CovidSens: a vision on reliable social sensing for COVID-19 DOI Creative Commons
Md Tahmid Rashid, Dong Wang

Artificial Intelligence Review, Journal Year: 2020, Volume and Issue: 54(1), P. 1 - 25

Published: June 12, 2020

With the spiraling pandemic of Coronavirus Disease 2019 (COVID-19), it has becoming inherently important to disseminate accurate and timely information about disease. Due ubiquity Internet connectivity smart devices, social sensing is emerging as a dynamic AI-driven paradigm extract real-time observations from online users. In this paper, we propose CovidSens, vision sensing-based risk alert systems spontaneously obtain analyze data infer state COVID-19 propagation. CovidSens can actively help keep general public informed spread identify risk-prone areas by inferring future propagation patterns. The concept motivated three observations: (1) people have been sharing their health experience via media, (2) official warning channels news agencies are relatively slower than reporting experiences on (3) users frequently equipped with substantially capable mobile devices that able perform non-trivial on-device computation for processing analytics. We envision an unprecedented opportunity leverage posts generated ordinary build analytic system gathering circulating vital Specifically, attempts answer questions: How distill reliable coexistence prevailing rumors misinformation in media? inform latest effectively, them remain prepared? computational power edge (e.g., smartphones, IoT UAVs) construct fully integrated edge-based platforms rapid detection spread? discuss roles potential challenges developing systems. approaches originating multiple disciplines AI, estimation theory, machine learning, constrained optimization) be effective addressing challenges. Finally, outline few research directions work CovidSens.

Language: Английский

IoT in the Wake of COVID-19: A Survey on Contributions, Challenges and Evolution DOI Creative Commons
Musa Ndiaye, Stephen S. Oyewobi, Adnan M. Abu‐Mahfouz

et al.

IEEE Access, Journal Year: 2020, Volume and Issue: 8, P. 186821 - 186839

Published: Jan. 1, 2020

The novel coronavirus (COVID-19), declared by the World Health Organization (WHO) as a global pandemic, has brought with it changes to general way of life. Major sectors world industry and economy have been affected Internet Things (IoT) management framework is no exception in this regard. This article provides an up date survey on how pandemic such COVID-19 IoT technologies. It looks at contributions that associated sensor technologies made towards virus tracing, tracking spread mitigation. challenges deployment hardware face rapidly spreading looked into part review article. effects evolution architectures also addressed, leading likely outcomes future implementations. In general, insight advancement sensor-based E-health pandemics. answers question shaped networks.

Language: Английский

Citations

167
Emerging Technologies for Use in the Study, Diagnosis, and Treatment of Patients with COVID-19 DOI Creative Commons
Maria Tsikala Vafea, Eleftheria Atalla,

Joanna Georgakas

et al.

Cellular and Molecular Bioengineering, Journal Year: 2020, Volume and Issue: 13(4), P. 249 - 257

Published: June 24, 2020

The COVID-19 pandemic has caused an unprecedented health and economic worldwide crisis. Innovative solutions are imperative given limited resources immediate need for medical supplies, healthcare support treatments. purpose of this review is to summarize emerging technologies being implemented in the study, diagnosis, treatment COVID-19. Key focus areas include applications artificial intelligence, use Big Data Internet Things, importance mathematical modeling predictions, utilization technology community screening, nanotechnology vaccine development, utility telemedicine, implementation 3D-printing manage new demands potential robotics. concludes by highlighting collaboration scientific with open sharing knowledge, tools, expertise.

Language: Английский

Citations

152
Artificial Intelligence for COVID-19: A Systematic Review DOI Creative Commons

Lian Wang,

Yonggang Zhang, Dongguang Wang

et al.

Frontiers in Medicine, Journal Year: 2021, Volume and Issue: 8

Published: Sept. 30, 2021

Background: Recently, Coronavirus Disease 2019 (COVID-19), caused by severe acute respiratory syndrome virus 2 (SARS-CoV-2), has affected more than 200 countries and lead to enormous losses. This study systematically reviews the application of Artificial Intelligence (AI) techniques in COVID-19, especially for diagnosis, estimation epidemic trends, prognosis, exploration effective safe drugs vaccines; discusses potential limitations. Methods: We report this systematic review following Preferred Reporting Items Systematic Reviews Meta-Analyses (PRISMA) guidelines. searched PubMed, Embase Cochrane Library from inception 19 September 2020 published studies AI applications COVID-19. used PROBAST (prediction model risk bias assessment tool) assess quality literature related diagnosis prognosis registered protocol (PROSPERO CRD42020211555). Results: included 78 studies: 46 articles discussed AI-assisted COVID-19 with total accuracy 70.00 99.92%, sensitivity 73.00 100.00%, specificity 25 area under curve 0.732 1.000. Fourteen evaluated based on clinical characteristics at hospital admission, such as clinical, laboratory radiological characteristics, reaching 74.4 95.20%, 72.8 98.00%, 55 96.87% AUC 0.66 0.997 predicting critical Nine models predict peak, infection rate, number infected cases, transmission laws, development trend. Eight explore drugs, primarily through drug repurposing development. Finally, 1 article predicted vaccine targets that have develop vaccines. Conclusions: In review, we shown achieved high performance evaluation, prediction discovery enhance significantly existing medical healthcare system efficiency during pandemic.

