Artículos de investigación

Revisión exhaustiva sobre la transformación de la atenciónsanitaria impulsada por la IA

Vol. 20 Núm. 2 (2024)
Publicado: 2024-10-15
Sivadi Balakrishna
Vijender Kumar Solanki

Introducción: en el panorama dinámico de la atención médica, la intersección entre tecnología de vanguardia y soluciones centradas en el paciente ha generado un cambio de paradigma, y a la vanguardia de esta ola transformadora se encuentra la Inteligencia Artificial (IA).

Problema: al centrarse en la integración multifacética de las tecnologías de IA, la narrativa explora su papel fundamental en la mejora de los resultados para los pacientes.

Objetivo: Este artículo se adentra en el ámbito dinámico de la transformación de la atención médica mediante la explotación estratégica de la IA.

Metodología: este documento explora el profundo impacto de la IA en la atención médica, examinando el estado actual del campo y visualizando su futuro. Desde algoritmos de aprendizaje automático para la detección temprana de enfermedades hasta planes de tratamiento personalizados, el documento profundiza en las diversas aplicaciones de la IA en la atención médica y el potencial que tiene para revolucionar todo el ecosistema. Desde estrategias de tratamiento personalizadas hasta procesos de atención médica optimizados, el documento desentraña las diversas formas en que la IA está remodelando el panorama de la atención médica.

Resultados: la necesidad de mejorar los resultados para los pacientes, caracterizada por una mayor eficiencia, precisión y atención personalizada, encuentra en la IA un aliado prometedor. El artículo ha mostrado cómo las tecnologías de IA, incluidos el aprendizaje automático y la analítica avanzada, ofrecen soluciones tangibles a desafíos de larga data en el diagnóstico, la planificación de tratamientos y la interacción con los pacientes.

Conclusiones: un examen exhaustivo de las tendencias emergentes ilumina el potencial de mejoras sustanciales en el bienestar de los pacientes facilitadas por la asociación sinérgica entre la IA y las prácticas de atención.

Originalidad: la exploración de las aplicaciones de la IA a lo largo de este artículo subraya su potencial transformador para reformar las prácticas tradicionales.

Limitaciones: la posibilidad de mejorar continuamente los resultados para los pacientes a través de tecnologías innovadoras sigue siendo una de las prioridades de esta transformación.

Palabras clave: Array, Array, Array, Array, Array, Array

Cómo citar

[1]
S. Balakrishna y V. Kumar Solanki, «Revisión exhaustiva sobre la transformación de la atenciónsanitaria impulsada por la IA», ing. Solidar, vol. 20, n.º 2, pp. 1–30, oct. 2024, doi: 10.16925/2357-6014.2024.02.07.

[1] F. Jiang, Y. Jiang, H. Zhi, Y. Dong, H. Li, S. Ma, Y. Wang, “Artificial intelligence in healthcare: past, present and future,” Stroke and vascular neurology, vol. 2, no. 4.

[2] E. Drysdale, E. Dolatabadi, C. Chivers, V. Liu, S. Saria, M. Sendak, M. Mazwi, Implementing AI in healthcare. In Vector-SickKids Health AI Deployment Symposium. Toronto.

[3] J. Amann, A. Blasimme, E. Vayena, D. Frey, V.I. Madai, Precise4Q Consortium, “Explainability for artificial intelligence in healthcare: a multidisciplinary perspective,” BMC medical informatics and decision making, 20, pp. 1-9.

[4] L. Christodoulou, AI Remote Vital Signs Monitoring and Diagnostics Based on Wireless Wearable Bio-sensors-Systems-Devices. 2021.

[5] M. Y. Shaheen, “Applications of Artificial Intelligence (AI) in healthcare: A review,” ScienceOpen Preprints (2021).

[6] J. Wiens, E.S. Shenoy, “Machine learning for healthcare: on the verge of a major shift in healthcare epidemiology,” Clinical infectious diseases, vol. 66, no. 1, pp. 149-153.

[7] A. Callahan, N.H. Shah, Machine learning in healthcare. In Key advances in clinical informatics (pp. 279-291). Academic Press.

[8] S. Khedkar, P. Gandhi, G. Shinde, V. Subramanian, Deep learning and explainable AI in healthcare using EHR. Deep learning techniques for biomedical and health informatics, 129-148.

[9] R. Miotto, F. Wang, S. Wang, X. Jiang, J.T. Dudley, „Deep learning for healthcare: review, opportunities, and challenges,” Briefings in Bioinformatics, vol. 19, no. 6, pp. 1236-1246.

[10] Y.W. Chen, L.C. Jain, Deep learning in healthcare. Paradigms and applications. Springer, Heidelberg.

[11] S.K. Pandey, R.R. Janghel, “Recent deep learning techniques, challenges and its applications for medical healthcare system: a review,” Neural Processing Letters, 50, 1907-1935.

[12] T. Osman Andersen, F. Nunes, L. Wilcox, E. Kaziunas, S. Matthiesen, F. Magrabi, Realizing AI in healthcare: challenges appearing in the wild. In Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems (pp. 1-5). 2021.

[13] A.S. Panayides, A. Amini, N.D. Filipovic, A. Sharma, S.A. Tsaftaris, A. Young, C.S. Pattichis, “AI in medical imaging informatics: current challenges and future directions,” IEEE journal of biomedical and health informatics, vol. 24, no. 7, pp. 1837-1857.

