Nuevas tecnologías en Medicina

Autores/as

  • José Luis Izquierdo Alonso
  • Carlos Almonacid Sánchez

DOI:

https://doi.org/10.37536/RIECS.2022.7.1.308

Palabras clave:

Inteligencia artificial, Big Data, Machine Learning, Deep Learning, Nuevas tecnologías, Telemedicina

Resumen

Varios sectores de la sociedad, y de forma especial la medicina, están en un momento de cambio que, apoyado en un gran desarrollo tecnológico, va a revolucionar nuestra forma de entender la gestión, investigación y asistencia clínica. Actualmente la medicina se basa en estudios, frecuentemente con pobre validez externa, cuyos resultados se trasladan a un paciente concreto a pesar de estar basados en simples medias estadísticas. El avance hacia una medicina personalizada solo será posible con nuevos enfoques en los que el Big Data y la inteligencia artificial van a ser de gran ayuda a la hora de trasladar la mejor investigación clínica a nuestro paciente, sin olvidar todo lo que estas herramientas pueden aportar en la investigación básica. A nivel de gestión, el modelo asistencial presencial deberá complementarse con otras formas de trabajo mucho más eficientes que ayuden a consolidar la sostenibilidad del sistema sanitario actual. En este entorno, el desarrollo tecnológico proporcionará las herramientas necesarias para implementar estos cambios. El Big Data como fuente de información, la inteligencia artificial con todas las variantes, que incluyen desde el procesamiento de lenguaje natural hasta el desarrollo de técnicas de imagen, y el desarrollo de nuevas tecnologías en múltiples áreas, van a ser herramientas habituales para el médico, muy posiblemente antes de que finalice esta década. Aunque en esta revisión nos hemos focalizado en patologías respiratorias este plantea-miento es plenamente extrapolable a cualquier otra área de la medicina.

