 19.
Article em En | MEDLINE | ID: mdl-38178303
ABSTRACT
Large language models (LLMs) such as ChatGPT have emerged as potential game-changers in nursing, aiding in patient education, diagnostic assistance, treatment recommendations, and administrative task efficiency. While these advancements signal promising strides in healthcare, integrated LLMs are not without challenges, particularly artificial intelligence hallucination and data privacy concerns. Methodologies such as prompt engineering, temperature adjustments, model fine-tuning, and local deployment are proposed to refine the accuracy of LLMs and ensure data security. While LLMs offer transformative potential, it is imperative to acknowledge that they cannot substitute the intricate expertise of human professionals in the clinical field, advocating for a synergistic approach in patient care.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Inteligência Artificial Tipo de estudo: Guideline Limite: Humans Idioma: En Revista: Eur J Cardiovasc Nurs Assunto da revista: ANGIOLOGIA / CARDIOLOGIA / ENFERMAGEM Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Singapura

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Inteligência Artificial Tipo de estudo: Guideline Limite: Humans Idioma: En Revista: Eur J Cardiovasc Nurs Assunto da revista: ANGIOLOGIA / CARDIOLOGIA / ENFERMAGEM Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Singapura