Potencial de la tecnología en la medicina

retos y oportunidades para México

Autores/as

DOI:

https://doi.org/10.56162/transdigital353

Palabras clave:

internet de las cosas médicas, almacenaje en la nube, inteligencia artificial, aprendizaje de máquina, redes neuronales convolucionales, ehealth, mHealth

Resumen

La industria médica es el área donde el uso de tecnología transformó los entornos de evaluación de la salud humana. Esto propició el uso de tendencias tecnológicas como mecanismo de procuración médica. Actualmente, existen diferentes términos técnicos que forman parte de la comunidad médica. Esto les proporciona un dinamismo comparable a algunos sectores industriales como el de la aeronáutica. Desde su origen, la medicina aceptó el uso de técnicas y desarrollo científico como parte esencial de sus procedimientos y métodos para la atención de enfermedades, padecimientos o brotes infecciosos. Conocer esta situación en la industria médica nacional permite ubicar el potencial y las oportunidades que existen en este sector. Esta investigación proporcionó un panorama general de la terminología y los aspectos más relevantes relacionado al uso de esta y otras tecnologías en la industria médica. La seguridad y el uso de la información se unen para crear infraestructura tecnológica que representa una fuente de innovación dentro de la industria médica.

Citas

Aceto, G., Persico, V., & Pescapé, A. (2020), Industry 4.0 and Health: Internet of Things, Big Data, and Cloud Computing for Healthcare 4.0. Journal of Industrial Information Integration, 18, 100129.

Angermueller, C., Pärnamaa, T., Parts, L., & Stegle, O. (2016). Deep learning for computational biology. Molecular Systems Biology, 12(7). https://doi.org/10.15252/msb.20156651

Berner, E. S. (2009). Clinical decision support systems: state of the art. AHRQ publication, 90069, 1-26.

Boutros-Saikali, N., Saikali, K., & Abou Naoum, R. (2018). An IoMT platform to simplify the development of healthcare monitoring applications [Conferencia]. 2018 Third International Conference on Electrical and Biomedical Engineering, Clean Energy and Green Computing (EBECEGC), Beirut, Lebanon.

Cook, D. (2010). Improving drug safety using computational biology. IDrugs, 13(2), 85-89.

Dinevski, D., Bele, U., Šarenac, T., Rajkovi?, U., & Šušterši?, O. (2013). Clinical decision support systems. En G. Graschew & S. Rakowsky (Eds.), Telemedicine Techniques and Applications (pp. 217–238). IntechOpen.

Esposito, C., De Santis, A., Tortora, G., Chang, H., & Choo, K. R. (2018). Blockchain: A panacea for healthcare cloud-based data security and privacy? IEEE Cloud Computing, 5(1), 31–37. https://doi.org/10.1109/MCC.2018.011791712

França, R. P., Iano, Y., Monteiro, A. C. B., & Arthur, R. (2021). A Methodology for Improving Efficiency in Data Transmission in Healthcare Systems. En C. Chakraborty, A. Banerjee, M. Kolekar, L. Garg & B. Chakraborty (eds.), Internet of Things for Healthcare Technologies (Vol. 73, pp 49–70). Springer.

GDHM. (2023). State of Digital Health around the world today. Página web oficial de The Global Digital Health Monitor. https://globalhealth.org/?gad_source=1&gclid=Cj0KCQjwgrO4BhC2ARIsAKQ7zUmcRTFjrgoqZtqP3q5MOvrKJSz4wj9uY7RgTftrtJ-Ez_HJVX06PxgaAv5vEALw_wcB

Gorry, G. A., & Barnett, G. O. (1968). Experience with a model of sequential diagnosis. Computers and Biomedical Research, 1(5), 490–507.

Greeshma, K. V., & Viji Gripsy, J. (2021). A Review on Classification and Retrieval of Biomedical Images Using Artificial Intelligence. En P. Siarry, M. Jabbar, R. Aluvalu, A. Abraham & A. Madureira (eds.), The Fusion of Internet of Things, Artificial Intelligence, and Cloud Computing in Health Care (pp. 47–66). Springer.

Holzinger, A. (2019). Introduction to Machine Learning & Knowledge Extraction (MAKE). Machine Learning and Knowledge Extraction, 1(1), 1-20.

IMCO. (2022). Índice de competitividad internacional 2022. Página web oficial del Centro de Investigación en Política Publica. https://imco.org.mx/indice-de-competitividad-internacional-2022/

Josefiok, M., Krahn, T., & Sauer, J. (2015). A Survey on Expert Systems for Diagnosis Support in the Field of Neurology. En R. Neves-Silva, L. C. Jain & R. Howlett (eds.), Intelligent Decision Technologies: Proceedings of the 7th KES International Conference on Intelligent Decision Technologies (KES-IDT 2015) (Vol. 39, pp. 291–300). Springer.

