Gestión de Salud a través de la Inteligencia Artificial

Yucelin Ramirez Soto, Ybelisse Romero-Méndez, Ramphy Rojas Hernández

Resumen


Introducción: la gestión en salud desde los modelos tradicionales ha sido revolucionada por la incorporación de la Inteligencia Artificial (IA). Para el año 2020 se estimaba que los conocimientos médicos se duplicarían cada 73 días, por lo que un profesional debería dedicar más de 24 horas para aprender nuevos conocimientos y mantenerse actualizado. Gracias a las llamadas tecnologías de la información y comunicación (TIC), ahora el acceso y gestión de la información se ha optimizado. Objetivos: exponer la relevancia de la IA en el campo de la Salud, así como definir las bases teóricas de este tipo de tecnología que es capaz de apoyar en la toma de decisiones clínicas, analizar datos y optimizar los procesos tanto gerenciales como administrativos, de investigación y práctica profesional, con niveles de precisión extraordinarios. Metodología: se practicó una revisión documental con análisis cualitativo a través de la teoría fundamentada, a fin detectar las relaciones emergentes entre la IA y gestión en salud. Conclusiones: la integración de la IA en el campo de la salud constituye una adición innovadora que ha cambiado la forma de atención al paciente con diagnósticos más precisos, cirugías más seguras y tratamientos predictivos. Para el logro de la eficacia y completa implementación de la IA en la salud, se requiere de la participación del profesional humano, en su desarrollo y validación, siendo ellos los protagonistas de la transformación de los procesos de gestión y atención, para garantizar la adopción de la IA en las diferentes especialidades y organizaciones de salud.

 


Palabras clave


salud; gestión; inteligencia artificial; aprendizaje profundo.

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Referencias


Asan, O., Bayrak, A. E., & Choudhury, A. (2020). Artificial Intelligence and Human Trust in Healthcare: Focus on Clinicians. Journal of Medical Internet Research, 22(6), e15154. https://doi.org/10.2196/15154

Atanasova, A., Marinova, N., & Iliev, K. (2022). Interaction Between Types Of Artificial Intelligence. Scientific Research and Education in the Air Force, 35–41. https://doi.org/10.19062/2247-3173.2022.23.4

ArcGIS Pro (2020). Arquitectura de los Principales Modelos de Aprendizaje Profundo Documento en línea, extraído desde: https://pro.arcgis.com/es/pro-app/latest/tool-reference/image-analyst/overview-of-the-deep-learning-models.htm

Bohr, A., & Memarzadeh, K. (2020). The rise of artificial intelligence in healthcare applications. En A. Bohr & K. Memarzadeh (Eds.), Artificial Intelligence in Healthcare (pp. 25–60). Elsevier. https://doi.org/10.1016/B978-0-12-818438-7.00002-2

Budhwar, P., Chowdhury, S., Wood, G., Aguinis, H., Bamber, G. J., Beltran, J. R., Boselie, P., Lee Cooke, F., Decker, S., DeNisi, A., Dey, P. K., Guest, D., Knoblich, A. J., Malik, A., Paauwe, J., Papagiannidis, S., Patel, C., Pereira, V., Ren, S., … Varma, A. (2023). Human resource management in the age of generative artificial intelligence: Perspectives and research directions on ChatGPT. Human Resource Management Journal, 33(3), 606–659. https://doi.org/10.1111/1748-8583.12524

Carolan, J. E., McGonigle, J., Dennis, A., Lorgelly, P., & Banerjee, A. (2022). Technology-Enabled, Evidence-Driven, and Patient-Centered: The Way Forward for Regulating Software as a Medical Device. JMIR Medical Informatics, 10(1), e34038. https://doi.org/10.2196/34038

Chalasani, S. H., Syed, J., Ramesh, M., Patil, V., & Pramod Kumar, T. M. (2023). Artificial intelligence in the field of pharmacy practice: A literature review. Exploratory Research in Clinical and Social Pharmacy, 12, 100346. https://doi.org/10.1016/j.rcsop.2023.100346