[14] L. Hafizović, A. Čaušević, A. Deumić, L.S. Bećirović, L.G. Pokvić, A. Badnjević, The Use of Artificial Intelligence in Diagnostic Medical Imaging: Systematic Literature Review. In 2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE) (pp. 1-6). IEEE.

[15] H. Zhang, J. Zheng, “The application analysis of medical chatbots and virtual assistants,” Front. Soc. Sci. Technol, vol. 3, 11-16.

[16] L. Palladino, B. Maris, A. Antonelli, P. Fiorini, Autonomy in robotic prostate biopsy through AI-assisted fusion. In 2021 20th International Conference on Advanced Robotics (ICAR) (pp. 142-147).

[17] J. Wang, L. Zhu, P. Yang, P. Li, J. Wang, H. Li, B. Sheng, „State-of-the-art: A taxonomy of artificial intelligence-assisted robotics for medical therapies and applications,” Global Translational Medicine, vol. 1, no. 2, p. 176.

[18] A. Qayyum, J. Qadir, M. Bilal, A. Al-Fuqaha, Secure and robust machine learning for healthcare: A survey. IEEE Reviews in Biomedical Engineering, 14, pp. 156-180.

[19] D. Saraswat, P. Bhattacharya, A. Verma, V.K. Prasad, S. Tanwar, G. Sharma, R. Sharma, Explainable AI for healthcare 5.0: opportunities and challenges. IEEE Access.

[20] D. Dave, H. Naik, S. Singhal, P. Patel, Explainable AI meets healthcare: A study on heart disease dataset. arXiv preprint arXiv:2011.03195.

[21] J. Gerlings, M.S. Jensen, A. Shollo, Explainable ai, but explainable to whom? An exploratory case study of xai in healthcare. Handbook of Artificial Intelligence in Healthcare: Vol 2: Practicalities and Prospects, 169-198. 2022.

[22] Q. Wang, M. Su, M. Zhang, R. Li, „Integrating digital technologies and public health to fight COVID-19 pandemic: key technologies, applications, challenges and outlook of digital healthcare,” International Journal of Environmental Research and Public Health, vol. 18, no. 11, 6053.

[23] T.Q. Sun, R. Medaglia, “Mapping the challenges of Artificial Intelligence in the public sector: Evidence from public healthcare,” Government Information Quarterly, vol. 36, no. 2, pp. 368-383.

[24] S. Venkatraman, R.P. Sundarraj, R. Seethamraju, “Exploring health-analytics adoption in Indian private healthcare organizations: An institutional-theoretic perspective,” Information and Organization, vol. 32, no.3, pp. 100430.

[25] M.H. Stanfill, D.T. Marc, “Health information management: implications of artificial intelligence on healthcare data and information management,” Yearbook of medical informatics, vol. 28, no. 01, pp. 056-064.

[26] C. Wang, S. Liu, H. Yang, J. Guo, Y. Wu, J. Liu, „Ethical considerations of using ChatGPT in health care,” Journal of Medical Internet Research, vol. 25, e48009.

[27] V. Ferrari, G. Klinker, F. Cutolo, “Augmented reality in healthcare,” Journal of Healthcare Engineering, 2019.

[28] J. Ara, F.B. Karim, M.S.A. Alsubaie, Y.A. Bhuiyan, M.I. Bhuiyan, S.B. Bhyan, H. Bhuiyan, “Comprehensive analysis of augmented reality technology in modern healthcare system,” International Journal of Advanced Computer Science and Applications, vol. 12, no. 6, pp. 840-849.

[29] M. Regona, T. Yigitcanlar, B. Xia, R.Y.M. Li, “Opportunities and adoption challenges of AI in the construction industry: a PRISMA review,” Journal of Open Innovation: Technology, Market, and Complexity, vol. 8, no. 1, p. 45.

[30] Y.K. Dwivedi, L. Hughes, E. Ismagilova, G. Aarts, C. Coombs, T. Crick, M.D. Williams, “Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice, and policy,” International Journal of Information Management, vol. 57, p. 101994.

[31] J. Guan, “Artificial intelligence in healthcare and medicine: promises, ethical challenges, and governance,” Chinese Medical Sciences Journal, vol. 34, no. 2, pp. 76-83.

[32] S. Balakrishna, M. Thirumaran, V.K. Solanki, “IoT sensor data integration in healthcare using semantics and machine learning approaches,” A handbook of internet of things in biomedical and cyber-physical system, pp. 275-300.

[33] S. Balakrishna, M. Thirumaran, Semantic interoperability in IoT and big data for health care: a collaborative approach. In Handbook of data science approaches for biomedical engineering, pp. 185-220. Academic Press.

[34] G.N. Sujini, S. Balakrishna, Machine Learning based Computer-Aided Diagnosis Models for Thyroid Nodule Detection and Classification: A Comprehensive Survey. In 2023 2nd International Conference on Automation, Computing and Renewable Systems (ICACRS), pp. 1283-1287. IEEE.

[35] N. Sujini Ganne, S. Balakrishna, Categorization of Thyroid Cancer Sonography Images Using an Amalgamation of Deep Learning Techniques. In International Conference on Soft Computing and Signal Processing, pp. 483-491.

[36] S. Balakrishna, Y. Gopi, “Comparative Analysis on Machine Learning Algorithms for Multiple Disease Prediction,” International Journal of Machine Learning and Networked Collaborative Engineering, vol. 5, no. 1, pp. 8–16.

MÉTRICAS
VISTAS DEL ARTÍCULO: 2125
VISTAS DEL PDF: 1549