Un bosquejo de la inteligencia artificial frente a la COVID-19 en el mundo

Autores/as

  • Alejandro Rosete Suárez Universidad Tecnológica de La Habana "José Antonio Echevarría", CUJAE
  • María Matilde García Lorenzo Universidad Central de Las Villas "Marta Abreu"
  • Yailé Caballero Universidad de Camagüey
  • Rafael Bello Universidad Central de Las Villas "Marta Abreu"

Palabras clave:

Inteligencia Artificial, COVID 19, Diagnóstico médico, Aprendizaje Automático

Resumen

La pandemia de la COVID 19 ha cambiado muchos de los paradigmas de la sociedad moderna. Cuando ya han pasado nueve  meses desde su inicio, aún no se tiene claro cómo y cuándo terminará, pero sí se sabe que dejará una sociedad cambiada. En este trabajo, se presenta un bosquejo de lo que ha podido aportar la Inteligencia Artificial en esta batalla contra la pandemia. Para ello, se revisan más de 30 trabajos publicados en las principales revistas científicas, en que se reportan aplicaciones de la Inteligencia Artificial frente a la COVID. Entre las temáticas en los que  parece ser más importante están: el diagnóstico, el diseño de medicamentos, la predicción del comportamiento de la pandemia, y la gestión de la misma. Los métodos más empleados reportados son Aprendizaje Automático, y particularmente los de Aprendizaje Profundo. Sin ser exhaustivo, este bosquejo permite entender claramente el rol que la Inteligencia Artificial puede y debe jugar en este contexto.

Citas

Ahuja, A.S., Reddy, V.P. y Marques, O. (2020). Artificial intelligence and COVID-19: A multidisciplinary approach. Integrative Medicine Research, 9(100434). doi:10.1016/j.imr.2020.100434

Albahri, O.S., Zaidan, A.A., Albahri, A.S., Zaidan, B.B., Abdulkareem, K.H., Al-qaysi, Z.T., Alamoodi, A.H., Aleesa, A.M., Chyad, M.A., Alesa, R.M., Kem, B.B., Lakulu, M.M., Ibrahim, A.B. y Rashida, N.A. (2020). Systematic review of artificial intelligence techniques in the detection and classification of COVID-19 medical images in terms of evaluation and benchmarking: Taxonomy analysis, challenges, future solutions and methodological aspects. Journal of Infection and Public Health. doi:10.1016/j.jiph.2020.06.028

Araújo, E.J., Chaves, A.A. y Lorena, L.A.N. (2020). A mathematical model for the coverage location problem with overlap control. Computers & Industrial Engineering. 146(106548). doi:10.1016/j.cie.2020.106548

Barbosa-Libotte, G., Lobato, F.G., Mendes-Platt, G. y Silva-Neto, A.J. (2020). Determination of an optimal control strategy for vaccine administration in COVID-19 pandemic treatment. Computer Methods and Programs in Biomedicine, 196(105664). doi:10.1016/j.cmpb.2020.105664

Barredo-Arrieta, A., Díaz-Rodríguez, N., Del-Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., Chatilaf, R. y Herrera, F. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115. doi:10.1016/j.inffus.2019.12.012

Behnood, A., Golafshani, E. M. y Hosseini, S. M. (2020). Determinants of the infection rate of the COVID-19 in the U.S. using ANFIS and virus optimization algorithm (VOA). Chaos, Solitons and Fractals, 139(110051). doi:10.1016/j.chaos.2020.110051

Bello, R., Miao, D., Falcon, R., Nakata, M., Rosete,A. y Ciucci, D. (Eds.) (2020). Rough Sets, Proceedings of the International Joint Conference IJCRS 2020, Havana, Cuba, June 29 – July 3, 2020. Lecture Notes in Artificial Intelligence, Vol. 12179, [versión electrónica de Springerlink] https://www.springer.com/gp/book/9783030527044

Bullock, J., Luccioni, A., Hoffmann-Pham, K., Nga-Lam, C. S. y Luengo-Oroz, M. (2020). Mapping the Landscape of Artificial Intelligence Applications Against Covid-19. arXiv:2003.11336v1 cs.CY.

