Wei Qiuying, Pan Songcheng, Liu Xiaoyu, Hong Mei, Nong Chunying, Zhang Weiqi
Anesthesia Surgery Center, The First Affiliated Hospital of Guangxi Medical University, Naning, Guangxi, China.
Guangdong Lingnan Nightingale Nursing Academy, Guangzhou, Guangdong, China.
Front Med (Lausanne). 2025 Feb 11;12:1545420. doi: 10.3389/fmed.2025.1545420. eCollection 2025.
Artificial intelligence is increasingly influencing healthcare, providing transformative opportunities and challenges for nursing practice. This review critically evaluates the integration of AI in nursing, focusing on its current applications, limitations, and areas that require further investigation. A comprehensive analysis of recent studies highlights the use of AI in clinical decision support systems, patient monitoring, and nursing education. However, several barriers to successful implementation are identified, including technical constraints, ethical dilemmas, and the need for workforce adaptation. Significant gaps in the literature are also evident, such as the limited development of nursing-specific AI tools, insufficient long-term impact assessments, and the absence of comprehensive ethical frameworks tailored to nursing contexts. The potential of AI to reshape personalized care, advance robotics in nursing, and address global health challenges is explored in depth. This review integrates existing knowledge and identifies critical areas for future research, emphasizing the necessity of aligning AI advancements with the specific needs of nursing. Addressing these gaps is essential to fully harness AI's potential while reducing associated risks, ultimately enhancing nursing practice and improving patient outcomes.
人工智能对医疗保健的影响日益增大,给护理实践带来了变革性的机遇和挑战。本综述批判性地评估了人工智能在护理中的整合情况,重点关注其当前应用、局限性以及需要进一步研究的领域。对近期研究的全面分析凸显了人工智能在临床决策支持系统、患者监测和护理教育中的应用。然而,也发现了成功实施的若干障碍,包括技术限制、伦理困境以及劳动力适应需求。文献中还存在明显的重大差距,例如针对护理的人工智能工具开发有限、长期影响评估不足以及缺乏针对护理背景量身定制的全面伦理框架。深入探讨了人工智能重塑个性化护理、推动护理领域机器人技术发展以及应对全球健康挑战的潜力。本综述整合了现有知识并确定了未来研究的关键领域,强调使人工智能进步与护理的特定需求保持一致的必要性。弥补这些差距对于充分发挥人工智能的潜力同时降低相关风险至关重要,最终可提升护理实践并改善患者结局。