Aplicaciones, oportunidades y desafíos de implementar la inteligencia artificial en medicina
DOI:
https://doi.org/10.22517/25395203.25606Palabras clave:
inteligencia artificial, atención médica, aprendizaje automáticoResumen
La inteligencia artificial se está usando ampliamente en diversos campos de la medicina. El objetivo de esta revisión es describir las principales aplicaciones, oportunidades y desafíos de la inteligencia artificial en medicina brindando una perspectiva del contexto actual. Se realizó una revisión narrativa de la literatura, identificando la información más actualizada y relevante sobre el tema. Se consultaron las bases de datos electrónicas PubMed, Scopus y SciELO, desde enero de 2019 a marzo de 2024, tanto en inglés como en español. Se incluyeron revisiones sistemáticas y no sistemáticas de la literatura, scoping reviews, artículos originales y capítulos de libros. Se excluyeron artículos duplicados, trabajos científicos poco claros, aquellos de bajo rigor científico y literatura gris. La implementación de la inteligencia artificial en medicina ha traído consigo notables beneficios, que van desde el registro de información médica hasta el descubrimiento de nuevos fármacos. Ha generado una revolución en la forma tradicional de hacer medicina. Por otro lado, ha traído consigo desafíos en materia de precisión, confiabilidad, ética, privacidad, entre otros. Es crucial mantener un enfoque centrado en el paciente y garantizar que estas tecnologías se utilicen para mejorar los resultados en salud y promover la equidad en el acceso a la atención médica. La colaboración entre profesionales de la salud, investigadores, entidades reguladoras y desarrolladores de tecnología será fundamental para enfrentar estos desafíos y aprovechar todo el potencial de la inteligencia artificial.
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Hamet P, Tremblay J. Artificial intelligence in medicine. Metab - Clin Exp. el 1 de abril de 2017;69:S36–40.
Liu P ran, Lu L, Zhang J yao, Huo T tong, Liu S xiang, Ye Z wei. Application of Artificial Intelligence in Medicine: An Overview. Curr Med Sci. el 1 de diciembre de 2021;41(6):1105–15.
Patel VL, Shortliffe EH, Stefanelli M, Szolovits P, Berthold MR, Bellazzi R, et al. The coming of age of artificial intelligence in medicine. Artif Intell Med. el 1 de mayo de 2009;46(1):5–17.
Xie Q, Liu Y, Huang H, Hong B, Wang J, Han H, et al. An innovative method for screening and evaluating the degree of diabetic retinopathy and drug treatment based on artificial intelligence algorithms. Pharmacol Res. el 1 de septiembre de 2020;159:104986.
Gong J, Liu J yu, Jiang Y jun, Sun X wen, Zheng B, Nie S dong. Fusion of quantitative imaging features and serum biomarkers to improve performance of computer-aided diagnosis scheme for lung cancer: A preliminary study. Med Phys. 2018;45(12):5472–81.
Rodriguez-Ruiz A, Lång K, Gubern-Merida A, Broeders M, Gennaro G, Clauser P, et al. Stand-Alone Artificial Intelligence for Breast Cancer Detection in Mammography: Comparison With 101 Radiologists. JNCI J Natl Cancer Inst. el 1 de septiembre de 2019;111(9):916–22.
Rodriguez-Ruiz A, Lång K, Gubern-Merida A, Teuwen J, Broeders M, Gennaro G, et al. Can we reduce the workload of mammographic screening by automatic identification of normal exams with artificial intelligence? A feasibility study. Eur Radiol. el 1 de septiembre de 2019;29(9):4825–32.
Acs B, Rantalainen M, Hartman J. Artificial intelligence as the next step towards precision pathology. J Intern Med. 2020;288(1):62–81.
Bera K, Schalper KA, Rimm DL, Velcheti V, Madabhushi A. Artificial intelligence in digital pathology — new tools for diagnosis and precision oncology. Nat Rev Clin Oncol. noviembre de 2019;16(11):703–15.
Stefano GB. Robotic Surgery: Fast Forward to Telemedicine. Med Sci Monit. el 17 de abril de 2017;23:1856–1856.
Zuo S, Yang GZ. Endomicroscopy for Computer and Robot Assisted Intervention. IEEE Rev Biomed Eng. 2017;10:12–25.
Tejo-Otero A, Buj-Corral I, Fenollosa-Artés F. 3D Printing in Medicine for Preoperative Surgical Planning: A Review. Ann Biomed Eng. el 1 de febrero de 2020;48(2):536–55.
Navarrete-Welton AJ, Hashimoto DA. Current applications of artificial intelligence for intraoperative decision support in surgery. Front Med. el 1 de agosto de 2020;14(4):369–81.
Bajorath J, Kearnes S, Walters WP, Meanwell NA, Georg GI, Wang S. Artificial Intelligence in Drug Discovery: Into the Great Wide Open. J Med Chem. el 27 de agosto de 2020;63(16):8651–2.
Nas S, Koyuncu M. Emergency Department Capacity Planning: A Recurrent Neural Network and Simulation Approach. Comput Math Methods Med. el 15 de noviembre de 2019;2019:e4359719.
Yang YY, Shulruf B. An expert-led and artificial intelligence system-assisted tutoring course to improve the confidence of Chinese medical interns in suturing and ligature skills: a prospective pilot study. J Educ Eval Health Prof. el 10 de abril de 2019;16:7.
Dekker I, De Jong EM, Schippers MC, De Bruijn-Smolders M, Alexiou A, Giesbers B. Optimizing Students’ Mental Health and Academic Performance: AI-Enhanced Life Crafting. Front Psychol. el 3 de junio de 2020;11:1063.