Cannon, J. (2019). Report shows consumers don’t trust artificial intelligence. Recuperado de: https://www.fintechnews.org/report-shows-consumers-dont-trust-artificial-intelligence/

Castillo, O. y Melin, P. (2020). Forecasting of COVID-19 time series for countries in the world based on a hybrid approach combining the fractal dimension and fuzzy logic. Chaos, Solitons and Fractals, 140(110242). doi: 10.1016/j.chaos.2020.110242

Coombs, C. (2020). Will COVID-19 be the tipping point for the Intelligent Automation of work? A review of the debate and implications for research. International Journal of Information Management, doi:10.1016/j.ijinfomgt.2020.102182

Díaz-Canel, M. y Núñez-Jover, J. (2020). Gestión gubernamental y ciencia cubana en el enfrentamiento a la COVID-19. Anales de la Academia de Ciencias de Cuba, 10(2, especial COVID-19)

Ebrahimnejad, A. y Verdegay, J.L. (2018). Fuzzy Sets-Based Methods and Techniques for Modern Analytics. Studies in Fuzziness and Soft Computing. Vol 364. Springer Nature. [versión electrónica de Springerlink] http://link.springer.com/book/10.1007/978-3-319-73903-8

Elavarasan, R.M. y Pugazhendhi, R. (2020). Restructured society and environment: A review on potential technological strategies to control the COVID-19 pandemic. Science of the Total Environment 725 (138858). doi: 10.1016/j.scitotenv.2020.138858

Farooq, J. y Bazaz, M.A. (2020). A novel adaptive deep learning model of COVID-19 with focus on mortality reduction strategies. Chaos, Solitons and Fractals, 138(110148). doi:10.1016/j.chaos.2020.110148

Fathollahi-Fard, A.M., Ahmadi, A., Goodarzian, F. y Cheikhrouhou, N. (2020). A bi-objective home healthcare routing and scheduling problem considering patients’ satisfaction in a fuzzy environment. Applied Soft Computing Journal, 93(106385). doi:10.1016/j.asoc.2020.106385

Gomes, R. Ribeiro, M. H. D. M., Mariani, V. C. y dos Santos, L. (2020). Forecasting Brazilian and American COVID-19 cases based on artificial intelligence coupled with climatic exogenous variables. Chaos, Solitons and Fractals, 139(110027). doi:10.1016/j.chaos.2020.110027

Issa, M. y Elaziz, M. A. (2020). Analyzing COVID-19 virus based on enhanced fragmented biological Local Aligner using improved Ions Motion Optimization algorithm. Applied Soft Computing Journal. doi:10.1016/j.asoc.2020.106683

Kaushik, A.C. y Raj, U. (2020). AI-driven drug discovery: A boon against COVID-19? AI Open, 1, 1–4. doi:10.1016/j.aiopen.2020.07.001

Kitchenham, B., Pretorius, R., Budgen, D., Brereton O. P., Turner, M., Niazi, M. y Linkman, S. (2010) Systematic literature reviews in software engineering – A tertiary study. Information and Software Technology, 52, 792–805. doi:10.1016/j.infsof.2010.03.006

Kubat, M. (2017). An Introduction to Machine Learning. 2nd ed. Springer International Publishing AG. [versión electrónica de Springerlink] http://link.springer.com/openurl?genre=book&isbn=978-3-319-63913-0

Kumar, A., Sharma, K., Singh, H., Naugriya, S.G., Gill S. S. y Buyya, R. (2021). A drone-based networked system and methods for combating coronavirus disease (COVID-19) pandemic. Future Generation Computer Systems, 115, 1–19, doi:10.1016/j.future.2020.08.046

Laghi, A. (2020). Cautions about radiologic diagnosis of COVID-19 infection driven by artificial intelligence. TheLancet, 2(May), 225. Recuperado: www.thelancet.com/digital-health.

