APLICACIÓN DE LA INTELIGENCIA ARTIFICIAL EN LA NUTRICIÓN PERSONALIZADA
DOI:
https://doi.org/10.26788/ri.v11i4.3990Palabras clave:
Aplicación móvil, inteligencia artificial, nuevas tecnologías y nutrición personalizada.Resumen
El desarrollo tecnológico ha influido en diversas áreas del conocimiento y la actividad humana. La aparición de tecnologías como big data, machine learning o inteligencia artificial están revolucionando las relaciones humanas; dichas tecnologías, y actualmente, se utilizan en diversas actividades y ámbitos. En este contexto, la nutrición promueve la alimentación de calidad, crea alimentos nutritivos, establece patrones de consumo saludables, evita el desperdicio de alimentos, genera seguridad alimentaria, nutrición personalizada, entre otros aspectos. En esta investigación se realizó una revisión documental de las principales investigaciones sobre las diversas aplicaciones de la inteligencia artificial en el campo de la nutrición personalizada. El problema planteado fue: ¿Cómo se aplica la inteligencia artificial en la nutrición personalizada? El objetivo fue analizar la aplicación de la inteligencia artificial en la nutrición personalizada. La metodología usó el enfoque cualitativo, el tipo de investigación fue descriptivo-exploratorio, los métodos fueron el descriptivo y la observación, las técnicas consideraron el análisis documental y análisis de contenido y los instrumentos fueron la ficha de resumen, ficha de análisis documental y ficha de análisis bibliográfico. Finalmente, los resultados y conclusiones demuestran que la inteligencia artificial se aplica en la nutrición personalizada a través de aplicativos móviles y otros, contribuye en la nutrición personalizada y su uso inadecuado podría originar riesgos en la nutrición personalizada.
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Albar, S. A., Alwan, N. A., Evans, C. E. L., Greenwood, D. C., & Cade, J. E. (2016). Agreement between an online dietary assessment tool (myfood24) and an interviewer-administered 24-h dietary recall in British adolescents aged 11–18 years. British Journal of Nutrition, 115(9), 1678–1686. https://doi.org/10.1017/S0007114516000593
Celis-Morales, C., Lara, J., & Mathers, J. C. (2015). Personalising nutritional guidance for more effective behaviour change. Proceedings of the Nutrition Society, 74(2), 130–138. https://doi.org/10.1017/S0029665114001633
Cesare, N., Dwivedi, P., Quynh, C., & Nsoesie, E. (2019). Use of social media, search queries, and demographic data to assess obesity prevalence in the United States. Humanities and Social Sciences Communications, 106(5). https://doi.org/10.1057/s41599-019-0314-x
Deen, T. (2019). ¿La inteligencia artificial es la solución para la crisis alimentaria? Inter Press Service (IPS). https://reliefweb.int/report/world/la-inteligencia-artificial-es-la-soluci-n-para-la-crisis-alimentaria
Del Prado, G. (2017). Intelligent robots don’t need to be conscious to turn against us. Insider. https://www.businessinsider.com/artificial-intelligence-machine-consciousness-expert-stuart-russell-future-ai-2015-7
Dongare, A., Kharde, R. R., & Kachare, A. D. (2012). Introduction to Artificial Neural Network. https://www.semanticscholar.org/
Ertel, W. (2017). Introduction to artificial intelligence. 2da Edición. Springer Cham. https://doi.org/10.1007/978-3-319-58487-4
Fluss, D. (2017). The AI revolution in customer service. CMR. https://www.destinationcrm.com/Articles/Columns-Departments/Scouting-Report/The-AI-Revolution-in-Customer-Service-115528.aspx
Forrestal, S. (2010). Energy intake misreporting among children and adolescents: a literature review. Maternal & Child Nutrition, 7(2), 112–127. https://doi.org/10.1111/j.1740-8709.2010.00270.x
Green, B. (2018). Ethical Reflections on Artificial Intelligence. Scientia et Fides, 6(2), 9–31. https://doi.org 10.12775/SetF.2018.015
