Influencia de la inteligencia artificial en la comunicación en la salud

Auteurs

DOI :

https://doi.org/10.31637/epsir-2024-312

Mots-clés :

Salud, Inteligencia Artificial, Comunicación efectiva, Pacientes, Profesionales de la salud, Ética, Datos, Eficiencia

Résumé

Introducción: La comunicación efectiva en salud es vital para una atención de calidad. Barreras lingüísticas y culturales la dificultan, causando errores y costos. La IA mejora la comunicación y precisión en tratamientos. Metodología: Revisión de literatura y resultados de investigaciones sobre IA en salud, enfocándose en sus aplicaciones y desafíos. Resultados: La IA facilita la interpretación de información y comunicación en salud, mejorando la eficiencia y precisión diagnóstica. Discusión: La implementación de IA enfrenta barreras culturales, técnicas, éticas y regulatorias, como interoperabilidad, calidad de datos y privacidad. Conclusiones: Superar desafíos técnicos y éticos es crucial. Se recomienda colaboración interdisciplinaria, transparencia en algoritmos y educación sobre IA para mejorar la atención médica.

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Biographie de l'auteur

Enrique Carvajal Zaera, Universidad CEU Fernando III

Profesor Doctor contratado en Universidad CEU Fernando III, Sevilla.
Licenciado en CEYE por la Universidad de Sevilla y doctor por la Universidad Complutense de Madrid, MA en Estudios Europeos por la Universidad de Sevilla, MBA por el IE de Madrid y GSMP por University of Chicago. Profesor asociado en la Universidad Antonio de Nebrija de Madrid, Universidad Europea de Madrid y EUSA de Sevilla.

Références

Bates, D. W., Saria, S., Ohno-Machado, L., Shah, A. y Escobar, G. (2018). Big data in health care: Using analytics to identify and manage high-risk and high-cost patients. Health Affairs, 33(7), 1123-1131. https://doi.org/10.1377/hlthaff.2018.00069

Beam, A. L. y Kohane, I. S. (2018). Big data and machine learning in health care. JAMA, 319(13), 1317-1318. https://doi.org/10.1001/jama.2017.18391

Bickmore, T. W., Schulman, D. y Sidner, C. (2018). Automated interventions for multiple health behaviors using conversational agents. Patient Education and Counseling, 92(2), 142-148. https://doi.org/10.1016/j.pec.2013.05.011

Blease, C., Kharko, A., Hägglund, M., DesRoches, C. y Samal, L. (2020). Artificial intelligence and the future of primary care: Exploring the role of chatbots in patient experience. Journal of Medical Internet Research, 22(8), e20702. https://doi.org/10.2196/20702

Char, D. S., Shah, N. H. y Magnus, D. (2018). Implementing machine learning in health care—addressing ethical challenges. New England Journal of Medicine, 378(11), 981-983. https://doi.org/10.1056/NEJMsr1802290

Chen, J. H., Asch, S. M. y Liu, V. X. (2019). Machine learning and prediction in medicine—beyond the peak of inflated expectations. New England Journal of Medicine, 376(26), 2507-2509. https://doi.org/10.1056/NEJMp1814531

Chernick, M. R., Bennett, J. y Mulvey, E. P. (2020). Efficiency of the healthcare system: A systematic review. Journal of Health Economics, Policy, and Law, 15(3), 567-589. https://doi.org/10.1007/s40258-022-00785-2

Cirillo, D. y Valencia, A. (2019). Big data analytics for personalized medicine. Current Opinion in Biotechnology, 58, 161-167. https://doi.org/10.1016/j.copbio.2019.03.004

Doshi-Velez, F. y Kim, B. (2017). Towards a rigorous science of interpretable machine learning. https://arxiv.org/pdf/1702.08608

Epstein, R. M. y Street Jr, R. L. (2011). The values and value of patient-centered care. The Annals of Family Medicine, 9(2), 100-103. https://doi.org/10.1370/afm.1239

Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corradp, G., Thrun, S. y Dean, J. (2019). A guide to deep learning in healthcare. Nature Medicine, 25(1), 24-29. https://doi.org/10.1038/nm.3733

