• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

智能互联医疗中的趋势、技术及关键挑战

Trends, Technologies, and Key Challenges in Smart and Connected Healthcare.

作者信息

Navaz Alramzana Nujum, Serhani Mohamed Adel, El Kassabi Hadeel T, Al-Qirim Nabeel, Ismail Heba

机构信息

Department of Information Systems and SecurityCollege of Information TechnologyUnited Arab Emirates University Al Ain United Arab Emirates.

Department of Computer Science and Software EngineeringCollege of Information TechnologyUAE University Al Ain United Arab Emirates.

出版信息

IEEE Access. 2021 May 11;9:74044-74067. doi: 10.1109/ACCESS.2021.3079217. eCollection 2021.

DOI:10.1109/ACCESS.2021.3079217
PMID:34812394
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8545204/
Abstract

Cardio Vascular Diseases (CVD) is the leading cause of death globally and is increasing at an alarming rate, according to the American Heart Association's Heart Attack and Stroke Statistics-2021. This increase has been further exacerbated because of the current coronavirus (COVID-19) pandemic, thereby increasing the pressure on existing healthcare resources. Smart and Connected Health (SCH) is a viable solution for the prevalent healthcare challenges. It can reshape the course of healthcare to be more strategic, preventive, and custom-designed, making it more effective with value-added services. This research endeavors to classify state-of-the-art SCH technologies via a thorough literature review and analysis to comprehensively define SCH features and identify the enabling technology-related challenges in SCH adoption. We also propose an architectural model that captures the technological aspect of the SCH solution, its environment, and its primary involved stakeholders. It serves as a reference model for SCH acceptance and implementation. We reflected the COVID-19 case study illustrating how some countries have tackled the pandemic differently in terms of leveraging the power of different SCH technologies, such as big data, cloud computing, Internet of Things, artificial intelligence, robotics, blockchain, and mobile applications. In combating the pandemic, SCH has been used efficiently at different stages such as disease diagnosis, virus detection, individual monitoring, tracking, controlling, and resource allocation. Furthermore, this review highlights the challenges to SCH acceptance, as well as the potential research directions for better patient-centric healthcare.

摘要

根据美国心脏协会的《2021年心脏病发作和中风统计报告》,心血管疾病(CVD)是全球主要的死亡原因,且正以惊人的速度增长。由于当前的冠状病毒(COVID-19)大流行,这一增长进一步加剧,从而增加了现有医疗资源的压力。智能互联健康(SCH)是应对普遍存在的医疗挑战的一个可行解决方案。它可以重塑医疗保健的进程,使其更具战略性、预防性和定制性,通过增值服务使其更有效。本研究旨在通过全面的文献综述和分析对最先进的SCH技术进行分类,以全面定义SCH的特征,并确定在采用SCH时与使能技术相关的挑战。我们还提出了一个架构模型,该模型涵盖了SCH解决方案的技术方面、其环境及其主要涉及的利益相关者。它可作为SCH接受和实施的参考模型。我们引入了COVID-19案例研究,说明了一些国家在利用不同的SCH技术(如大数据、云计算、物联网、人工智能、机器人技术、区块链和移动应用程序)的力量方面如何以不同方式应对这一流行病。在抗击这一流行病的过程中,SCH已在疾病诊断、病毒检测、个体监测、追踪、控制和资源分配等不同阶段得到有效应用。此外,本综述强调了SCH接受方面的挑战,以及以患者为中心的更好医疗保健的潜在研究方向。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2d4d/8545204/ad1626eb1de9/serha7-3079217.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2d4d/8545204/116e88bd5d59/serha1-3079217.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2d4d/8545204/63bea650382a/serha2-3079217.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2d4d/8545204/e4b6198ff62a/serha3-3079217.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2d4d/8545204/cd724eefd645/serha4-3079217.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2d4d/8545204/19bbb7c0c0f4/serha5-3079217.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2d4d/8545204/a1883b0850fb/serha6-3079217.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2d4d/8545204/ad1626eb1de9/serha7-3079217.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2d4d/8545204/116e88bd5d59/serha1-3079217.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2d4d/8545204/63bea650382a/serha2-3079217.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2d4d/8545204/e4b6198ff62a/serha3-3079217.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2d4d/8545204/cd724eefd645/serha4-3079217.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2d4d/8545204/19bbb7c0c0f4/serha5-3079217.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2d4d/8545204/a1883b0850fb/serha6-3079217.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2d4d/8545204/ad1626eb1de9/serha7-3079217.jpg