Guillen, S. (2018). Industria 4.0: Machine learning y la visión artificial en la seguridad alimentaria. Ainia. https://www.ainia.es/ainia-news/industria-4-vision-artificial-seguridad-alimentaria/
Halzack, S. (2017). Robots and artificial intelligence set to upend the art of making a sale. The Whashington Post. https://www.washingtonpost.com/news/business/wp/2017/01/18/robots-and-artificial-intelligence-set-to-upend-the-art-of-making-a-sale/
Hamet, P., & Tremblay, J. (2016). Artificial intelligence in medicine. Metabolism: Clinical and Experimental, 36–40. https://doi.org/10.1016/j.metabol.2017.01.011
He, H., Cangelosi, A., Mcginnity, T. M., & Mehnen, J. (2020). The Challenges and Opportunities of Artificial Intelligence in Implementing Trustworthy Robotics and Autonomous Systems. SpringertLink. https://doi.org/10.1109/IRCE50905.2020.9199244
Hernandez, R., Fernandez, C., & Baptista, M. del P. (2014). Metodología de la investigación. In M. G. Hill, Journal of Chemical Information and Modeling. 6ta. Edición, Vol. 53, Issue 9. https://www.esup.edu.pe/wp-content/uploads/2020/12/2. Hernandez, Fernandez y Baptista-Metodología Investigacion Cientifica 6ta ed.pdf
Hood, L., Heath, J. R., Phelps, M. E., & Lin, B. (2021). Systems Biology and new technologies enable predictive and preventative medicine. Cell Biology and Translational Medicine, 12, 47–53. https://doi.org/10.1007/5584_2021_622
Huang, M. H., & Rust, R. T. (2018). Artificial Intelligence in Service. Journal of Service Research, 21(2), 155–172. https://doi.org/10.1177/1094670517752459
Huang, M., & Rust, R. (2017). Technology-driven service strategy. The Academy of Marketing Science, 45, 906–924. https://doi.org/10.1007/s11747-017-0545-6
Jaewon, Y., & Arnold, T. (2016). No TitleFrontline Employee Customer-Oriented Attitude in the Presence of Job Demands and Resources: The Influence Upon Deep and Surface Acting. SAGE Journals, 19(1), 102–117. https://doi.org/10.1177/1094670515589956
Johnson, H. (2016). Fast food workers are becoming obsolete. Business Insider. https://www.businessinsider.in/fast-food-workers-are-becoming-obsolete/articleshow/52300518.cms
Kirk, D., Catal, C., & Tekinerdogan, B. (2021). Precision nutrition: A systematic literature review. Computers in Biology and Medicine, 133(104365). https://doi.org/10.1016/j.compbiomed.2021.104365
Koteluk, O., Wartecki, A., Mazurek, S., Kołodziejczak, I., & Mackiewicz, A. (2021). How do machines learn? Artificial intelligence as a new era in medicine. Journal of Personalized Medicine, 11(1), 1–22. https://doi.org/10.3390/jpm11010032
Kouvari, M., Mamalaki, E., Bathrellou, E., & Poulimeneas, Dimitrios Yannakoulia, M. D. B. (2021). The validity of technology-based dietary assessment methods in childhood and adolescence: a systematic review. Critical Reviews in Food Science and Nutrition, 61(7), 1065–1080. https://doi.org/10.1080/10408398.2020.1753166
Kunz, W. H., Heinonen, K., & Lemmink, J. (2019). Future service technologies: is service research on track with business reality? Journal of Services Marketing, 33(4), 479–487. https://doi.org/10.1108/JSM-01-2019-0039
Kwon, D. Y., & Kwon, D. Y. (2020). Personalized diet oriented by artificial intelligence and ethnic foods. Journal of Ethnic Foods, 7(1), 1–16. https://doi.org/10.1186/s42779-019-0040-4
Leachman, S., & Merlino, G. (2017). Medicine: The final frontier in cancer diagnosis. National Library of Medicine, 542, 36–38. https://doi.org/10.1038/nature21492
Li, D., & Du, Y. (2016). Artificial Intelligence with Uncertainty (2da Edició). Boca Raton. https://doi.org/10.1201/9781315366951
Limketkai, B. N., Mauldin, K., Manitius, A., & Jalilian, L. (2021). The Age of Artificial Intelligence: Use of Digital Technology in Clinical Nutrition. Neurocirugía Brasileña, 9(7). https://doi.org/10.1007/s40137-021-00297-3