Citas

Eric Topol. Deep medicine: how artificial intelligence can make healthcare human again. Basic books. Hachette Book Group. 1290 Avenue of the Americas, New York, NY10104. First edition: March 2019. ISBNs: 978-1-5416-4464-9 (ebook).
Richard Koch. El principio 80/20. Espasa Libros, S. L. U., 2009. Av. Diagonal, 662-664, 08034 Barcelona (España). ISBN: 978-84-493-3116-9.
Izquierdo JL, Morena D, González Y, Paredero JM, Pérez B, Graziani D, Gutiérrez M, Rodríguez JM. Clinical Management of COPD in a Real-World Setting. A Big Data Analysis. Arch Bronconeumol. 2021; 57 (2): 94–100.
Izquierdo JL, Godoy R. Manejo clínico de la EPOC en Castilla la Mancha. Una oportunidad para mejorar. Revista de SOCAMPAR. 2020; 1 (V).
Izquierdo JL, De Miguel J. Economic Impact of pulmonary drugs on direct costs of stable Chronic Obstructive Pulmonary Disease. Journal of COPD. 2004; 1: 215-223.
De Miguel Díez J, Izquierdo Alonso JL, Rodríguez González Moro JM, De Lucas Ramos P, Molina París J. Tratamiento farmacológico de la EPOC en dos niveles asistenciales. Grado de adecuación de las normativas recomendadas. Arch Bronconeumol 2003; 39: 195-202.
Pozo-Rodríguez F, López-Campos JL, Alvarez-Martínez CJ, Castro-Acosta A, Agüero R, Hueto J, et al; AUDIPOC Study Group. Clinical audit of COPD patients requiring hospital admissions in Spain: AUDIPOC study. PLoS One. 2012; 7 (7): e42156. doi: 10.1371/journal.pone.0042156. Epub 2012 Jul 31. PMID: 22911875; PMCID: PMC3418048.
Izquierdo JL, Almonacid C, González Y, et al. The impact of COVID-19 on patients with asthma. Eur Respir J 2021; 57: 2003142.
Graziani D, Soriano JB, Del Rio-Bermudez C, Morena D, Díaz T, Castillo M, Alonso M, Ancochea J, Lumbreras S, Izquierdo JL. Characteristics and Prognosis of COVID-19 in Patients with COPD. J Clin Med. 2020 Oct 12;9(10): E3259. doi: 10.3390/jcm9103259.PMID: 33053774.
Izquierdo JL, Ancochea J; Savana COVID-19 Research Group, Soriano JB Clinical Characteristics and Prognostic Factors for Intensive Care Unit Admission of Patients With COVID-19: Retrospective Study Using Machine Learning and Natural Language Processing. J Med Internet Res. 2020 Oct 28;22(10): e21801. doi: 10.2196/21801.PMID: 33090964.
Izquierdo JL, Soriano J, González Y, Lumbreras S, Ancochea J, Echeverry C, RRG_ Moro JM. Use of N-Acetylcysteine at high doses as oral treatment for paties hospitalized with COVID-19. Science Progress. Sci Prog. 2022 Jan-Mar;105 (1): 368504221074574. doi: 10.1177/00368504221074574. PMID: 35084258; PMCID: PMC8795755.
Ancochea J, Izquierdo JL, Savana COVID-19 Research Group*, Soriano JB. Evidence of gender bias in the diagnosis and management of covid-19 patients: a big data analysis of electronic health records. Journal of Women Health. J Womens Health (Larchmt). 2021 Mar; 30 (3): 393-404.
Izquierdo JL, Almonacid C, Campos C, et al. Systemic Corticosteroids in Patients with Bronchial Asthma: A Real-Life Study. J Investig Allergol Clin Immunol 2021; Nov 11:0. doi: 10.18176/jiaci.0765. Epub ahead of print. PMID: 34779775.
Jakobsson E, Nygård L, Kottorp A, et al. Does the purpose matter? A comparison of everyday information and communication technologies between eHealth use and general use as perceived by older adults with cognitive impairment. Disability and Rehabilitation: Assistive Technology 2020; 0: 1–10.
Dores AR, Geraldo A, Carvalho IP, et al. The Use of New Digital Information and Communication Technologies in Psychological Counseling during the COVID-19 Pandemic. Int J Environ Res Public Health; 17. Epub ahead of print October 2020. DOI: 10.3390/ijerph17207663.
Almonacid C, Blanco-Aparicio M, Domínguez-Ortega J, et al. [Teleconsultation in the follow-up of the asthma patient. Lessons after COVID-19]. Arch Bronconeumol 2021; 57: 13–14.
BTS/SIGN British guideline on the management of asthma | British Thoracic Society | Better lung health for all, https://www.brit-thoracic.org.uk/standards-of-care/guidelines/btssign-british-guideline-on-the-management-of-asthma/ (2016, accessed 9 December 2016).
da Fonseca MH, Kovaleski F, Picinin CT, Pedroso B, Rubbo P. E-Health Practices and Technologies: A Systematic Review from 2014 to 2019. Healthcare (Basel). 2021 Sep 10;9(9):1192. doi: 10.3390/healthcare9091192. PMID: 34574966; PMCID: PMC8470487.
Chongmelaxme B, Lee S, Dhippayom T, et al. The Effects of Telemedicine on Asthma Control and Patients’ Quality of Life in Adults: A Systematic Review and Meta-analysis. J Allergy Clin Immunol Pract 2019; 7: 199-216.e11.
