Artificial Intelligence Applications in Fangcang Shelter Hospitals: Opportunities and Challenges.

Q2 Medicine
Ming Li, Xiao-Hu Li, Kai-Yuan Min, Jun-Tao Yang
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引用次数: 0

Abstract

Fangcang shelter hospitals are modular, rapidly deployable facilities that play a vital role in pandemic response by providing centralized isolation and basic medical care for large patient populations. Artificial intelligence (AI) has the potential to transform Fangcang shelter hospitals into intelligent, responsive systems that are capable of significantly improving emergency preparedness, operational efficiency, and patient outcomes. Key application areas include site selection and design optimization, clinical decision support, AI-assisted clinical documentation and patient engagement, intelligent robotics, and operational management. However, realizing AI's full potential requires overcoming several challenges, including limited data accessibility, privacy and governance concerns, inadequate algorithmic adaptability in dynamic emergency settings, insufficient transparency and accountability in AI-driven decisions, fragmented system architectures due to proprietary formats, high costs disproportionate to the temporary nature of Fangcang shelter hospitals, and hardware reliability in austere environments. Addressing these challenges demands standardized data-sharing frameworks, development of explainable and robust AI algorithms, clear ethical and legal oversight, interoperable modular system designs, and active collaboration among multidisciplinary stakeholders.

人工智能在方舱方舱医院的应用:机遇与挑战。
房仓方舱医院是模块化的、可快速部署的设施,通过为大量患者提供集中隔离和基本医疗服务,在大流行应对中发挥着至关重要的作用。人工智能(AI)有可能将方舱方舱医院转变为智能、响应迅速的系统,能够显著提高应急准备、运营效率和患者治疗效果。关键应用领域包括选址和设计优化、临床决策支持、人工智能辅助临床文档和患者参与、智能机器人和运营管理。然而,实现人工智能的全部潜力需要克服几个挑战,包括有限的数据可访问性、隐私和治理问题、动态紧急情况下的算法适应性不足、人工智能驱动决策的透明度和问责性不足、专有格式导致的系统架构碎片化、与方舱方舱医院的临时性质不成比例的高成本,以及恶劣环境下的硬件可靠性。应对这些挑战需要标准化的数据共享框架,开发可解释和强大的人工智能算法,明确的道德和法律监督,可互操作的模块化系统设计,以及多学科利益相关者之间的积极合作。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Chinese Medical Sciences Journal
Chinese Medical Sciences Journal Medicine-Medicine (all)
CiteScore
2.40
自引率
0.00%
发文量
1275
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