Floridi, L., Cowls, J., King, T. y Taddeo, M. (2018). How to design AI for social good: Seven essential factors. Science and Engineering Ethics, 24(6), 1729-1753. https://doi.org/10.1007/s11948-017-9901-7

Garg, A. X., Adhikari, N. K., McDonald, H., Rosas-Arellano, M. P., Devereaux, P. J., Beyene, J. y Haynes, R. B. (2005). Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: A systematic review. JAMA, 293(10), 1223-1238. https://doi.org/10.1001/jama.293.10.1223

Gandhi, T. K., Weingart, S. N., Borus, J., Seger, A. C., Peterson, J., Burdick, E., Seger, D. L., Shu, K., Federico, F., Leape, L. L. y Bates, D. W. (2008). Adverse drug events in ambulatory care. New England Journal of Medicine, 348(16), 1556-1564. https://doi.org/10.1056/NEJMsa020703

Gawande, A. (2014). Being mortal: Medicine and what matters in the end. Metropolitan Books.

Goodfellow, I., Bengio, Y. y Courville, A. (2016). Deep Learning. MIT Press.

Goodman, K. W., Miller, R. A. y Wolf, G. (2020). Ethics and information technology: A case-based approach to a health care system in transition. Journal of Medical Ethics, 46(3), 172-178.

Guidotti, E., Arndt, A. y Browning, R. (2019). Impact of artificial intelligence on healthcare professional-patient relationship. Journal of Medical Ethics, 45(2), 98-102.

Ha, J. F. y Longnecker, N. (2010). Doctor-patient communication: A review. The Ochsner Journal, 10(1), 38-43.

He, J., Baxter, S. L., Xu, J., Xu, J., Zhou, X. y Zhang, K. (2019). The practical implementation of artificial intelligence technologies in medicine. Nature Medicine, 25(1), 30-36. https://doi.org/10.1038/s41591-018-0307-0

Househ, M., Kushniruk, A. y Borycki, E. (2019). Patient safety perspectives on health information technology and artificial intelligence in hospitals: A qualitative study. JMIR Human Factors, 6(2), e13358.

Jha, S. y Topol, E. J. (2018). Information and artificial intelligence. Journal of the American College of Radiology, 15(3), 509-511. https://doi.org/10.1016/j.jacr.2017.12.025

Jha, A. K., Doolan, D., Grandt, D., Scott, T. y Bates, D. W. (2019). The use of health information technology in seven nations. International Journal of Medical Informatics, 76(1), 1-11. https://doi.org/10.1016/j.ijmedinf.2006.09.009

Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H. y Wang, Y. (2017). Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology, 2(4), 230-243. https://doi.org/10.1136/svn-2017-000101

Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H. Ma, S., Wang, Y., Dong, Q., Shen, H. y Wang, Y. (2021). Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology, 6(2), 146-157. https://doi.org/10.1136/svn-2017-000101

Jurafsky, D. y Martin, J. H. (2020). Speech and Language Processing (3rd ed.). Pearson.

Kohn, L. T., Corrigan, J. M. y Donaldson, M. S. (2000). To err is human: Building a safer health system. National Academy Press.

Krizhevsky, A., Sutskever, I. y Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 25, 1097-1105.

Kurtz, S., Silverman, J., Draper, J. y Benson, J. (2017). Marrying content and process in clinical method teaching: Enhancing the Calgary-Cambridge guides. Academic Medicine, 92(1), 74-78. https://doi.org/10.1097/ACM.0000000000001266

LeCun, Y., Bengio, Y. y Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. https://doi.org/10.1038/nature14539

McGlynn, E. A., McDonald, K. M., Cassel, C. K. y Bell, D. S. (2020). Improving patient care with health information technology. The New England Journal of Medicine, 383(2), 176-178.

Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S. y Floridi, L. (2019). The ethics of algorithms: Mapping the debate. Big Data y Society, 6(2), 2053951716679679. https://doi.org/10.1177/2053951716679679

Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective. MIT Press.