Charow, R., Jeyakumar, T., Younus, S., Dolatabadi, E., Salhia, M., Al-Mouaswas, D., Anderson, M., Balakumar, S., Clare, M., Dhalla, A., Gillan, C., Haghzare, S., Jackson, E., Lalani, N., Mattson, J., Peteanu, W., Tripp, T., Waldorf, J., Williams, S., … Wiljer, D. (2021). Artificial Intelligence Education Programs for Health Care Professionals: Scoping Review. JMIR Medical Education, 7(4), e31043. https://doi.org/10.2196/31043

Davenport, T., & Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future Healthcare Journal, 6(2), 94–98. https://doi.org/10.7861/futurehosp.6-2-94

DeGroat, W., Abdelhalim, H., Patel, K., Mendhe, D., Zeeshan, S., & Ahmed, Z. (2024). Discovering biomarkers associated and predicting cardiovascular disease with high accuracy using a novel nexus of machine learning techniques for precision medicine. Scientific Reports, 14(1), 1. https://doi.org/10.1038/s41598-023-50600-8

Durkin, K. (2019). Artificial Intelligence-driven Smart Healthcare Services, Wearable Medical Devices, and Body Sensor Networks. American Journal of Medical Research, 6(2), 37.

Fjelland, R. (2020). Why general artificial intelligence will not be realized. Humanities and Social Sciences Communications, 7(1), 10. https://doi.org/10.1057/s41599-020-0494-4

Food and Drugs Administration (FDA). (2019). Proposed regulatory framework for modifications to artificial intelligence/machine learning (AI/ML)-based Software as a Medical Device (SaMD).

Grudin, J. (2019). Anticipating the Future of HCI by Understanding Its Past and Present. Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems, 1–4. https://doi.org/10.1145/3290607.3298806

Habehh, H., & Gohel, S. (2021). Machine Learning in Healthcare. Current Genomics, 22(4), 291–300. https://doi.org/10.2174/1389202922666210705124359

He, X., Liu, X., Zuo, F., Shi, H., & Jing, J. (2023). Artificial intelligence-based multi-omics analysis fuels cancer precision medicine. Seminars in Cancer Biology, 88, 187–200. https://doi.org/10.1016/j.semcancer.2022.12.009

Heinrich, K., Zschech, P., Janiesch, C., & Bonin, M. (2021). Process data properties matter: Introducing gated convolutional neural networks (GCNN) and key-value-predict attention networks (KVP) for next event prediction with deep learning. Decision Support Systems, 143, 113494. https://doi.org/10.1016/j.dss.2021.113494

Hussain, A., Malik, A., Halim, M. U., & Ali, A. M. (2014). The use of robotics in surgery: a review. International Journal of Clinical Practice, 68(11), 1376–1382. https://doi.org/10.1111/ijcp.12492

Janiesch, C., Zschech, P., & Heinrich, K. (2021). Machine learning and deep learning. Electronic Markets, 31(3), 685–695. https://doi.org/10.1007/s12525-021-00475-2

Johnson, K. B., Wei, W., Weeraratne, D., Frisse, M. E., Misulis, K., Rhee, K., Zhao, J., & Snowdon, J. L. (2021). Precision Medicine, AI, and the Future of Personalized Health Care. Clinical and Translational Science, 14(1), 86–93. https://doi.org/10.1111/cts.12884

Johri, A. (2020). Artificial intelligence and engineering education. Journal of Engineering Education, 109(3), 358–361. https://doi.org/10.1002/jee.20326

Kaliki, S., Vempuluru, V., Ghose, N., Patil, G., Viriyala, R., & Dhara, K. (2023). Artificial intelligence and machine learning in ocular oncology: Retinoblastoma. Indian Journal of Ophthalmology, 71(2), 424. https://doi.org/10.4103/ijo.IJO_1393_22

Kelleher (2016) Deep learning. Ediciones: The MIT Press (2016)

Khalifa, N. E. M., Taha, M. H. N., Ezzat Ali, D., Slowik, A., & Hassanien, A. E. (2020). Artificial Intelligence Technique for Gene Expression by Tumor RNA-Seq Data: A Novel Optimized Deep Learning Approach. IEEE Access, 8, 22874–22883. https://doi.org/10.1109/ACCESS.2020.2970210

