{"title":"Artificial Intelligence Applications in Fangcang Shelter Hospitals: Opportunities and Challenges.","authors":"Ming Li, Xiao-Hu Li, Kai-Yuan Min, Jun-Tao Yang","doi":"10.24920/004510","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":35615,"journal":{"name":"Chinese Medical Sciences Journal","volume":" ","pages":"1-7"},"PeriodicalIF":0.0000,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Chinese Medical Sciences Journal","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.24920/004510","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Medicine","Score":null,"Total":0}
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.