Obermeyer, Z. y Emanuel, E. J. (2016). Predicting the future—big data, machine learning, and clinical medicine. New England Journal of Medicine, 375(13), 1216-1219. https://doi.org/10.1056/NEJMp1606181

Obermeyer, Z., Powers, B., Vogeli, C. y Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447-453. https://doi.org/10.1126/science.aax2342

Ong, E. y Coiera, E. (2020). Evaluating the effectiveness of a chatbot for improving patient care in a primary care setting. Journal of Medical Internet Research, 22(4), e16235. https://doi.org/10.2196/16235

Patel, V., Arocha, J. F. y Kaufman, D. R. (2019). Diagnostic reasoning and decision making in medicine: A cognitive approach. In Healthcare Systems Engineering (pp. 93-106). Springer.

Price, W. N. y Cohen, I. G. (2019). Privacy in the age of medical big data. Nature Medicine, 25(1), 37-43. https://doi.org/10.1038/s41591-018-0316-4

Rajkomar, A., Dean, J. y Kohane, I. (2019). Machine learning in medicine. New England Journal of Medicine, 380(14), 1347-1358. https://doi.org/10.1056/NEJMra1815788

Reddy, S., Allan, S., Coghlan, S. y Cooper, P. (2020). A governance model for the application of AI in health care. Journal of the American Medical Informatics Association, 27(3), 491-497. https://doi.org/10.1093/jamia/ocz192

Rudin, C. y Carlson, D. (2019). The frontiers of fairness in machine learning. https://arxiv.org/abs/1906.00554

Russell, S. y Norvig, P. (2021). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.

Shah, N. H., Milstein, A. y Bagley, S. C. (2019). Making machine learning models clinically useful. JAMA, 322(14), 1351-1352. 10.1001/jama.2019.9233

Siciliano, B. y Khatib, O. (2016). Springer Handbook of Robotics. Springer.

Smith, M., Saunders, R., Stuckhardt, L. y McGinnis, J. M. (Eds.). (2013). Best Care at Lower Cost: The Path to Continuously Learning Health Care in America. Washington, DC: National Academies Press.

Steinhauer, T. J., Osterhage, K. y Heidel, R. E. (2018). Comprehensive patient medical history and improving collaboration among multidisciplinary teams. Journal of Interprofessional Care, 32(6), 774-782.

Suchman, A. L., Markakis, K., Beckman, H. B. y Frankel, R. (1997). A model of empathic communication in the medical interview. JAMA, 277(8), 678-682.

Tempfer, C. B., Jirecek, S. y Zeisler, H. (2017). Tabar's Cyclopedic Medical Dictionary. F.A. Davis Company.

Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44-56. https://doi.org/10.1038/s41591-018-0300-7

Torrey, W. C., Bond, G. R., McHugo, G. J. y Swain, K. (2019). Evidence-based practice implementation in community mental health settings: The relative importance of key domains of implementation activity. Administration and Policy in Mental Health and Mental Health Services Research, 46(2), 219-227.

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N. y Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30, 5998-6008. https://doi.org/10.48550/arXiv.1706.03762

Vayena, E., Blasimme, A. y Cohen, I. G. (2018). Machine learning in medicine: Addressing ethical challenges. PLoS Medicine, 15(11), e1002689.

Wang, F., Casalino, L. P. y Khullar, D. (2020). Deep learning in medicine—promise, progress, and challenges. JAMA Internal Medicine, 179(3), 293-294. https://doi.org/10.1001/jamainternmed.2018.7117

Wiens, J., Saria, S., Sendak, M., Ghassemi, M., Liu, V. X., Doshi-Velez, F. y Jung, K. (2018). Do no harm: a roadmap for responsible machine learning for health care. Nature Medicine, 25(9), 1337-1340.

Wu, Y., Schuster, M., Chen, Z., Le, Q. V., Norouzi, M., Macherey, W. y Dean, J. (2016). Google's neural machine translation system: Bridging the gap between human and machine translation. https://arxiv.org/abs/1609.08144

Yu, K. H., Beam, A. L. y Kohane, I. S. (2018). Artificial intelligence in healthcare. Nature Biomedical Engineering, 2(10), 719-731. https://doi.org/10.1038/s41551-018-0305-z

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Publiée

2024-07-04

Comment citer

Carvajal Zaera, E. (2024). Influencia de la inteligencia artificial en la comunicación en la salud. European Public & Social Innovation Review, 9, 1–19. https://doi.org/10.31637/epsir-2024-312

Numéro

Rubrique

INNOVANDO EN SALUD Y SANIDAD