Barbosa MT, Sousa CS, Morais-Almeida M, et al. Telemedicine in COPD: An Overview by Topics. COPD 2020; 17: 601–617.
Thakkar J, Kurup R, Laba T-L, et al. Mobile Telephone Text Messaging for Medication Adherence in Chronic Disease: A Meta-analysis. JAMA Intern Med 2016; 176: 340–349.
Ahmed S, Ernst P, Bartlett SJ, et al. The Effectiveness of Web-Based Asthma Self-Management System, My Asthma Portal (MAP): A Pilot Randomized Controlled Trial. Journal of Medical Internet Research 2016; 18: e313.
Portnoy JM, Waller M, De Lurgio S, et al. Telemedicine is as effective as in-person visits for patients with asthma. Ann Allergy Asthma Immunol 2016; 117: 241–245.
Shah AC, Badawy SM. Telemedicine in Pediatrics: Systematic Review of Randomized Controlled Trials. JMIR Pediatr Parent 2021; 4: e22696.
Zhu Y, Gu X, Xu C. Effectiveness of telemedicine systems for adults with heart failure: a meta-analysis of randomized controlled trials. Heart Fail Rev 2020; 25: 231–243.
Jeminiwa R, Hohmann L, Qian J, et al. Impact of eHealth on medication adherence among patients with asthma: A systematic review and meta-analysis. Respir Med 2019; 149: 59–68.
Ruiz-Romero V, Martínez-Pillado M, Torres-Domínguez Y, et al. EVALUACIÓN DE LA SATISFACCIÓN DEL PACIENTE EN LA TELECONSULTA DURANTE LA PANDEMIA POR COVID-19(*). Rev Esp Salud Pública; 11.
Damico NJ, Deshane A, Kharouta M, et al. Telemedicine Use and Satisfaction Among Radiation Oncologists During the COVID-19 Pandemic: Evaluation of Current Trends and Future Opportunities. Adv Radiat Oncol 2022; 7: 100835.
Giró T. Inversión prioritaria - Diagnósticos de nueva generación (digitalizada y/o móvil). 5.
La telemedicina, área de inversión clave para los próximos 5 años. DiarioMedico, https://www.diariomedico.com/politica/la-telemedicina-area-de-inversion-clave-para-los-proximos-5-anos.html (2022, accessed 11 April 2022).
Bentes PCL, Nadal J. A telediagnosis assistance system for multiple-lead electrocardiography. Phys Eng Sci Med 2021; 44: 473–485.
Inamura N, Taniguchi T, Takada N. The telediagnosis of double aortic arch using spatio-temporal image correlation. Echocardiography 2021; 38: 1081–1083.
Mabuchi A, Waratani M, Tanaka Y, et al. Telediagnosis system for congenital heart disease in a Japanese prefecture. J Med Ultrason (2001) 2020; 47: 463–468.
Kaiser Y, Eklund A, Grunewald J. Moving target: shifting the focus to pulmonary sarcoidosis as an autoimmune spectrum disorder. Eur Respir J; 54. Epub ahead of print July 2019. DOI: 10.1183/13993003.021532018.
Xu L, Shi H, Shen M, et al. The Effects of mHealth-Based Gamification Interventions on Participation in Physical Activity: Systematic Review. JMIR Mhealth Uhealth 2022; 10: e27794.
Dakroub AH, Weinberger JJ, Levine DL. Gamification for the Win in Internal Medicine Residency: A Longitudinal, Innovative, Team-Based, Gamified Approach to Internal Medicine Board-Review. Cureus 2022; 14: e22822.
Tran S, Smith L, El-Den S, et al. The Use of Gamification and Incentives in Mobile Health Apps to Improve Medication Adherence: Scoping Review. JMIR Mhealth Uhealth 2022; 10: e30671.
Makris E, Hu L, Jones GB, et al. Moving the Dial on Heart Failure Patient Adherence Rates. Patient Prefer Adherence 2020; 14: 2407–2418.
Bonini M, Usmani OS. Novel methods for device and adherence monitoring in asthma. Curr Opin Pulm Med 2018; 24: 63–69.
Sardi L, Idri A, Fernández-Alemán JL. A systematic review of gamification in e-Health. J Biomed Inform 2017; 71: 31–48.
van Gaalen AEJ, Brouwer J, Schönrock-Adema J, et al. Gamification of health professions education: a systematic review. Adv Health Sci Educ Theory Pract 2021; 26: 683–711.
Boulet L-P. The Expert Patient and Chronic Respiratory Diseases. Can Respir J 2016; 2016: 9454506.
Olszewski AE, Wolbrink TA. Serious Gaming in Medical Education: A Proposed Structured Framework for Game Development. Simul Healthc 2017; 12: 240–253.
Metting E, Dassen L, Aardoom J, et al. Effectiveness of Telemonitoring for Respiratory and Systemic Symptoms of Asthma and COPD: A Narrative Review. Life (Basel) 2021; 11: 1215.
Kinast B, Lutz M, Schreiweis B. Telemonitoring of Real-World Health Data in Cardiology: A Systematic Review. Int J Environ Res Public Health 2021; 18: 9070.
Schutte-Rodin S. Telehealth, Telemedicine, and Obstructive Sleep Apnea. Sleep Med Clin 2020; 15: 359–375.
Ambrosino N, Vitacca M, Dreher M, et al. Tele-monitoring of ventilator-dependent patients: a European Respiratory Society Statement. Eur Respir J 2016; 48: 648–663.