Lalmuanawma, S., Hussain, J. y Chhakchhuak, L. (2020). Applications of machine learning and artificial intelligence for COVID-19 (SARS-CoV-2) pandemic: A review. Chaos, Solitons and Fractals, 139(110059), doi:10.1016/j.chaos.2020.110059

Lu, J., Feng, L., Yang, J., Hassan, M.M., Alelaiwi, A., y Humar, I. (2019). Artificial agent: The fusion of artificial intelligence and a mobile agent for energy-efficient traffic control in wireless sensor networks. Future Generation Computer Systems, 95, 45–51, doi:10.1016/j.future.2018.12.024

Magoulas, R. y Swoyer, S. (2020). AI Adoption in the Enterprise. Beijing: O´Reilly. Recuperado de http://www.oreilly.com/data/free/ai-adoption-in-the-enterprise.csp

Malki, Z., Atlam, E.S., Hassanien, A.E., Dagnew, G., Elhosseini, M.A. y Gad, I. (2020). Association between weather data and COVID-19 pandemic predicting mortality rate: Machine learning approaches. Chaos, Solitons and Fractals, 138(110137). doi:10.1016/j.chaos.2020.110137

Mardani, A, Saraji, M.K., Mishra, A.R. y Rani, P. (2020). A novel extended approach under hesitant fuzzy sets to design a framework for assessing the key challenges of digital health interventions adoption during the COVID-19 outbreak. Applied Soft Computing Journal, 96(106613). doi:10.1016/j.asoc.2020.106613

Mohanty, S., Harun, M., Rashid, A. I., Mridul, M., Mohanty, C. y Swayamsiddha, S. (2020). Application of Artificial Intelligence in COVID-19 drug repurposing. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 14, 1027-1031. doi: 10.1016/j.dsx.2020.06.068

Montes, G.A. y Goertzel, B. (2019). Distributed, decentralized, and democratized artificial intelligence. Technological Forecasting & Social Change, 141, 354–358. doi:10.1016/j.techfore.2018.11.010

Nikolopoulos, K., Punia, S., Schäfers, A., Tsinopoulos, C. y Vasilakis, C. (2020). Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions. European Journal of Operational Research. doi:10.1016/j.ejor.2020.08.001

Ocampo, L y Yamagishi, K. (2020). Modeling the lockdown relaxation protocols of the Philippine government in response to the COVID-19 pandemic: An intuitionistic fuzzy DEMATEL analysis. Socio-Economic Planning Sciences, doi:10.1016/j.seps.2020.100911

Otoom, M., Otoum, N., Alzubaidi, M.A., Etoom, Y. y Banihani, R. (2020). An IoT-based framework for early identification and monitoring of COVID-19 cases. Biomedical Signal Processing and Control, 62(102149). doi:10.1016/j.bspc.2020.102149

Oyelade, O.N., y Ezugwu, A.E. (2020). A case-based reasoning framework for early detection and diagnosis of novel coronavirus. Informatics in Medicine Unlocked, 20100395. doi:10.1016/j.imu.2020.100395

Park, Y., Casey,D., Joshi, I., Zhu, J. y Cheng, F. (2020). Emergence of New Disease: How Can Artificial Intelligence Help? Trends in Molecular Medicine, July 26(7), 627-629. doi:10.1016/j.molmed.2020.04.007

Perrault, R., Shoham. Y., Brynjolfsson. E., Clark, J., Etchemendy, J., Grosz. B., et al. (2019) The AI Index 2019 Annual Report. AI Index Steering Committee. Human-Centered AI Institute. Stanford University. Recuperado de http://hai.stanford.edu/

Raju Vaishya, R., Javaid, M., Khan, I. H. y Haleem, A. (2020). Artificial Intelligence (AI) applications for COVID-19 pandemic. Diabetes & Metabolic Syndrome: Clinical Research & Reviews 14, 337-339. doi:10.1016/j.dsx.2020.04.012

Ransing, R., Nagendrappa, S., Patil, A., Shoib, S. y Sarkar, D. (2020). Potential role of artificial intelligence to address the COVID-19 outbreak-related mental health issues in India. Psychiatry Research, 290(113176). doi:10.1016/j.psychres.2020.113176

Ren, Z., Liao, H., y Liu Y. (2020). Generalized Z-numbers with hesitant fuzzy linguistic information and its application to medicine selection for the patients with mild symptoms of the COVID-19. Computers & Industrial Engineering, 145(106517). doi:10.1016/j.cie.2020.106517

Russell, S.J. y Norvig, P. (2010). Artificial Intelligence: A Modern Approach. 3rd ed. New Jersey: Prentice Hall.

