Desarrollo Académico y Profesional en el Área de la Salud con Uso de IA

Academic and Professional Development in the Health Field Using AI

Contenido principal del artículo

Autores/as

El estudio evalúa el impacto de la inteligencia artificial (IA) en la formación académica y el desarrollo profesional en salud, abarcando educación médica y práctica clínica entre 2020–2024. Su alcance consideró investigaciones indexadas en bases como Scopus y Web of Science. Metodológicamente aplicó el marco PICOT para definir la pregunta y criterios, y el protocolo PRISMA para la búsqueda, selección, elegibilidad e inclusión; se emplearon descriptores y operadores booleanos, criterios de inclusión/exclusión y registro sistemático de metadatos. Los resultados muestran una adopción creciente de herramientas de IA para predicción, diagnóstico, monitorización y apoyo docente; evidencian ventajas en eficiencia, personalización y detección temprana, junto a desafíos técnicos, éticos y de confianza. Se concluye que la IA tiene potencial transformador en salud y educación, pero requiere mayor validación, regulación y formación para su integración segura y equitativa.

The study evaluates the impact of artificial intelligence (AI) on academic training and professional development in health, covering medical education and clinical practice between 2020–2024. Its scope considered research indexed in databases such as Scopus and Web of Science. Methodologically, it applied the PICOT framework to define the question and criteria, and the PRISMA protocol for search, selection, eligibility, and inclusion; descriptors and Boolean operators, inclusion/exclusion criteria, and systematic metadata registration were used. The results show a growing adoption of AI tools for prediction, diagnosis, monitoring, and teaching support; they demonstrate advantages in efficiency, personalization, and early detection, along with technical, ethical, and trust challenges. It is concluded that AI has transformative potential in health and education, but requires further validation, regulation, and training for its safe and equitable integration.

Detalles del artículo

Cómo citar
Acosta Linares, A. A., & Arce Villanueva, C. A. (2025). Desarrollo Académico y Profesional en el Área de la Salud con Uso de IA. Revista Tribunal, 5(13), 381-395. https://doi.org/10.59659/revistatribunal.v5i13.276
Sección
Artículos de Investigación

Cómo citar

Acosta Linares, A. A., & Arce Villanueva, C. A. (2025). Desarrollo Académico y Profesional en el Área de la Salud con Uso de IA. Revista Tribunal, 5(13), 381-395. https://doi.org/10.59659/revistatribunal.v5i13.276

Referencias

Abonamah, A. A., y Abdelhamid, N. (2024). Managerial insights for AI/ML implementation: a playbook for successful organizational integration. Discover Artificial Intelligence, 4(1), 22. https://doi.org/10.1007/s44163-023-00100-5

Bello, F. A., Kohler, J., Hinrechsen, K., Araya, V., Hidalgo, L., y Jara, J. L. (2020). Using machine learning methods to identify significant variables for the prediction of first-year Informatics Engineering students dropout. 2020 39th International Conference of the Chilean Computer Science Society (SCCC), 1–5. https://doi.org/10.1109/SCCC51225.2020.9281280

Boulif, A., Ananou, B., Ouladsine, M., y Delliaux, S. (2023). A Literature Review: ECG-Based Models for Arrhythmia Diagnosis Using Artificial Intelligence Techniques. Bioinformatics and Biology Insights, 17. SAGE Publications Inc. https://doi.org/10.1177/11779322221149600

Dudek, G., Sakowski, S., Brzezińska, O., Sarnik, J., Budlewski, T., Dragan, G., Poplawska, M., Poplawski, T., Bijak, M., y Makowska, J. (2024). Machine learning-based prediction of rheumatoid arthritis with development of ACPA autoantibodies in the presence of non-HLA genes polymorphisms. PLoS ONE, 19(3). https://doi.org/10.1371/journal.pone.0300717

