2021, Número 36
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INFODIR 2021; 17 (36)
Inteligencia artificial como potencia de herramienta en salud
Jiménez HLG
Idioma: Español
Referencias bibliográficas: 67
Paginas: 1-30
Archivo PDF: 587.30 Kb.
RESUMEN
Introducción: La inteligencia artificial puede ser una herramienta tecnológica novedosa, útil y práctica que transforme la forma en que se realiza la asistencia sanitaria en búsqueda de lograr mejores resultados en salud.
Objetivo: Incentivar la aplicación práctica de la inteligencia artificial como potencial herramienta en salud mediante la construcción de nuevo conocimiento.
Desarrollo: Se realizó una investigación documental al seleccionar documentos de bases de datos con ayuda de las palabras clave. Se revisó la bibliografía seleccionada, se comparó, se analizó e interpretó el contenido. Los hallazgos evidenciaron que la inteligencia artificial contempla varias formas de aprendizaje automático a través de una gama de aplicaciones y medios (algoritmos, computadoras, dispositivos, robots, Internet) que facilitaría cambios en la forma en que se realiza la atención sanitaria. El recurso humano que trabaja en salud requiere de recursos, preparación académica, capacitación para utilizar y enfrentar los diversos desafíos imperantes con la intención de maximizar el uso de la inteligencia artificial; en la resolución de problemas e implementar mejoras en conjunto con otros actores sociales de modo que se constituya en un recurso que permita mejoras en salud.
Conclusiones: La inteligencia artificial podría generar cada vez más cambios en salud mediante una atención innovadora, moderna, dinámica, humana y personalizada por las facilidades y mecanismos que permiten las tecnologías de comunicación, información, informática y computación. Se requiere gestionar adecuadamente los diversos desafíos para concretar mejores beneficios en salud.
REFERENCIAS (EN ESTE ARTÍCULO)
Rong G, Mendez A, Assi E, Zhao B, Sawan M. Artificial intelligence in healthcare: review and prediction case studies. Engineering. 2020 [acceso 25/02/2021];6(3):291-301. Disponible en: https://www.sciencedirect.com/science/article/pii/S2095809919301535
Shameer K, Johnson K, Glicksberg B, Dudley J, Sengupta P. Machine learning in cardiovascular medicine: are we there yet? Heart. 2018;104(14):1156-64. DOI: https://doi.org/10.1136/heartjnl-2017-311198
Hood L, Auffray C. Participatory medicine: a driving force for revolutionizing healthcare. Genome Medicine. 2013;5(110):1-4. DOI: https://doi.org/10.1186/gm514
Poudel A, Nissen L. Telepharmacy: a pharmacist’s perspective on the clinical benefits and challenges. Integrated Pharmacy Research & Practice. 2016;5:75-82. DOI: https://doi.org/10.2147/IPRP.S101685
Apweiler R, Beissbarth T, Berthold M, Blüthgen N, Burmeister Y. Whither systems medicine? Experimental & molecular medicine. 2018 [acceso 14/03/2021];50(3):e453. Disponible en: https://www.nature.com/articles/emm2017290
Noorbakhsh N, Zand R, Zhang Y, Abedi V. Artificial intelligence transforms the future of healthcare. The American journal of medicine. 2019;132(7):795-801. DOI: https://doi.org/10.1016/j.amjmed.2019.01.017
Holzinger A. Interactive machine learning for health informatics: when do we need the human-in-the-loop? Brain Informatics. 2016;3:119-31. DOI: https://doi.org/10.1007/s40708-016-0042-6
Mahmud M, Kaiser M, McGinnity T, Hussain A. Deep Learning in Mining Biological Data. Cogn Comput. 2021;13:1–33. DOI: https://doi.org/10.1007/s12559-020-09773-x