相似文献

1
Trends, Technologies, and Key Challenges in Smart and Connected Healthcare.智能互联医疗中的趋势、技术及关键挑战
IEEE Access. 2021 May 11;9:74044-74067. doi: 10.1109/ACCESS.2021.3079217. eCollection 2021.
2
Harnessing the Power of Smart and Connected Health to Tackle COVID-19: IoT, AI, Robotics, and Blockchain for a Better World.利用智能互联健康的力量应对新冠疫情:物联网、人工智能、机器人技术和区块链打造更美好的世界。
IEEE Internet Things J. 2021 Apr 19;8(16):12826-12846. doi: 10.1109/JIOT.2021.3073904. eCollection 2021 Aug 15.
3
Future Smart Connected Communities to Fight COVID-19 Outbreak.未来智能互联社区抗击新冠疫情
Internet Things (Amst). 2021 Mar;13:100342. doi: 10.1016/j.iot.2020.100342. Epub 2020 Dec 7.
4
A Hybrid Stacked CNN and Residual Feedback GMDH-LSTM Deep Learning Model for Stroke Prediction Applied on Mobile AI Smart Hospital Platform.基于移动 AI 智能医院平台的应用,采用混合堆叠 CNN 和残差反馈 GMDH-LSTM 深度学习模型进行中风预测。
Sensors (Basel). 2023 Mar 27;23(7):3500. doi: 10.3390/s23073500.
5
A Survey on harnessing the Applications of Mobile Computing in Healthcare during the COVID-19 Pandemic: Challenges and Solutions.新冠疫情期间利用移动计算在医疗保健领域应用的调查:挑战与解决方案
Comput Netw. 2023 Apr;224:109605. doi: 10.1016/j.comnet.2023.109605. Epub 2023 Feb 3.
6
A Blockchain and Artificial Intelligence-Based, Patient-Centric Healthcare System for Combating the COVID-19 Pandemic: Opportunities and Applications.一种基于区块链和人工智能的、以患者为中心的抗击新冠疫情医疗系统:机遇与应用
Healthcare (Basel). 2021 Aug 8;9(8):1019. doi: 10.3390/healthcare9081019.
7
Internet of Things (IoT) Adoption Model for Early Identification and Monitoring of COVID-19 Cases: A Systematic Review.用于早期识别和监测新冠肺炎病例的物联网采用模型:一项系统综述
Int J Prev Med. 2022 Aug 8;13:112. doi: 10.4103/ijpvm.IJPVM_667_20. eCollection 2022.
8
A Comprehensive Review on Smart Health Care: Applications, Paradigms, and Challenges with Case Studies.智能医疗保健综述:应用、范例及案例研究的挑战
Contrast Media Mol Imaging. 2022 Sep 29;2022:4822235. doi: 10.1155/2022/4822235. eCollection 2022.
9
Artificial Intelligence Applications and Self-Learning 6G Networks for Smart Cities Digital Ecosystems: Taxonomy, Challenges, and Future Directions.人工智能应用和自学习 6G 网络在智慧城市数字生态系统中的应用:分类、挑战和未来方向。
Sensors (Basel). 2022 Aug 1;22(15):5750. doi: 10.3390/s22155750.
10
Application of Internet of Things and Sensors in Healthcare.物联网和传感器在医疗保健中的应用。
Sensors (Basel). 2022 Jul 31;22(15):5738. doi: 10.3390/s22155738.

引用本文的文献

1
Nationwide trends of online consultations in China: a 10-year sequential cross-sectional study of 65 305 clinicians.中国在线问诊的全国性趋势:一项对65305名临床医生的10年连续性横断面研究。
BMJ Public Health. 2025 Jul 21;3(2):e002296. doi: 10.1136/bmjph-2024-002296. eCollection 2025.
2
FaciaVox: A diverse multimodal biometric dataset of facial images and voice recordings.FaciaVox:一个包含面部图像和语音记录的多样化多模态生物识别数据集。
Data Brief. 2025 Mar 21;60:111489. doi: 10.1016/j.dib.2025.111489. eCollection 2025 Jun.
3
Enhancing Connected Health Ecosystems Through IoT-Enabled Monitoring Technologies: A Case Study of the Monit4Healthy System.