Evans, C., Kay, W., Amici-Dargan, S., González, R. D. M., Donert, K., y Rutherford, S. (2024). Developing a scale to explore self-regulatory approaches to assessment and feedback with academics in higher education. Frontiers in Psychology, 15. https://doi.org/10.3389/fpsyg.2024.1357939

Figueroa, C., Ayala, A., Trejo, L. A., Ramos, B., Briz, C. L., Noriega, I., y Chávez, A. (2023). Measuring the Effectiveness of a Multicomponent Program to Manage Academic Stress through a Resilience to Stress Index. Sensors, 23(5), 2650. https://doi.org/10.3390/s23052650

Giorgini, F., Di Dalmazi, G., y Diciotti, S. (2023). Artificial intelligence in endocrinology: a comprehensive review. Journal of Endocrinological Investigation. Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/s40618-023-02235-9

Gonzalez-Nucamendi, A., Noguez, J., Neri, L., Robledo-Rella, V., y García-Castelán, R. M. G. (2023). Predictive analytics study to determine undergraduate students at risk of dropout. Frontiers in Education, 8. https://doi.org/10.3389/feduc.2023.1244686

Gray, K., Slavotinek, J., Dimaguila, G. L., y Choo, D. (2022). Artificial Intelligence Education for the Health Workforce: Expert Survey of Approaches and Needs. JMIR Medical Education, 8(2). https://doi.org/10.2196/35223

Griffiths, D., Frías-Martínez, E., Tlili, A., y Burgos, D. (2024). A Cybernetic Perspective on Generative AI in Education: From Transmission to Coordination. International Journal of Interactive Multimedia and Artificial Intelligence, 8(5), 15. https://doi.org/10.9781/ijimai.2024.02.008

Higgins, O., Short, B. L., Chalup, S. K., y Wilson, R. L. (2023). Artificial intelligence (AI) and machine learning (ML) based decision support systems in mental health: An integrative review. International Journal of Mental Health Nursing, 32(4), 966–978. https://doi.org/10.1111/inm.13114

Khalifa, M., Albadawy, M., y Iqbal, U. (2024). Advancing clinical decision support: The role of artificial intelligence across six domains. Computer Methods and Programs in Biomedicine Update, 5. https://doi.org/10.1016/j.cmpbup.2024.100142

King, S. M., Anas, S., Carnicer Hijazo, R., Jordaan, J., Potter, J. D. F., y Low-Beer, N. (2024). Twelve tips for designing and implementing an academic coaching program. Medical Teacher, 1–7. https://doi.org/10.1080/0142159X.2024.2308058

Lakshmi, D., y Hemanth, D. J. (2024). An Overview of Deepfake Methods in Medical Image Processing for Health Care Applications. https://doi.org/10.3233/faia231448

Ludlow, K., Westbrook, J., Jorgensen, M., Lind, K. E., Baysari, M. T., Gray, L. C., Day, R. O., Ratcliffe, J., Lord, S. R., Georgiou, A., Braithwaite, J., Raban, M. Z., Close, J., Beattie, E., Zheng, W. Y., Debono, D., Nguyen, A., Siette, J., Seaman, K., Haddock, R. (2021). Co-designing a dashboard of predictive analytics and decision support to drive care quality and client outcomes in aged care: a mixed-method study protocol. BMJ Open, 11(8), e048657. https://doi.org/10.1136/bmjopen-2021-048657

Majjate, H., Bellarhmouch, Y., Jeghal, A., Yahyaouy, A., Tairi, H., y Zidani, K. A. (2023). AI-Powered Academic Guidance and Counseling System Based on Student Profile and Interests. Applied System Innovation, 7(1), 6. https://doi.org/10.3390/asi7010006

Malakhov, V. V., Smyshlyaeva, L. G., Melentieva, A. N., y Okorokov, A. O. (2023). Use of big data in schoolchildren’s career guidance practices for the medical profession. Perspectives of Science and Education, 66(6), 516–531. https://doi.org/10.32744/pse.2023.6.30