Kim I, Oh J. Deep learning: from chemoinformatics to precision medicine. Journal of Pharmaceutical Investigation. 2017;47(4):317-23. DOI: https://doi.org/10.1007/s40005-017-0332-x
Ravì D, Wong C, Deligianni F, Berthelot M, Andreu J, Lo B, et al. Deep learning for health informatics. IEEE journal of biomedical and health informatics. 2016;21(1):4-21. DOI: https://doi.org/10.1109/JBHI.2016.2636665
Auffray C, Balling R, Barroso I, Bencze L, Benson M, Bergeron J, et al. Making sense of big data in health research: towards an EU action plan. Genome medicine. 2016;8(1):71. DOI: https://doi.org/10.1186/s13073-016-0323-y
Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, et al. Artificial intelligence in healthcare: past, present and future. Stroke and vascular neurology. 2017;2(4):230-43. DOI: https://doi.org/10.1136/svn-2017-000101
Miotto R, Wang F, Wang S, Jiang X, Dudley J. Deep learning for healthcare: review, opportunities and challenges. Briefings in bioinformatics. 2017;19(6):1236-46. DOI: https://doi.org/10.1093/bib/bbx044
van der Schaar M, Alaa A, Floto A, Gimson A, Scholtes S, Wood A, et al. How artificial intelligence and machine learning can help healthcare systems respond to COVID-19. Machine Learning. 2021 [acceso 04/03/2021];110(1):1-14. Disponible en: https://link.springer.com/content/pdf/10.1007/s10994-020-05928-x.pdf
Shortliffe E, Sepúlveda M. Clinical decision support in the era of artificial intelligence. Jama. 2018;20(21):2199-200. DOI: https://doi.org/10.1001/jama.2018.17163
Wiens J, Shenoy E. Machine learning for healthcare: on the verge of a major shift in healthcare epidemiology. Clinical Infectious Diseases. 2017;66(1):149-53. DOI: https://doi.org/10.1093/cid/cix731
Meskó B, Hetényi G, Győrffy Z. Will artificial intelligence solve the human resource crisis in healthcare? BMC health services research. 2018;18(1):545. DOI: https://doi.org/10.1186/s12913-018-3359-4
Alexander E, Butler C, Darr A, Jenkiss M, Long R, Shipman C, et al. ASHP statement on telepharmacy. American Journal of Health-System Pharmacy. 2017 [acceso 16/01/2021];74(9):e236-e41. Disponible en: https://academic.oup.com/ajhp/article-abstract/74/9/e236/5102780
Steckler T. Telepharmacy: controversy and promise. Journal of Pharmacy Technology. 2016;32 (6):227-9. DOI: https://doi.org/10.1177/8755122516670415
Ching T, Himmelstein D, Beaulieu B, Kalinin A, Do B, Way G, et al. Opportunities and obstacles for deep learning in biology and medicine. Journal of The Royal Society Interface. 2018;15(141):1-47. DOI: https://doi.org/10.1098/rsif.2017.0387
Jones L, Golan D, Hanna S, Ramachandran M. Artificial intelligence, machine learning and the evolution of healthcare: A bright future or cause for concern? Bone & joint research. 2018;7(3):223-5. DOI: https://doi.org/10.1302/2046-3758.73.BJR-2017-0147.R1
Naylor C. On the prospects for a (deep) learning health care system. Jama. 2018;320(11):1099-100. DOI: https://doi.org/10.1001/jama.2018.11103
Lucignani G, Neri E. Integration of imaging biomarkers into systems biomedicine: a renaissance for medical imaging. Clinical and Translational Imaging. 2019;7:149-53. DOI: https://doi.org/10.1007/s40336-019-00320-9
Pitoglou S. Machine Learning in Healthcare: Introduction and Real-World Application Considerations. En Moumtzoglou A. (Ed.) Quality Assurance in the Era of Individualized Medicine, Hershey, PA: IGI Global. 2020:92-109. DOI: https://doi.org/10.4018/978-1-7998-2390-2.ch004
Rajkomar A, Dean J, Kohane I. Machine learning in medicine. New England Journal of Medicine. 2019;380(14):1347-58. DOI: https://doi.org/10.1056/NEJMra1814259
Thaler S, Menkovski V. The role of deep learning in improving healthcare. In: Consoli S., Reforgiato Recupero D, Petković M. (eds) The Role of Deep Learning in Improving Healthcare. Data Science for Healthcare Springer, Cham. 2019:75-116. DOI: https://doi.org/10.1007/978-3-030-05249-2_3
Li Y, Chen C, Wasserman W. Deep feature selection: theory and application to identify enhancers and promoters. Journal of Computational Biology. 2016;23(5):322-36. DOI: https://doi.org/10.1089/cmb.2015.0189
LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521:436-44. DOI: https://doi.org/10.1038/nature14539
Mamoshina P, Vieira A, Putin E, Zhavoronkov A. Applications of deep learning in biomedicine. Molecular pharmaceutics. 2016;13(5):1445-54. DOI: https://doi.org/10.1021/acs.molpharmaceut.5b00982
Stewart C. Learning Analytics: Shifting from theory to practice. Journal on Empowering Teaching Excellence. 2017;1(1):1-12. DOI: https://doi.org/10.15142/T3G63W
Wu J, Yang C, Liao C, Nian M. Analytics 2.0 for Precision Education: An Integrative Theoretical Framework of the Human and Machine Symbiotic Learning. Journal of Educational Technology & Society. 2021 [acceso 15/03/2021];24(1):267-79. Disponible en: https://www.jstor.org/stable/26977872?seq=1#metadata_info_tab_contents
Injadat M, Moubayed A, Nassif A, Shami A. Machine learning towards intelligent systems: applications, challenges, and opportunities. Artificial Intelligence Review. 2021 [acceso 28/02/2021];1-50. Disponible en: https://link.springer.com/article/10.1007/s10462-020-09948-w
Ekins S. The next era: deep learning in pharmaceutical research. Pharmaceutical research. 2016;33:2594-603. DOI: https://doi.org/10.1007/s11095-016-2029-7
Yao Z, Bi J, Chen Y. Applying deep learning to individual and community health monitoring data: A survey. International Journal of Automation and Computing. 2018;15(6):643-55. DOI: https://doi.org/10.1007/s11633-018-1136-9
Kooi T, Litjens G, Van Ginneken B, Gubern A, Sánchez C, Mann R, et al. Large scale deep learning for computer aided detection of mammographic lesions. Medical image analysis. 2017;35:303-12. DOI: https://doi.org/10.1016/j.media.2016.07.007
Dai Y, Wang G. A deep inference learning framework for healthcare. Pattern Recognition Letters. 2018. DOI: https://doi.org/10.1016/j.patrec.2018.02.009
Galvão Y, Ferreira J, Albuquerque V, Barros P, Fernandes B. A multimodal approach using deep learning for fall detection. Expert Systems with Applications. 2021 [acceso 18/03/2021];168:1-9. Disponible en: https://www.sciencedirect.com/science/article/pii/S0957417420309489
Bejnordi B, Veta M, Van Diest P, Van Ginneken B, Karssemeijer N, Litjens G, et al. Diagnostic assessment of deep learning algorithms for detection of lymph node Bmetastases in women with breast cancer. Jama. 2017;318(22):2199-210. DOI: https://doi.org/10.1001/jama.2017.14585
Pham T, Tran T, Phung D, Venkatesh S. Predicting healthcare trajectories from medical records: A deep learning approach. Journal of biomedical informatics. 2017;69:218-29. DOI: https://doi.org/10.1016/j.jbi.2017.04.001
Srivastava S, Soman S, Rai A, Srivastava P. Deep learning for health informatics: Recent trends and future directions. In N. Rashmi, P. Mueller, P. Lorenz International Conference on Advances in Computing, Communications and Informatics (ICACCI) IEEE. Manapal University, Karnataka, India. 2017:1665-70. DOI: https://doi.org/10.1109/ICACCI.2017.8126082
Purushotham S, Meng C, Che Z, Liu Y. Benchmark of deep learning models on large healthcare mimic datasets. Journal of Biomedical Informatics. 2017;83:112-34. DOI: https://doi.org/10.1016/j.jbi.2018.04.007
Chen M, Yang J, Hao Y, Mao S, Hwang K. A 5G cognitive system for healthcare. Big Data and Cognitive Computing. 2017;1(1):1-15. DOI: https://doi.org/10.3390/bdcc1010002
O’Sullivan S, Holzinger A, Zatloukal K, Saldiva P, Sajid M, Wichmann D, et al. Machine learning enhanced virtual autopsy. Autopsy & case reports. 2017;7(4):3-7. DOI: https://doi.org/10.4322/acr.2017.037
Litjens G, Kooi T, Bejnordi B, Setio A, Ciompi F, Ghafoorian M, et al. A survey on deep learning in medical image analysis. Medical image analysis. 2017;42:60-88. DOI: https://doi.org/10.1016/j.media.2017.07.005
Yuan W, Li C, Guan D, Han G, Khattak A. Socialized healthcare service recommendation using deep learning. Neural Computing and Applications. 2018;30(7):2071-82. DOI: https://doi.org/10.1007/s00521-018-3394-4
Beam A, Kohane I. Big data and machine learning in health care. Jama. 2018;319(13):1317-18. DOI: https://doi.org/10.1001/jama.2017.18391
de Meulder B, Lefaudeux D, Bansal A, Mazein A, Chaiboonchoe A, Ahmed H, et al. A computational framework for complex disease stratification from multiple large-scale datasets. BMC systems biology. 2018;12(1):60. DOI: https://doi.org/10.1186/s12918-018-0556-z
Gong K, Wu D, Arru C, Homayounieh F, Neumark N, Guan J, et al. A Multi-Center Study of COVID-19 Patient Prognosis Using Deep Learning-based CT Image Analysis and Electronic Health Records. European Journal of Radiology. 2021 [acceso 24/01/2021]. Disponible en: https://www.sciencedirect.com/science/article/pii/S0720048X21000632
Faust O, Hagiwara Y, Hong T, Lih O, Acharya U. Deep learning for healthcare applications based on physiological signals: A review. Computer methods and programs in biomedicine. 2018;161:1-13. DOI: https://doi.org/10.1016/j.cmpb.2018.04.005
Auger S, Jacobs B, Dobson R, Marshall C, Noyce A. Big data, machine learning and artificial intelligence: a neurologist’s guide. Practical Neurology. 2021 [acceso 30/01/2021];21(1):4-11. Disponible en: https://pn.bmj.com/content/practneurol/21/1/4.full.pdf
McKendrick M, Yang S, McLeod G. The use of artificial intelligence and robotics in regional anaesthesia. Anaesthesia. 2021 [acceso 18/02/2021];76:171-81. Disponible en: https://associationofanaesthetistspublications.onlinelibrary.wiley.com/doi/pdf/10.1111/anae.15274
Davidson L, Boland M. Towards deep phenotyping pregnancy: a systematic review on artificial intelligence and machine learning methods to improve pregnancy outcomes. Briefings in Bioinformatics. 2021 [acceso 18/03/2021]:1-29. Disponible en: https://academic.oup.com/bib/advance-article/doi/10.1093/bib/bbaa369/6065792
Lee D, Yoon S. Application of Artificial Intelligence-Based Technologies in the Healthcare Industry: Opportunities and Challenges. International Journal of Environmental Research and Public Health. 2021;18(1):1-18. DOI: https://doi.org/10.3390/ijerph18010271
He J, Baxter S, Xu J, Xu J, Zhou X, Zhang K, et al. The practical implementation of artificial intelligence technologies in medicine. Nature medicine. 2019;25(1):30-6. Disponible en: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6995276/
