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How Artificial Intelligence and New Technologies Can Help the Management of the COVID-19 Pandemic

Author

Listed:
  • Davide Barbieri

    (Department of Neuroscience and Rehabilitation, University of Ferrara, Via Savonarola 9, 44121 Ferrara, Italy)

  • Enrico Giuliani

    (Department of Biomedical, Metabolic and Neuroscience Sciences, University of Modena and Reggio Emilia, Via Del Pozzo 71, 41125 Modena, Italy)

  • Anna Del Prete

    (School of Anesthesiology and Intensive Care, University of Modena and Reggio Emilia, Via Del Pozzo 71, 41125 Modena, Italy)

  • Amanda Losi

    (Department of Biomedical, Metabolic and Neuroscience Sciences, University of Modena and Reggio Emilia, Via Del Pozzo 71, 41125 Modena, Italy)

  • Matteo Villani

    (Department of Anesthesiology and Intensive Care, Azienda USL Piacenza, Via Antonio Anguissola 15, 29121 Piacenza, Italy)

  • Alberto Barbieri

    (School of Anesthesiology and Intensive Care, University of Modena and Reggio Emilia, Via Del Pozzo 71, 41125 Modena, Italy)

Abstract

The COVID-19 pandemic has worked as a catalyst, pushing governments, private companies, and healthcare facilities to design, develop, and adopt innovative solutions to control it, as is often the case when people are driven by necessity. After 18 months since the first case, it is time to think about the pros and cons of such technologies, including artificial intelligence—which is probably the most complex and misunderstood by non-specialists—in order to get the most out of them, and to suggest future improvements and proper adoption. The aim of this narrative review was to select the relevant papers that directly address the adoption of artificial intelligence and new technologies in the management of pandemics and communicable diseases such as SARS-CoV-2: environmental measures; acquisition and sharing of knowledge in the general population and among clinicians; development and management of drugs and vaccines; remote psychological support of patients; remote monitoring, diagnosis, and follow-up; and maximization and rationalization of human and material resources in the hospital environment.

Suggested Citation

  • Davide Barbieri & Enrico Giuliani & Anna Del Prete & Amanda Losi & Matteo Villani & Alberto Barbieri, 2021. "How Artificial Intelligence and New Technologies Can Help the Management of the COVID-19 Pandemic," IJERPH, MDPI, vol. 18(14), pages 1-10, July.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:14:p:7648-:d:596736
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    References listed on IDEAS

    as
    1. Luciana Zaccagni & Stefania Toselli & Davide Barbieri, 2021. "Physical Activity during COVID-19 Lockdown in Italy: A Systematic Review," IJERPH, MDPI, vol. 18(12), pages 1-13, June.
    2. Ali Asgary & Svetozar Zarko Valtchev & Michael Chen & Mahdi M. Najafabadi & Jianhong Wu, 2020. "Artificial Intelligence Model of Drive-Through Vaccination Simulation," IJERPH, MDPI, vol. 18(1), pages 1-10, December.
    3. Jelena Musulin & Sandi Baressi Šegota & Daniel Štifanić & Ivan Lorencin & Nikola Anđelić & Tijana Šušteršič & Anđela Blagojević & Nenad Filipović & Tomislav Ćabov & Elitza Markova-Car, 2021. "Application of Artificial Intelligence-Based Regression Methods in the Problem of COVID-19 Spread Prediction: A Systematic Review," IJERPH, MDPI, vol. 18(8), pages 1-39, April.
    4. Davide Barbieri & Nitesh Chawla & Luciana Zaccagni & Tonći Grgurinović & Jelena Šarac & Miran Čoklo & Saša Missoni, 2020. "Predicting Cardiovascular Risk in Athletes: Resampling Improves Classification Performance," IJERPH, MDPI, vol. 17(21), pages 1-9, October.
    5. Edouard Mathieu & Hannah Ritchie & Esteban Ortiz-Ospina & Max Roser & Joe Hasell & Cameron Appel & Charlie Giattino & Lucas Rodés-Guirao, 2021. "A global database of COVID-19 vaccinations," Nature Human Behaviour, Nature, vol. 5(7), pages 947-953, July.
    6. Nikola Anđelić & Sandi Baressi Šegota & Ivan Lorencin & Zdravko Jurilj & Tijana Šušteršič & Anđela Blagojević & Alen Protić & Tomislav Ćabov & Nenad Filipović & Zlatan Car, 2021. "Estimation of COVID-19 Epidemiology Curve of the United States Using Genetic Programming Algorithm," IJERPH, MDPI, vol. 18(3), pages 1-26, January.
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