转化式学习
软件部署
医学
医疗保健
任务(项目管理)
工程伦理学
心理学
计算机科学
政治学
工程类
教育学
系统工程
法学
操作系统
作者
Brigitte Fong Yeong Woo,Tom Huynh,Arthur Tang,Nhat Bui,Giang T. Nguyen,Wilson Tam
出处
期刊:European Journal of Cardiovascular Nursing
[Oxford University Press]
日期:2024-01-05
卷期号:23 (5): 549-552
被引量:8
标识
DOI:10.1093/eurjcn/zvad120
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI