Inteligencia artificial en medicina: panorama actual

Autores/as

  • Enrique Ruibal-Tavares Universidad de Sonora
  • Jesús R. Calleja-López Universidad de Sonora
  • Cristián N. Rivera-Rosas Universidad de Sonora
  • Luis J. Aguilera-Duarte Universidad de Sonora

DOI:

https://doi.org/10.59420/remus.10.2023.178

Palabras clave:

Inteligencia artificial, Diagnóstico, Tratamiento, Campo médico

Resumen

La inteligencia artificial (IA) tiene un potencial enorme para modernizar la práctica y educación de la medicina. Esta tecnología, basada en algoritmos con los cuales las máquinas son capaces de llevar a procesos de razonamiento de alta complejidad, hoy en día tiene aplicaciones en la atención médica que, a pesar de seguir en etapas tempranas, muestran mucho potencial para optimizar el ejercicio profesional de las ciencias médicas. Esto implica beneficios en métodos de prevención y diagnóstico de enfermedades, así como también en tratamientos novedosos y mejoras en los pronósticos para los pacientes. Se realizó una revisión bibliográfica con el objetivo de plantear el panorama general de las aplicaciones de la IA en medicina y su posible rol a futuro. En conclusión, es muy probable que este tipo de instrumentos formen parte del quehacer cotidiano de todo trabajador de la salud y, por consiguiente, es importante conocer sus límites y sus ventajas, buscando integrar de la mejor manera posible estas herramientas auxiliares en el campo médico.

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Citas

Avila-Tomás JF, Mayer-Pujadas MA, Que-sada-Varela VJ. La Inteligencia artificial y sus aplicaciones en medicina I: Intro-ducción Antecedentes a la IA y robótica. Atención Primaria. 2020; 52(10):778–84. https://doi.org/10.1016/j.aprim.2020.04.013

Yu K-H, Beam AL, Kohane IS. Artificial Intelligence in Healthcare. Nature Bio-medical Engineering. 2018; 2(10):719–31. https://doi.org/10.1038/s41551-018-0305-z

Murphy, K. P. y Bach F. Machine Learning: A Probabilistic Perspective (MIT Press, Cam-bridge, 2012).

Haleem A, Javaid M, Khan IH. Current Status and Applications of Artificial Intelligence (AI) in Medical Field: An Overview. Current Medi-cine Research and Practice. 2019; 9(6):231–7. https://doi.org/10.1016/j.cmrp.2019.11.005

Haleem A, Vaishya R, Javaid M, Khan IH. Artificial Intelligence (AI) Applica-tions in Orthopedics: An Innovative Tech-nology to Embrace. Journal of Clini-cal Orthopaedics and Trauma. 2020; 11. https://doi.org/10.1016/j.jcot.2019.06.012

Lupton M. Some Ethical and Legal Con-sequences of the Application of Artifi-cial Intelligence in the Field of Medi-cine. Trends in Medicine. 2018; 18(4). http://dx.doi.org/10.15761/TiM.1000147

Lanzagorta-Ortega D, Carrillo-Pérez DL, Carrillo-Esper R. Inteligencia artifi-cial en medicina: Presente y Futuro. Gac-eta Médica de México. 2023; 158(91). https://doi.org/10.24875/gmm.m22000688

Howard J. Artificial Intelligence: Implications for the Future of Work. American Journal of Industrial Medicine. 2019; 62(11):917–26. https://doi.org/10.1002/ajim.23037

Topol EJ. High-Performance Medicine: The Convergence of Human and Artificial Intelli-gence. Nature Medicine. 2019; 25(1):44–56. https://doi.org/10.1038/s41591-018-0300-7

Basogai-Olabe X. Redes neuronales arti-ficiales y sus aplicaciones. Escuela Supe-rior de Ingeniería de Bilbao. Bilbao: Es-cuela Superior de Ingeniería de Bilbao. https://ocw.ehu.eus/file.php/102/redes_neuro/contenidos/pdf/libro-del-curso.pdf

Hamet P, Tremblay J. Artificial Intelligence in Medicine. Metabolism. 2017; 69: 36–40. https://doi.org/10.1016/j.metabol.2017.01.011

Busnatu Ștefan, Niculescu A-G, Bolocan A, Petrescu GE, Păduraru DN, Năstasă I et al. Clinical Applications of Artificial In-telligence—An Updated overview. Jour-nal of Clinical Medicine. 2022; 11(8):2265. https://doi.org/10.3390/jcm11082265