本文引用的文献

1
Governance, technology and citizen behavior in pandemic: Lessons from COVID-19 in East Asia.疫情中的治理、技术与公民行为:东亚地区新冠疫情的经验教训
Prog Disaster Sci. 2020 Apr;6:100090. doi: 10.1016/j.pdisas.2020.100090. Epub 2020 Apr 6.
2
COVID-19: Detecting Government Pandemic Measures and Public Concerns from Twitter Arabic Data Using Distributed Machine Learning.COVID-19:利用分布式机器学习从推特阿拉伯语数据中检测政府大流行病措施和公众关切。
Int J Environ Res Public Health. 2021 Jan 1;18(1):282. doi: 10.3390/ijerph18010282.
3
Internet of Things for Current COVID-19 and Future Pandemics: an Exploratory Study.
通过物联网监测技术增强互联健康生态系统:Monit4Healthy系统案例研究
Sensors (Basel). 2025 Apr 4;25(7):2292. doi: 10.3390/s25072292.
4
Key Performance Indicators for Service Robotics in Senior Community-Based Settings.基于社区的老年服务机器人的关键绩效指标
Healthcare (Basel). 2025 Mar 30;13(7):770. doi: 10.3390/healthcare13070770.
5
Force Map-Enhanced Segmentation of a Lightweight Model for the Early Detection of Cervical Cancer.用于宫颈癌早期检测的轻量级模型的力映射增强分割
Diagnostics (Basel). 2025 Feb 20;15(5):513. doi: 10.3390/diagnostics15050513.
6
Improved genetic algorithm based on greedy and simulated annealing ideas for vascular robot ordering strategy.基于贪心与模拟退火思想的改进遗传算法在血管机器人排序策略中的应用
PLoS One. 2025 Feb 20;20(2):e0306990. doi: 10.1371/journal.pone.0306990. eCollection 2025.
7
Connected Healthcare System Technology Interventions to Improve Patient Safety by Reducing Medical Errors: A Systematic Review.通过减少医疗差错来提高患者安全的互联医疗系统技术干预措施:一项系统综述。
Glob J Qual Saf Healthc. 2024 Dec 30;8(1):43-49. doi: 10.36401/JQSH-24-23. eCollection 2025 Feb.
8
Word-of-mouth referrals between patients are a critical component of medical tourism for pediatric hematopoietic cell transplantation.患者之间的口碑推荐是儿童造血细胞移植医疗旅游的关键组成部分。
Medicine (Baltimore). 2025 Jan 10;104(2):e41244. doi: 10.1097/MD.0000000000041244.
9
Advancement in Biosensor Technologies of 2D MaterialIntegrated with Cellulose-Physical Properties.二维材料与纤维素集成的生物传感器技术进展——物理性质
Micromachines (Basel). 2023 Dec 30;15(1):82. doi: 10.3390/mi15010082.
10
Discuss the Application of Data Services in Data Health Management of High-Risk Pregnant and Lying-In Women in Smart Medical Care.探讨数据服务在智慧医疗高危孕产妇及婴幼儿数据健康管理中的应用。
Scanning. 2022 Aug 25;2022:5957697. doi: 10.1155/2022/5957697. eCollection 2022.
面向当前新冠疫情及未来大流行的物联网:一项探索性研究。
J Healthc Inform Res. 2020;4(4):325-364. doi: 10.1007/s41666-020-00080-6. Epub 2020 Nov 12.
4
Adoption of Digital Technologies in Health Care During the COVID-19 Pandemic: Systematic Review of Early Scientific Literature.新冠疫情期间医疗保健领域数字技术的应用:早期科学文献的系统综述
J Med Internet Res. 2020 Nov 6;22(11):e22280. doi: 10.2196/22280.
5
A drone-based networked system and methods for combating coronavirus disease (COVID-19) pandemic.一种用于抗击冠状病毒病(COVID-19)大流行的基于无人机的网络系统和方法。
Future Gener Comput Syst. 2021 Feb;115:1-19. doi: 10.1016/j.future.2020.08.046. Epub 2020 Sep 3.
6
Reflux events detected by multichannel bioimpedance smart feeding tube during high flow nasal cannula oxygen therapy and enteral feeding: First case report.高流量鼻导管吸氧和肠内喂养期间多通道生物阻抗智能喂养管检测到的反流事件:首例报告。
J Crit Care. 2020 Dec;60:226-229. doi: 10.1016/j.jcrc.2020.08.005. Epub 2020 Aug 22.
7
Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review.机器学习和人工智能在2019冠状病毒病(严重急性呼吸综合征冠状病毒2)大流行中的应用:综述
Chaos Solitons Fractals. 2020 Oct;139:110059. doi: 10.1016/j.chaos.2020.110059. Epub 2020 Jun 25.
8
How telemedicine integrated into China's anti-COVID-19 strategies: case from a National Referral Center.远程医疗如何融入中国抗击新冠疫情的策略:来自一家国家级转诊中心的案例
BMJ Health Care Inform. 2020 Aug;27(3). doi: 10.1136/bmjhci-2020-100164.
9
A user-centered, learning asthma smartphone application for patients and providers.一款以用户为中心、面向患者和医疗服务提供者的学习型哮喘智能手机应用程序。
Learn Health Syst. 2020 Feb 18;4(3):e10217. doi: 10.1002/lrh2.10217. eCollection 2020 Jul.
10
Agents and robots for collaborating and supporting physicians in healthcare scenarios.用于协作和支持医疗场景中医生的代理和机器人。
J Biomed Inform. 2020 Aug;108:103483. doi: 10.1016/j.jbi.2020.103483. Epub 2020 Jun 27.