Language: Английский

Citations

132
Using artificial intelligence to improve public health: a narrative review DOI Creative Commons
David B. Olawade,

Ojima J. Wada,

Aanuoluwapo Clement David-Olawade

et al.

Frontiers in Public Health, Journal Year: 2023, Volume and Issue: 11

Published: Oct. 26, 2023

Artificial intelligence (AI) is a rapidly evolving tool revolutionizing many aspects of healthcare. AI has been predominantly employed in medicine and healthcare administration. However, public health, the widespread employment only began recently, with advent COVID-19. This review examines advances health potential challenges that lie ahead. Some ways aided delivery are via spatial modeling, risk prediction, misinformation control, surveillance, disease forecasting, pandemic/epidemic diagnosis. implementation not universal due to factors including limited infrastructure, lack technical understanding, data paucity, ethical/privacy issues.

Language: Английский

Citations

103
Artificial intelligence for forecasting and diagnosing COVID-19 pandemic: A focused review DOI
Carmela Comito, Clara Pizzuti

Artificial Intelligence in Medicine, Journal Year: 2022, Volume and Issue: 128, P. 102286 - 102286

Published: March 28, 2022

Language: Английский

Citations

93
Machine learning applications for COVID-19 outbreak management DOI Open Access
Arash Heidari, Nima Jafari Navimipour, Mehmet Ünal

et al.

Neural Computing and Applications, Journal Year: 2022, Volume and Issue: 34(18), P. 15313 - 15348

Published: June 10, 2022

Language: Английский

Citations

93
IoMT: A COVID-19 Healthcare System Driven by Federated Learning and Blockchain DOI
Omaji Samuel, Akogwu Blessing Omojo, Abdulkarim Musa Onuja

et al.

IEEE Journal of Biomedical and Health Informatics, Journal Year: 2022, Volume and Issue: 27(2), P. 823 - 834

Published: Jan. 18, 2022

Internet of medical things (IoMT) has made it possible to collect applications and devices improve healthcare information technology. Since the advent pandemic coronavirus (COVID-19) in 2019, public health become more sensitive than ever. Moreover, different news items incorporated have resulted differing perceptions COVID-19, especially on social media platform infrastructure. In addition, unprecedented virality changing nature COVID-19 makes call centres be likely overstressed, which is due a lack authentic unregulated information. Furthermore, data privacy restricted sharing among institutions. To resolve above-mentioned limitations, this paper proposing infrastructure based federated learning blockchain. The proposed potentials enhance trust authenticity disseminate Also, can effectively provide shared model while preserving owners. security analyses show that robust against security-related attacks.

Language: Английский

Citations

84
The dark side of artificial intelligence in service: The “watching-eye” effect and privacy concerns DOI
Yaou Hu, Hyounae Min

International Journal of Hospitality Management, Journal Year: 2023, Volume and Issue: 110, P. 103437 - 103437

Published: Feb. 10, 2023

Language: Английский

Citations

58
Multivariate risks assessment for complex bio-systems by Gaidai-Xing reliability method DOI Creative Commons
Oleg Gaidai, Vladimir Yakimov, Qingsong Hu

et al.

Systems and Soft Computing, Journal Year: 2024, Volume and Issue: 6, P. 200074 - 200074

Published: Jan. 24, 2024

Spread of novel coronavirus and other flu-like illnesses, periodically causing increased death morbidity rates, places pressures on national health systems. In order to provide a reliable long-term forecast the new infection rate, this research employs Gaidai-Xing bio-system reliability technique, especially suitable for multi-regional biological, environmental public The goal study was directly apply state art statistical techniques unprocessed raw clinical data, utilizing multicenter, population-based biostatistical methodology. Epidemiological risks have been accurately forecasted, specifically European Union member states. Based their survey suggested spatiotemporal methodology may be applied in variety biological applications.

Language: Английский

Citations

29
Development and preliminary testing of Health Equity Across the AI Lifecycle (HEAAL): A framework for healthcare delivery organizations to mitigate the risk of AI solutions worsening health inequities DOI Creative Commons
Jee Young Kim, Alifia Hasan, Katherine C. Kellogg

et al.

PLOS Digital Health, Journal Year: 2024, Volume and Issue: 3(5), P. e0000390 - e0000390

Published: May 9, 2024

The use of data-driven technologies such as Artificial Intelligence (AI) and Machine Learning (ML) is growing in healthcare. However, the proliferation healthcare AI tools has outpaced regulatory frameworks, accountability measures, governance standards to ensure safe, effective, equitable use. To address these gaps tackle a common challenge faced by delivery organizations, case-based workshop was organized, framework developed evaluate potential impact implementing an solution on health equity. Health Equity Across Lifecycle (HEAAL) co-designed with extensive engagement clinical, operational, technical, leaders across organizations ecosystem partners US. It assesses 5 equity assessment domains–accountability, fairness, fitness for purpose, reliability validity, transparency–across span eight key decision points adoption lifecycle. process-oriented containing 37 step-by-step procedures evaluating existing 34 new total. Within each procedure, it identifies relevant stakeholders data sources used conduct procedure. HEAAL guides how may mitigate risk solutions worsening inequities. also informs much resources support are required assess

Language: Английский

Citations

16