Gao, J., Jiang, Q., Zhou, B., & Chen, D. (2019) Convolutional neural networks for computer-aided detection or diagnosis in medical image analysis: An overview. Mathematical Biosciences and Engineering, 16(6), 6536-6561.

Khrisna Akbar, H. (2014). Risk management framework with COBIT 5 and risk management framework for cloud computing integration [Conferencia]. 2014 International Conference of Advanced Informatics: Concept, Theory and Application (ICAICTA), Bandung, Indonesia. https://ieeexplore.ieee.org/document/7005923

Ledley, R. S., & Lusted, L. B. (1959). Reasoning foundations of medical diagnosis. Science, 130(3366), 9–21.

Liu, H., Uren, V., Song, D., & Rüger, S. (2009). A four-factor user interaction model for content-based image retrieval. En L. Azzopardi, G. Kazai, S. Robertson, S. Rüger, M. Shokouhi, D. Song & E. Yilmaz (Eds.), Advances in Information Retrieval: Theory Second International Conference on the Theory of Information Retrieval, ICTIR 2009 Cambridge, UK, September 10-12, 2009 Proceedings (Vol. 5766, pp. 297-304). Springer.

Miller, R. A. (2009). Computer-assisted diagnostic decision support: history, challenges, and possible paths forward. Advances in Health Sciences Education, 14(1), 89–106.

Tripathi, N., & Reed, J. H. (2014). Cellular Communications A comprehensive and Practical Guide. Wiley-IEEE Press.

Osorio, E. A., Osorio, J. D., & García, D. A. (2019). SafeWalk App, mobile application in health for people living in motor disabilities. IOP Conference Series. IOP Conference Series: Materials Science and Engineering, 519, 012019. https://iopscience.iop.org/article/10.1088/1757-899X/519/1/012019

Ozcelik, N., K?vrak, M., Kotan, A., & Selimo?lu, ?. (2024). Lung Cancer Detection Based on Computed Tomography Image Using Convolutional Neural Networks. Technology and Health Care, 32(3), 1795-1805.

Saibene, A., Assale, M., & Giltri, M. (2021). Expert systems: Definitions, advantages, and issues in medical field applications. Expert Systems with Applications, 177, 114900.

Seemann, M. D., Nekolla, S., Ziegler, S., Bengel, F., & Schwaiger, M. (2004). PET/CT: Fundamental principles. European Journal of Medical Research, 9(5), 241-246.

Shahi, A., Chakraborty C., Ghosh, S., & Anand A. (2023) Application of Deep Learning Healthcare. En H. K. Deva Sarma, V. Piuri & A. Kumar Pujari (Eds.), Machine Learning in Information and Communication Technology Proceedings of ICICT 2021, SMIT (Vol. 498, pp. 131–140). Springer.

Shaw, T., McGregor, D., Brunner, M., Keep, M., Janssen, A., & Barnet, S. (2017) What is eHealth (6)? Development of a Conceptual Model for eHealth: Qualitative Study with Key Informants. Journal of Medical Internet Research,19(10), e324.

Sobnath, D. D., Nada, P., Kayyali, R., Nabhani-Gebara, S., Pierscionek, B., Vaes, A. W., & Kaimakamis, E. (2017). Features of a mobile support app for patients with chronic obstructive pulmonary disease: Literature review and current applications. JMIR mHealth and uHealth, 5(2). https://mhealth.jmir.org/2017/2/e17/

Syed, M. A., Majid, M., Qayyum, A., Awais, M., Alnowami M., & Khan, M. K. (2019). Medical image analysis using convolutional neural networks: A review. (226).

Tahir, A., Chen, F., Khan, H. U., Ming, Z., Ahmad, A., Nazir, S., & Shafiq, M. (2020), A Systematic Review on Cloud Storage Mechanisms Concerning e-Healthcare Systems. Sensors, 20(18), 5392.

Sait, U., Shivakumar, S., Lal, K. V., Kumar T., Ravishankar, V. D., & Bhalla, K. (2019). A Mobile Application for Early Diagnosis of Pneumonia in the Rural context [Conferencia]. , Seattle, Estados Unidos de América.

Zanjal, S. V., & Talmale, G. R. (2016). Medicine reminder and monitoring system for secure health using IOT. Procedia Computer Science, 78(3), 471–476.

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El autor de correspodencia se identifica con el siguiente símbolo: *

Publicado

17-10-2024

Cómo citar

Duran Granados, E., & Vicario Solórzano, C. M. (2024). Potencial de la tecnología en la medicina: retos y oportunidades para México. Transdigital, 5(10), e353. https://doi.org/10.56162/transdigital353

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