Yao L., Zhang H., Zhang M., Chen X., Zhang J., Huang J., Zhang L. Applica-tion of Artificial Intelligence in Renal Disease. Clin. Ehealth. 2021; 4:54–61. https://doi.org/10.1016/j.ceh.2021.11.003

Krittanawong C, Zhang H, Wang Z, Aydar M, Kitai T. Artificial Intelligence in Precision Cardiovascular Medicine. J Am Coll Cardiol. 2017; 69: 2657–2664. https://doi.org/10.1016/j.jacc.2017.03.571

Schwalbe N, Wahl B. Artificial Intelli-gence and the Future of Global Health. The Lancet. 2020; 395(10236):1579–86. https://doi.org/10.1016/s0140-6736(20)30226-9

Jha S, Topol EJ. Adapting to Artificial Intelli-gence: Radiologists and Pathologists as Infor-mation Specialists. JAMA 2016; 316: 2353–54. https://doi.org/10.1001/jama.2016.17438

Ávila-Tomás JF, Mayer-Pujadas MA, Que-sada-Varela VJ. La Inteligencia artificial y sus aplicaciones en medicina II: Im-portancia actual Y Aplicaciones Prácti-cas. Atención Primaria. 2021; 53(1):81–8. https://doi.org/10.1016/j.aprim.2020.04.014

Mintz Y, Brodie R. Introduction to Artificial Intelligence in Medi-cine. Minimally Invasive Therapy Al-lied Technologies. 2019; 28(2):73–81. https://doi.org/10.1080/13645706.2019.1575882

Jungwirth D, Haluza D. Artificial Intelligence and Public Health: An Exploratory Study. International Journal of Environmental Re-search and Public Health. 2023; 20(5):4541. https://doi.org/10.3390/ijerph20054541

Giansanti D. Artificial Intelligence in Public Health: Current Trends and Future Possibilities. International Journal of Environmental Re-search and Public Health. 2022; 19(19):11907. https://doi.org/10.3390/ijerph191911907

Patel V., Shah M. A Comprehensive Study on Artificial Intelligence and Ma-chine Learning in Drug Discovery and Drug Development. Intell. Med. 2021. https://doi.org/10.1016/j.imed.2021.10.001

Nakamura T., Sasano T. Artificial Intelli-gence and Cardiology: Current Status and Perspective. J. Cardiol. 2022; 79:326–333. https://doi.org/10.1016/j.jjcc.2021.11.017

Bernstam EV, Shireman PK, Meric‐Ber-nstam F, N. Zozus M, Jiang X, Brimhall BB et al. Artificial Intelligence in Clini-cal and Translational Science: Successes, Challenges and Opportunities. Clinical and Translational Science. 2021; 15(2):309–21. https://doi.org/10.1111/cts.13175

Colbert JA, Chokshi DA. Technology in Medical Education—Osler Meets Watson. J Gen Intern Med. 2014 Dec; 29(12):1584-5. https://doi.org/10.1007/s11606-014-2975-x

Valdez-García J, López Cabrera M, Jiménez Martínez M, Díaz Elizondo J, Dávila Ri-vas J, Olivares Olivares S. Me prepa-ro para ayudar: respuesta de escuelas de medicina y ciencias de la salud ante COVID-19. Inv Ed Med. 2020; (35):85-95. https://www.medigraphic.com/cgi-bin/new/resumen.cgi?IDARTICULO=95038

Newman NA, Lattouf OM. Coalition for Medical Education-A Call to Action: A Prop-osition To Adapt Clinical Medical Education to Meet the Needs Of Students And Oth-er Healthcare Learners During COVID-19. J Card Surg. 2020 Jun; 35(6):1174-5. https://doi.org/10.1111/jocs.14590

Abd-Alrazaq A, AlSaad R, Alhuwail D, Ahmed A, Healy PM, Latifi S, Aziz S, Dam-seh R, Alabed Alrazak S, Sheikh J. Large Language Models in Medical Education: Opportunities, Challenges, and Future Direc-tions. JMIR Med Educ. 2023 Jun 1; 9:e48291. https://doi.org/10.2196/48291

Martínez-Ezquerro JD. Authors in the age of language-generation AI: To Be or not to Be, Is That Really the Ques-tion? Arch Med Res 2023; 54:163–167. https://doi.org/10.1016/j.arcmed.2023.03.002

Calleja-López JRT, Rivera-Rosas CN, Ruibal-Tavares E. Impact of ChatGPT and Artificial Intelligence in the Con-temporary Medical Landscape. Arch Med Res. 2023 Jul;54(5):102835. https://doi.org/10.1016/j.arcmed.2023.05.003