Kogan, E., Didden, E.-M., Lee, E., Nnewihe, A., Stamatiadis, D., Mataraso, S., Quinn, D., Rosenberg, D., Chehoud, C., & Bridges, C. (2023). A machine learning approach to identifying patients with pulmonary hypertension using real-world electronic health records. International Journal of Cardiology, 374, 95–99. https://doi.org/10.1016/j.ijcard.2022.12.016

Koumakis, L. (2020). Deep learning models in genomics; are we there yet? Computational and Structural Biotechnology Journal, 18, 1466–1473. https://doi.org/10.1016/j.csbj.2020.06.017

Lee, D. (2019). Effects of key value co-creation elements in the healthcare system: focusing on technology applications. Service Business, 13(2), 389–417. https://doi.org/10.1007/s11628-018-00388-9

Lee, D., & Yoon, S. N. (2021). Application of Artificial Intelligence-Based Technologies in the Healthcare Industry: Opportunities and Challenges. International Journal of Environmental Research and Public Health, 18(1), 271. https://doi.org/10.3390/ijerph18010271

Lee, S. M., & Lee, D. (2020). Healthcare wearable devices: an analysis of key factors for continuous use intention. Service Business, 14(4), 503–531. https://doi.org/10.1007/s11628-020-00428-3

Lee, S.-I., Celik, S., Logsdon, B. A., Lundberg, S. M., Martins, T. J., Oehler, V. G., Estey, E. H., Miller, C. P., Chien, S., Dai, J., Saxena, A., Blau, C. A., & Becker, P. S. (2018). A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia. Nature Communications, 9(1), 42. https://doi.org/10.1038/s41467-017-02465-5

Leijnen, S., & Veen, F. van. (2020). The Neural Network Zoo. Proceedings, 47(1), 9. https://doi.org/10.3390/proceedings47010009

Lindemann, B., Müller, T., Vietz, H., Jazdi, N., & Weyrich, M. (2021). A survey on long short-term memory networks for time series prediction. Procedia CIRP, 99, 650–655. https://doi.org/10.1016/j.procir.2021.03.088

London, D. (2023). Simply Artificial Inteligence . Perenguin Ramdom House. .

Ma, D., Dang, B., Li, S., Zang, H., & Dong, X. (2023). Implementation of computer vision technology based on artificial intelligence for medical image analysis. International Journal of Computer Science and Information Technology, 1(1), 69–76. https://doi.org/10.62051/ijcsit.v1n1.10

Mahiddin, N. B., Othman, Z. A., Bakar, A. A., & Rahim, N. A. A. (2022). An Interrelated Decision-Making Model for an Intelligent Decision Support System in Healthcare. IEEE Access, 10, 31660–31676. https://doi.org/10.1109/ACCESS.2022.3160725

Majumder, A., & Sen, D. (2021). Artificial intelligence in cancer diagnostics and therapy: current perspectives. Indian Journal of Cancer, 58(4), 481–492. https://doi.org/10.4103/ijc.IJC_399_20

Mathur, P., Srivastava, S., Xu, X., & Mehta, J. L. (2020). Artificial Intelligence, Machine Learning, and Cardiovascular Disease. Clinical Medicine Insights: Cardiology, 14, 117954682092740. https://doi.org/10.1177/1179546820927404

McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (2006). A Proposal for The Dartmouth Summer Research Project on Artificial Intelligence. AI Magazine, 27(4).

Mindsky, M. (1967). Semantic Information Processing. (M. Mindsky, Ed.). The MIT Press.

Nageeta, F., Waqar, F., Allahi, I., Murtaza, F., Nasir, M., Danesh, F., Irshad, B., Kumar, R., Tayyab, A., Khan, M. S. M., Kumar, S., Varrassi, G., Khatri, M., Muzammil, M. A., & Mohamad, T. (2023). Precision Medicine Approaches to Diabetic Kidney Disease: Personalized Interventions on the Horizon. Cureus, 15(9), e45575. https://doi.org/10.7759/cureus.45575

Olveres, J., González, G., Torres, F., Moreno-Tagle, J. C., Carbajal-Degante, E., Valencia-Rodríguez, A., Méndez-Sánchez, N., & Escalante-Ramírez, B. (2021). What is new in computer vision and artificial intelligence in medical image analysis applications. Quantitative Imaging in Medicine and Surgery, 11(8), 3830–3853. https://doi.org/10.21037/qims-20-1151