Nacional, E. (2019). La nutrición personalizada es el futuro de la alimentación y la salud. El Nacional.Cat. https://www.elnacional.cat/es/salud/nutricion-personalizada-futuro-alimentacion-salud_396661_102.html
Naimi, A., & Balzer, L. (2018). Stacked generalization: an introduction to super learning. National Library of Medicine, 33(5), 459–464. https://doi.org/10.1007/s10654-018-0390-z
Ng, A., & Neil, J. (2017). How artificial intelligence will change everything. The Wall Street Journal. https://www.wsj.com/articles/how-artificial-intelligence-will-change-everything-1488856320
Oke, S. (2008). A literature review on artificial intelligence. International Journal of Information and Management Sciences, 19(4), 535–570. https://www.researchgate.net/publication/228618921%0D
Panaretos, D., Koloverou, E., Dimopoulos, A. C., Kouli, G. M., Vamvakari, M., Tzavelas, G., Pitsavos, C., & Panagiotakos, D. B. (2018). A comparison of statistical and machine-learning techniques in evaluating the association between dietary patterns and 10-year cardiometabolic risk (2002-2012): The ATTICA study. British Journal of Nutrition, 120(3), 326–334. https://doi.org/10.1017/S0007114518001150
Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: promise and potential. Health Information Science and Systems, 2(1), 3. https://doi.org/10.1186/2047-2501-2-3
Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., & Yang, G. (2017). Deep Learning for Health Informatics. IEEE Journal of Biomedical and Health Informatics, 21(1), 4–21. https://doi.org/10.1109/JBHI.2016.2636665
Roberts, K., Roberts, J. H., Danaher, P. J., & Raghavan, R. (2015). Incorporating emotions into evaluation and choice models: Application to kmart Australia. Marketing Science, 34(6), 815–824. https://doi.org/10.1287/mksc.2015.0954
Sak, J., & Suchodolska, M. (2021). Artificial intelligence in nutrients science research: A review. Nutrients, 13(2), 1–17. https://doi.org/10.3390/nu13020322
Shaw, G., & Karami, A. (2017). Computational content analysis of negative tweets for obesity, diet, diabetes, and exercise. ASIS&T, 54(1), 357–365. https://doi.org/10.1002/pra2.2017.14505401039
Stuart, S. (2016). How do you feel? affectiva’s AI can tell. PCMAG. https://www.pcmag.com/news/how-do-you-feel-affectivas-ai-can-tell
Vargas-Hernández, J. E. (2016). Human nutrigenomics: Effects of food or food components on rna expression. Revista Facultad de Medicina, 64(2), 339–349. https://doi.org/10.15446/revfacmed.v64n2.51080
Vasaikar, S., Huang, C., Wang, X., Petyuk, V. A., Savage, S. R., Wen, B., Dou, Y., Zhang, Y., Shi, Z., Arshad, O. A., Gritsenko, M. A., Zimmerman, L. J., McDermott, J. E., Clauss, T. R., Moore, R. J., Zhao, R., Monroe, M. E., Wang, Y. T., Chambers, M. C., Watson, M. (2019). Proteogenomic Analysis of Human Colon Cancer Reveals New Therapeutic Opportunities. Cell, 177(4), 1035-1049.e19. https://doi.org/10.1016/j.cell.2019.03.030
Wedel, M., & Kannan, P. (2016). Marketing Analytics for Data-Rich Environments. American Marketing Association, 80(6), 97–121. https://doi.org/10.1509/jm.15.0413
Young, J., & Cormier, D. (2014). Can Robots Be Managers, Too? Harvart Business Review. https://hbr.org/2014/04/can-robots-be-managers-too
Yu, K. H., Beam, A. L., & Kohane, I. S. (2018). Artificial intelligence in healthcare. Nature Biomedical Engineering, 2(10), 719–731. https://doi.org/10.1038/s41551-018-0305-z
Zeevi, D., Korem, T., Zmora, N., Israeli, D., Rothschild, D., Weinberger, A., Ben-Yacov, O., Lador, D., Avnit-Sagi, T., Lotan-Pompan, M., Suez, J., Mahdi, J. A., Matot, E., Malka, G., Kosower, N., Rein, M., Zilberman-Schapira, G., Dohnalová, L., Pevsner-Fischer, M., Segal, E. (2015). Personalized Nutrition by Prediction of Glycemic Responses. Cell, 163(5), 1079–1094. https://doi.org/10.1016/j.cell.2015.11.001
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Derechos de autor 2023 Karla Cecilia Rivera Valdivia
Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