Shaikh, F., Brun-Andersen, M., Sohail, M. R., Mulero, F., Awan, O., Dupont-Roettger, D., Kubassova, O., Dehmeshki, J., y Bisdas, S. (2020). Current Landscape of Imaging and the Potential Role for Artificial Intelligence in the Management of COVID-19, Current Problems in Diagnostic Radiology, 000, 1-6, doi: 10.1067/j.cpradiol.2020.06.009

Sipior, J.C. (2020). Considerations for development and use of AI in response to COVID-19. International Journal of Information Management. doi:10.1016/j.ijinfomgt.2020.102170

Skansi, S. (2018). Introduction to Deep Learning: From Logical Calculus to Artificial Intelligence. Springer Nature. [versión electrónica de Springerlink] http://link.springer.com/openurl?genre=book&isbn=978-3-319-73004-2

Skiena, S.S. (2017). The Data Science Design Manual. Springer [versión electrónica de Springerlink] http://link.springer.com/openurl?genre=book&isbn=978-3-319-55444-0

Suri, J.S., Puvvula, A., Biswas, M., Majhail, M., Saba, L., Faa, G., Singh, I.M., Oberleitner, R., Turk, M., Chadha, P.S., Johri, A.M., Sanches, J.M., Khanna, N.N., Viskovic, K., Mavrogeni, S., Laird, J.R., Pareek, G., Miner, M., Sobel, D.W., Balestrieri, A., Sfikakis, P.P., Tsoulfas, G., Protogerou, A., Misra, D.P., Agarwal, V., Kitas, G.D., Ahluwalia, P., Kolluri, R., Teji, J., Maini, M.A., Agbakoba, A, Dhanjil, S.K., Sockalingam, M., Saxena, A., Nicolaides, A., Sharma, A., Rathore, V., Ajuluchukwu,, J.N.A., Fatemi, M., Alizad, A., Viswanathan, V., Krishnan, P.K. y Naidu, S. (2020). COVID-19 pathways for brain and heart injury in comorbidity patients: A role of medical imaging and artificial intelligence-based COVID severity classification: A review. Computers in Biology and Medicine, doi: 10.1016/j.compbiomed.2020.103960

Swapnarekha, H., Behera, H.S., Nayak, J., y Naik B. (2020). Role of intelligent computing in COVID-19 prognosis: A state-of-the-art review. Chaos, Solitons and Fractals, 138(109947). doi: 10.1016/j.chaos.2020.109947

Talbi, E.G. (2009). Metaheuristics from Design to Implementation. London, UK: Wiley

Togaçar, M., Ergen, B. y Comert, Z. (2020). COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches. Computers in Biology and Medicine, 121(103805). doi:10.1016/j.compbiomed.2020.103805

Velázquez-Pérez, L. (2020). La COVID-19: reto para la ciencia mundial, Editorial. Anales de la Academia de Ciencias de Cuba, 10(2, especial COVID-19)

Vinod, D.N. y Prabaharan, S.R.S. (2020). Data science and the role of Artificial Intelligence in achieving the fast diagnosis of COVID-1., Chaos, Solitons and Fractals, 140(110182), doi:10.1016/j.chaos.2020.110182

Wang, W. y Siau, K. (2018). Trusting Artificial Intelligence in Healthcare. En Twenty-fourth Americas Conference on Information Systems (AMCIS´2018), At.New Orleans. Recuperado de: http://www.researchgate.net

Yan, T., Wong, P. K., Ren,H., Wang, H., Wang, J. y Li, Y. (2020). Automatic distinction between COVID-19 and common pneumonia using multi-scale convolutional neural network on chest CT scans. Chaos, Solitons and Fractals, 140(110153), doi:10.1016/j.chaos.2020.110153

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Publicado

2020-12-13

Cómo citar

Rosete Suárez, A., García Lorenzo, M. M., Caballero, Y., & Bello, R. (2020). Un bosquejo de la inteligencia artificial frente a la COVID-19 en el mundo. Revista Cubana De Transformación Digital, 1(3), 05–26. Recuperado a partir de https://rctd.uic.cu/rctd/article/view/93

Número

Sección

Sección especial: Transformación digital ante la COVID-19