Eysenbach G. The Role of ChatGPT, Gen-erative Language Models, and Artificial In-telligence in Medical Education: A Conver-sation with ChatGPT and a Call for Papers. JMIR Med Educ. 2023 Mar 6; 9:e46885. https://doi.org/10.2196/46885

Ward TM, Mascagni P, Madani A, Padoy N, Perretta S, Hashimoto DA. Surgical Data Sci-ence and Artificial Intelligence for Surgical Education. J Surg Oncol. 2021; 124:221-30. https://doi.org/10.1002/jso.26496

He J, Baxter SL, Xu J, Xu J, Zhou X, Zhang K. The Practical Implementation of Arti-ficial Intelligence Technologies in Medi-cine. Nature Medicine. 2019;25(1):30–6. https://doi.org/10.1038/s41591-018-0307-0

Scafa Udriște A., Niculescu A.-G., Grumez-escu A.M., Bădilă E. Cardiovascular Stents: A Review of Past, Current, and Emerg-ing Devices. Materials. 2021; 14:2498. https://doi.org/10.3390/ma14102498

Redaelli A., Votta E. Cardiovascular pa-tient-specific modeling: Where Are We Now and What Does the Future Look like? APL Bioeng. 2020; 4:040401. https://doi.org/10.1063/5.0031452

Balu A., Nallagonda S., Xu F., Krish-namurthy A., Hsu M.-C., Sarkar S. A Deep Learning Framework for Design and Analysis of Surgical Bioprosthet-ic Heart Valves. Sci. Rep. 2019; 9:18560. https://doi.org/10.1038/s41598-019-54707-9

Lee Y., Veerubhotla K., Jeong M.H., Lee C.H. Deep Learning in Personaliza-tion of Cardiovascular Stents. J. Cardio-vasc. Pharmacol. Ther. 2019; 25:110–120. https://doi.org/10.1177/1074248419878405

Liu X., Aslan S., Hess R., Mass P., Olivieri L., Loke Y.H., Hibino N., Fuge M., Krieger A. Au-tomatic Shape Optimization of Patient-Specif-ic Tissue Engineered Vascular Grafts for Aor-tic Coarctation; Proceedings of the 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC); Montreal, QC, Canada. 20–24 July 2020; pp. 2319–2323.

Tilton M., Lewis G.S., Hast M.W., Fox E., Manogharan G. Additively Manufactured Patient-Specific Prosthesis for Tumor Re-construction: Design, Process, and Prop-erties. PLoS ONE. 2021; 16:e0253786. https://doi.org/10.1371/journal.pone.0253786

Li J., Gsaxner C., Pepe A., Morais A., Alves V., von Campe G., Wallner J., Egger J. Synthetic Skull Bone Defects for Au-tomatic Patient-Specific Craniofacial Im-plant Design. Sci. Data. 2021; 8:36. https://doi.org/10.1038/s41597-021-00806-0

Roy S., Dey S., Khutia N., Roy Chowd-hury A., Datta S. Design of Patient Specif-ic Dental Implant Using FE Analysis and Computational Intelligence Techniques. Appl. Soft Comput. 2018; 65:272–279. https://doi.org/10.1016/j.asoc.2018.01.025

Tang A, Tam R, Cadrin-Chênevert A, Guest W, Chong J, Barfett J, et al. Canadian Association of Radiologists White Paper on Artificial In-telligence in Radiology. Canadian Association of Radiologists Journal. 2018; 69(2):120–135. https://doi.org/10.1016/j.carj.2018.02.002

De Fauw J, Ledsam JR, Romera-Paredes B, Nikolov S, Tomasev N, Blackwell S, et al. Clinically Applicable Deep Learning for Diagnosis and Referral in Retinal Dis-ease. Nature Medicine. 2018;24(9):1342–50. https://doi.org/10.1038/s41591-018-0107-6

Slomka PJ, Dey D, Sitek A, Motwani M, Ber-man DS, Germano G. Cardiac Imaging: Work-ing Towards Fully-Automated Machine Anal-ysis & Amp; Interpretation. Expert Review of Medical Devices. 2017; 14(3):197–212. https://doi.org/10.1080/17434440.2017.1300057

Portada del artículo de revisión: Inteligencia artificial en medicina - panorama actual

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Publicado

2023-12-31

Cómo citar

Ruibal-Tavares, E., Calleja-López, J. R., Rivera-Rosas, C. N., & Aguilera-Duarte, L. J. (2023). Inteligencia artificial en medicina: panorama actual. REMUS - Revista Estudiantil De Medicina De La Universidad De Sonora, 5(2). https://doi.org/10.59420/remus.10.2023.178

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