Mayrath, M., Fontanez, D., Abdelbaset, F., Lenihan, B., y Lenihan, D. V. (2023). Increasing Diversity in the Physician Workforce: Pathway Programs and Predictive Analytics. Academic Medicine, 98(10), 1154–1158. https://doi.org/10.1097/ACM.0000000000005287

Morrow, E., Zidaru, T., Ross, F., Mason, C., Patel, K. D., Ream, M., y Stockley, R. (2023). Artificial intelligence technologies and compassion in healthcare: A systematic scoping review. Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.971044

Mpinga, E. K., Bukonda, N. K. Z., Qailouli, S., y Chastonay, P. (2022). Artificial Intelligence and Human Rights: ¿Are There Signs of an Emerging Discipline? A Systematic Review. Journal of Multidisciplinary Healthcare, 15, 235–246. https://doi.org/10.2147/JMDH.S315314

Of Science, I. J., Hachem Harouni Alaoui, Elkaber Hachem, Cherif Ziti, y Mustapha Bassiri. (2021). The Use of Predictive Analyzes for University Dropout Cases. Iraqi Journal of Science, 44–51. https://doi.org/10.24996/ijs.2021.SI.1.7

Omarov, B., Zhumanov, Z., Gumar, A., Kuntunova, L., Demirel, S., y Research, A. (n.d.). Artificial Intelligence Enabled Mobile Chatbot Psychologist using AIML and Cognitive Behavioral Therapy Academy of Logistics and Transport. IJACSA) International Journal of Advanced Computer Science and Applications, 14(6). www.ijacsa.thesai.org

Park, J., Feng, Y., y Jeong, S.-P. (2024). Developing an advanced prediction model for new employee turnover intention utilizing machine learning techniques. Scientific Reports, 14(1), 1221. https://doi.org/10.1038/s41598-023-50593-4

Pinsky, M. R., Bedoya, A., Bihorac, A., Celi, L., Churpek, M., Economou-Zavlanos, N. J., Elbers, P., Saria, S., Liu, V., Lyons, P. G., Shickel, B., Toral, P., Tscholl, D., y Clermont, G. (2024). Use of artificial intelligence in critical care: opportunities and obstacles. Critical Care, 28(1), 113. https://doi.org/10.1186/s13054-024-04860-z

Sideris, K., Weir, C. R., Schmalfuss, C., Hanson, H., Pipke, M., Tseng, P. H., Lewis, N., Sallam, K., Bozkurt, B., Hanff, T., Schofield, R., Larimer, K., Kyriakopoulos, C. P., Taleb, I., Brinker, L., Curry, T., Knecht, C., Butler, J. M., y Stehlik, J. (2024). Artificial intelligence predictive analytics in heart failure: results of the pilot phase of a pragmatic randomized clinical trial. Journal of the American Medical Informatics Association, 31(4), 919–928. https://doi.org/10.1093/jamia/ocae017

Teixeira, P. F., Battelino, T., Carlsson, A., Gudbjörnsdottir, S., Hannelius, U., von Herrath, M., Knip, M., Korsgren, O., Elding Larsson, H., Lindqvist, A., Ludvigsson, J., Lundgren, M., Nowak, C., Pettersson, P., Pociot, F., Sundberg, F., Åkesson, K., Lernmark, Å., y Forsander, G. (2024). Assisting the implementation of screening for type 1 diabetes by using artificial intelligence on publicly available data. Diabetologia. https://doi.org/10.1007/s00125-024-06089-5

Vázquez-Parra, J. C., Alcantar-Nieblas, C., Glasserman-Morales, L. D., y Nuñez-Rodríguez, X. (2023). Development of Social Entrepreneurship Competencies and Complex Thinking in an Intensive Course of Open Educational Innovation. International Journal of Educational Psychology, 13(1), 1–20. https://doi.org/10.17583/ijep.12187