人工智慧模型於護理研究的應用

近年人工智慧之快速發展提升了醫療服務之效率、疾病預測之精準度和健康照護產業之創新力。其中機器學習更是各領域發展重點，儘管在護理研究與臨床照護中使用較少，但隨著技術進步，有望在未來更廣泛之護理研究與臨床照護的場域中得到應用。本文針對機器學習於護理研究與照護之運用進行討論，首先介紹機器學習之種類與分類方式，在針對常見之神經元類機器學習模型進行分析，包含循環神經網絡、變壓器與自然語言處理。此外更針對機器學習之原則步驟與傳統統計方法之異同進行討論，反思機器學習模型品質監測策略與使用機器學習之可能限制與挑戰，最後呼籲藉由跨域交流以分享不同專業（資訊與護理）基礎知識，分析不同分析模組之優劣，時刻檢視研究過程與反思方法學之限制，才能最大限度地發揮人工智慧技術的潛力，推動護理研究的創新與進步。

In recent years, the rapid development of artificial intelligence has enhanced the efficiency of medical services, accuracy of disease prediction, and innovation in the healthcare industry. Among the many advances, machine learning has become a focal point of development in various fields. Although its use in nursing research and clinical care has been limited, technological progress promises broader applications of machine learning in these areas in the future. In this paper, the authors discuss the application of machine learning in nursing research and care. First, the types and classifications of machine learning are introduced. Next, common neural machine learning models, including recurrent neural networks, transformers, and natural language processing, are described and analyzed. Subsequently, the principles and steps of machine learning are explored and compared to traditional statistical methods, highlighting the quality-monitoring strategies used by machine learning models and the potential limitations and challenges of using machine learning. Finally, interdisciplinary collaboration is encouraged to share knowledge between information technology and nursing disciplines, analyze the advantages and disadvantages of various analytical models, continuously review the research process, and reflect on methodological limitations. Following this course, can help maximize the potential of artificial-intelligence-based technologies to drive innovation and progress in nursing research.