Jiang F, Jiang Y, Zhi H, et al. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol 2017; 2: 230–243.
Huhn S, Axt M, Gunga H-C, et al. The Impact of Wearable Technologies in Health Research: Scoping Review. JMIR Mhealth Uhealth 2022; 10: e34384.
van der Kamp MR, Klaver EC, Thio BJ, et al. WEARCON: wearable home monitoring in children with asthma reveals a strong association with hospital-based assessment of asthma control. BMC Med Inform Decis Mak 2020; 20: 192.
Rutkowski S, Buekers J, Rutkowska A, et al. Monitoring Physical Activity with a Wearable Sensor in Patients with COPD during In-Hospital Pulmonary Rehabilitation Program: A Pilot Study. Sensors (Basel) 2021; 21: 2742.
Düking P, Zinner C, Trabelsi K, et al. Monitoring and adapting endurance training on the basis of heart rate variability monitored by wearable technologies: A systematic review with meta-analysis. J Sci Med Sport 2021; 24: 1180–1192.
Roberts G. e-Inhalers. Clin Exp Allergy 2018; 48: 102–103.
Almonacid C, Melero C, López Viña A, et al. Effectiveness of Text Message Reminders on Adherence to Inhaled Therapy in Patients With Asthma: Prospective Multicenter Randomized Clinical Trial. JMIR Form Res 2021; 5: e12218.
Moore A, Preece A, Sharma R, et al. A randomised controlled trial of the effect of a connected inhaler system on medication adherence in uncontrolled asthmatic patients. European Respiratory Journal; 57. Epub ahead of print 1 June 2021. DOI: 10.1183/13993003.03103-2020.
Fonseca A, Osma J. Using Information and Communication Technologies (ICT) for Mental Health Prevention and Treatment. Int J Environ Res Public Health 2021; 18: E461.
Kampmeijer R, Pavlova M, Tambor M, et al. The use of e-health and m-health tools in health promotion and primary prevention among older adults: a systematic literature review. BMC Health Serv Res 2016; 16 Suppl 5: 290.
Sanchez MA, Rabin BA, Gaglio B, et al. A systematic review of eHealth cancer prevention and control interventions: new technology, same methods and designs? Transl Behav Med 2013; 3: 392–401.
Silang KA, Sohal PR, Bright KS, et al. eHealth Interventions for Treatment and Prevention of Depression, Anxiety, and Insomnia During Pregnancy: Systematic Review and Meta-analysis. JMIR Ment Health 2022; 9: e31116.
Jaramillo-Martinez GA, Vasquez-Serna H, Chavarro-Ordoñez R, et al. Ibagué Saludable: A novel tool of Information and Communication Technologies for surveillance, prevention and control of dengue, chikungunya, Zika and other vector-borne diseases in Colombia. J Infect Public Health 2018; 11: 145–146.
Yang F, Wang Y, Yang C, et al. Mobile health applications in self-management of patients with chronic obstructive pulmonary disease: a systematic review and meta-analysis of their efficacy. BMC Pulm Med 2018; 18: 147.
Epstein D, Barak-Corren Y, Isenberg Y, et al. Clinical Decision Support System: A Pragmatic Tool to Improve Acute Exacerbation of COPD Discharge Recommendations. COPD 2019; 16: 18–24.
Merone M, Pedone C, Capasso G, et al. A Decision Support System for Tele-Monitoring COPD-Related Worrisome Events. IEEE J Biomed Health Inform 2017; 21: 296–302.
Khemasuwan D, Sorensen JS, Colt HG. Artificial intelligence in pulmonary medicine: computer vision, predictive model and COVID-19. Eur Respir Rev 2020; 29: 200181.
Mlodzinski E, Stone DJ, Celi LA. Machine Learning for Pulmonary and Critical Care Medicine: A Narrative Review. Pulm Ther 2020; 6: 67–77.
Kumar Y, Koul A, Singla R, et al. Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda. J Ambient Intell Humaniz Comput 2022; 1–28.
Den Exter A. Editorial: EHealth Law: The Final Frontier? Eur J Health Law 2016; 23: 227–230.
Dimond B. Telemedicine and the law. Nurs Times 2003; 99: 50–52.
De Pietro C, Francetic I. E-health in Switzerland: The laborious adoption of the federal law on electronic health records (EHR) and health information exchange (HIE) networks. Health Policy 2018; 122: 69–74.
Edemacu K, Jang B, Kim JW. Collaborative Ehealth Privacy and Security: An Access Control With Attribute Revocation Based on OBDD Access Structure. IEEE J Biomed Health Inform 2020; 24: 2960–2972.
Al-Issa Y, Ottom MA, Tamrawi A. eHealth Cloud Security Challenges: A Survey. J Healthc Eng 2019; 2019: 7516035.
de la Torre-Díez I, López-Coronado M, Vaca C, et al. Cost-utility and cost-effectiveness studies of telemedicine, electronic, and mobile health systems in the literature: a systematic review. Telemed J E Health 2015; 21: 81–85.

Descargas

Publicado

30-05-2022 — Actualizado el 31-05-2022

Número

Sección

Artículos de Revisión