Jacobson N, Nemesure M. Using Artificial Intelligence to Predict Change in Depression and Anxiety Symptoms in a Digital Intervention: Evidence from a Transdiagnostic Randomized Controlled Trial. Psychiatry Research. 2021 [acceso 05/02/2021 ];295:113618. Disponible en: http://www.nicholasjacobson.com/files/PDFs/Jacobson%20&%20Nemesure,%202020.pdf
Esteva A, Robicquet A, Ramsundar B, Kuleshov V, DePristo M, Chou K, et al. A guide to deep learning in healthcare. Nature medicine. 2019;25:24-9. DOI: https://doi.org/10.1038/s41591-018-0316-z
Spargo M, Goodfellow N, Scullin C, Grigoleit S, Andreou A, Mavromoustakis C, et al. Shaping the Future of Digitally Enabled Health and Care. Pharmacy. 2021;9(17):1-9. DOI: https://doi.org/10.3390/pharmacy9010017
Shickel B, Tighe P, Bihorac A, Rashidi P. Deep EHR: a survey of recent advances in deep learning techniques for electronic health record (EHR) analysis. IEEE journal of biomedical and health informatics. 2017;22(5):1589-604. DPI: https://doi.org/10.1109/JBHI.2017.2767063
Haefner N, Wincent J, Parida V, Gassmann O. Artificial intelligence and innovation management: A review, framework, and research agenda. Technological Forecasting and Social Change. 2021 [acceso 19/02/2021];162:1-10. Disponible en: https://www.sciencedirect.com/science/article/pii/S004016252031218X
Milletari F, Ahmadi M, Kroll C, Plate A, Rozanski V, Maiostre J, et al. Hough-CNN: Deep learning for segmentation of deep brain regions in MRI and ultrasound. Computer Vision and Image Understanding. 2017 [acceso 04/01/2021];164:92-102. Disponible en: https://arxiv.org/pdf/1601.07014.pdf
Jacobson O, Dalianis H. Applying deep learning on electronic health records in Swedish to predict healthcare-associated infections. Proceedings of the 15th workshop on biomedical natural language processing. 2016:191-5. DOI: https://doi.org/10.18653/v1/W16-2926
Park S, Do K, Choi J, Sim J, Yang D, Eo H, et al. Principles for evaluating the clinical implementation of novel digital healthcare devices. Journal of the Korean Medical Association. 2018 [acceso 08/01/2021];61(12):765-75. Disponible en: https://synapse.koreamed.org/DOIx.php?id=10.5124/jkma.2018.61.12.765
Schwalbe N, Wahl B. Artificial intelligence and the future of global health. The Lancet. 2020;395(10236):1579-86. Disponible en: https://www.thelancet.com/action/showPdf?pii=S0140-6736%2820%2930226-9
Röösli E, Rice B, Hernandez T. Bias at warp speed: how AI may contribute to the disparities gap in the time of COVID-19. Journal of the American Medical Informatics Association. 2021 [acceso 04/02/2021];28(1):190-2. Disponible en: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7454645/
Cui M, Zhang D. Artificial intelligence and computational pathology. Laboratory Investigation. 2021 [acceso 28/02/2021];1-11. Disponible en: https://www.nature.com/articles/s41374-020-00514-0.pdf
Tsoi K, Yiu K, Lee H. The HOPE Asia Network. Applications of artificial intelligence for hypertension management. J Clin Hypertens. 2021:1–7. DOI: https://doi.org/10.1111/jch.1418
Morgenstern J, Rosella L, Daley M, Goel V, Schünemann H, Piggott T, et al.“AI’s gonna have an impact on everything in society, so it has to have an impact on public health”: a fundamental qualitative descriptive study of the implications of artificial intelligence for public health. BMC Public Health. 2021;21(40):1-14. DOI: https://doi.org/10.1186/s12889-020-10030-x