Paranjape, K., Schinkel, M., & Nanayakkara, P. (2020). Short Keynote Paper: Mainstreaming Personalized Healthcare-Transforming Healthcare through new era of Artificial Intelligence. IEEE Journal of Biomedical and Health Informatics, 24(7), 1–1. https://doi.org/10.1109/JBHI.2020.2970807

Qian, J., Song, Z., Yao, Y., Zhu, Z., & Zhang, X. (2022). A review on autoencoder based representation learning for fault detection and diagnosis in industrial processes. Chemometrics and Intelligent Laboratory Systems, 231, 104–711. https://doi.org/10.1016/j.chemolab.2022.104711

Raza, M. A., Aziz, S., Noreen, M., Saeed, A., Anjum, I., Ahmed, M., & Raza, S. M. (2022). Artificial Intelligence (AI) in Pharmacy: An Overview of Innovations. Innovations in pharmacy, 13(2). https://doi.org/10.24926/iip.v13i2.4839

Rego Rodríguez, F. A., Germán Flores, L., & Vitón-Castillo, A. A. (2022). Artificial intelligence and machine learning: present and future applications in health sciences. Seminars in Medical Writing and Education, 1, 9. https://doi.org/10.56294/mw20229

Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206–215. https://doi.org/10.1038/s42256-019-0048-x

Sapci, A. H., & Sapci, H. A. (2020). Artificial Intelligence Education and Tools for Medical and Health Informatics Students: Systematic Review. JMIR Medical Education, 6(1), e19285. https://doi.org/10.2196/19285

Secinaro, S., Calandra, D., Secinaro, A., Muthurangu, V., & Biancone, P. (2021). The role of artificial intelligence in healthcare: a structured literature review. BMC Medical Informatics and Decision Making, 21(1), 125. https://doi.org/10.1186/s12911-021-01488-9

Shi, T., Yang, Y., Huang, S., Chen, L., Kuang, Z., Heng, Y., & Mei, H. (2019). Molecular image-based convolutional neural network for the prediction of ADMET properties. Chemometrics and Intelligent Laboratory Systems, 194, 103853. https://doi.org/10.1016/j.chemolab.2019.103853

Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A., Chen, Y., Lillicrap, T., Hui, F., Sifre, L., van den Driessche, G., Graepel, T., & Hassabis, D. (2017). Mastering the game of Go without human knowledge. Nature, 550(7676), 354–359. https://doi.org/10.1038/nature24270

Stokes, J. M., Yang, K., Swanson, K., Jin, W., Cubillos-Ruiz, A., Donghia, N. M., MacNair, C. R., French, S., Carfrae, L. A., Bloom-Ackermann, Z., Tran, V. M., Chiappino-Pepe, A., Badran, A. H., Andrews, I. W., Chory, E. J., Church, G. M., Brown, E. D., Jaakkola, T. S., Barzilay, R., & Collins, J. J. (2020). A Deep Learning Approach to Antibiotic Discovery. Cell, 180(4), 688-702.e13. https://doi.org/10.1016/j.cell.2020.01.021

Suleimenov, I. E., Vitulyova, Y. S., Bakirov, A. S., & Gabrielyan, O. A. (2020). Artificial Intelligence. Proceedings of the 2020 6th International Conference on Computer and Technology Applications, 22–25. https://doi.org/10.1145/3397125.3397141

Turing, A. M. (1950). Computing Machinery and Intelligence. Oxford University Press on behalf of the Mind Association, 59(236), 433–460.

Wu, C., Lei, J., Zheng, Q., Zhao, W., Lin, W., Zhang, X., Zhou, X., Zhao, Z., Zhang, Y., Wang, Y., & Xie, W. (2023). Can GPT-4V(ision) Serve Medical Applications? Case Studies on GPT-4V for Multimodal Medical Diagnosis.

Zohuri, B., & Mossavar-Rahmani, F. (2023). Artificial General Intelligence (AGI) Unleashing The Power of Artificial General Intelligence: OpenAI’s Pursuit of Generative AI. Mod App Matrl Sci, 5(4).




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