Al Kuwaiti A, Nazer K, Al-Reedy A, Al-Shehri S, Al-Muhanna A, Subbarayalu AV, et al. A Review of the Role of Artificial Intelligence in Healthcare. J Pers Med. el 5 de junio de 2023;13(6):951.
Health C for D and R. What is Digital Health? FDA [Internet]. el 22 de septiembre de 2020 [citado el 12 de marzo de 2024]; Disponible en: https://www.fda.gov/medical-devices/digital-health-center-excellence/what-digital-health
Kumar K, Loebinger MR, Ghafur S. The role of wirelessly observed therapy in improving treatment adherence. Future Heal J. el 1 de julio de 2022;9(2):179–82.
Lee D, Yoon SN. Application of Artificial Intelligence-Based Technologies in the Healthcare Industry: Opportunities and Challenges. Int J Environ Res Public Health. enero de 2021;18(1):271.
Choudhury A, Asan O. Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review. JMIR Med Inf. 2020;8(7):e18599.
Loh HW, Ooi CP, Seoni S, Barua PD, Molinari F, Acharya UR. Application of explainable artificial intelligence for healthcare: A systematic review of the last decade (2011–2022). Comput Methods Programs Biomed. 2022;226:107161.
Kumar Y, Koul A, Singla R, Ijaz MF. Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda. J Ambient Intell Humaniz Comput. 2023;14(7):8459–86.
Younis HA, Eisa TAE, Nasser M, Sahib TM, Noor AA, Alyasiri OM, et al. A Systematic Review and Meta-Analysis of Artificial Intelligence Tools in Medicine and Healthcare: Applications, Considerations, Limitations, Motivation and Challenges. Diagnostics. 2024;14(1):109.
Lee S, Kim HS. Prospect of Artificial Intelligence Based on Electronic Medical Record. J Lipid Atheroscler. 2021;10(3):282–90.
Negro-Calduch E, Azzopardi-Muscat N, Krishnamurthy RS, Novillo-Ortiz D. Technological progress in electronic health record system optimization: Systematic review of systematic literature reviews. Int J Med Inf. 2021;152:104507.
Ali O, Abdelbaki W, Shrestha A, Elbasi E, Alryalat MAA, Dwivedi YK. A systematic literature review of artificial intelligence in the healthcare sector: Benefits, challenges, methodologies, and functionalities. J Innov Knowl. 2023;8(1):100333.
Sharma M, Savage C, Nair M, Larsson I, Svedberg P, Nygren JM. Artificial Intelligence Applications in Health Care Practice: Scoping Review. J Med Internet Res. 2022;24(10):e40238.
Arjoune A, Nguyen T, Doroshow R. Technical characterisation of digital stethoscopes: towards scalable artificial intelligence-based auscultation. J Med Eng Technol. 2023;47(3):165–78.
Zhang M, Li M, Guo L, Liu J. A Low-Cost AI-Empowered Stethoscope and a Lightweight Model for Detecting Cardiac and Respiratory Diseases from Lung and Heart Auscultation Sounds. Sensors. 2023;23(5):2591.
Poalelungi DG, Musat CL, Fulga A, Neagu M, Neagu AI, Piraianu AI, et al. Advancing Patient Care: How Artificial Intelligence Is Transforming Healthcare. J Pers Med. 2023;13(8):1214.
Wang Y, Li N, Chen L, Wu M, Meng S, Dai Z, et al. Guidelines, Consensus Statements, and Standards for the Use of Artificial Intelligence in Medicine: Systematic Review. J Med Internet Res. 2023;25(1):e46089.
Martinez-Millana A, Saez-Saez A, Tornero-Costa R, Azzopardi-Muscat N, Traver V, Novillo-Ortiz D. Artificial intelligence and its impact on the domains of universal health coverage, health emergencies and health promotion: An overview of systematic reviews. Int J Med Inf. 2022;166:104855.
Bitkina OV, Park J, Kim HK. Application of artificial intelligence in medical technologies: A systematic review of main trends. Digit Health. 2023;9:20552076231189331.
Shen J, Zhang CJP, Jiang B, Chen J, Song J, Liu Z, et al. Artificial Intelligence Versus Clinicians in Disease Diagnosis: Systematic Review. JMIR Med Inform. 2019;7(3):e10010.
Liu X, Faes L, Kale AU, Wagner SK, Fu DJ, Bruynseels A, et al. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. Lancet Digit Health. 2019;1(6):e271–97.
Nagendran M, Chen Y, Lovejoy CA, Gordon AC, Komorowski M, Harvey H, et al. Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies. BMJ. 2020;368:m689.
Yin J, Ngiam KY, Teo HH. Role of Artificial Intelligence Applications in Real-Life Clinical Practice: Systematic Review. J Med Internet Res. 2021;23(4):e25759.
Herman DS, Rhoads DD, Schulz WL, Durant TJS. Artificial Intelligence and Mapping a New Direction in Laboratory Medicine: A Review. Clin Chem. 2021;67(11):1466–82.
Wen X, Leng P, Wang J, Yang G, Zu R, Jia X, et al. Clinlabomics: leveraging clinical laboratory data by data mining strategies. BMC Bioinformatics. 2022;23(1):387.
Rabbani N, Kim GYE, Suarez CJ, Chen JH. Applications of Machine Learning in Routine Laboratory Medicine: Current State and Future Directions. Clin Biochem. 2022;103:1–7.
Aradhya S, Facio FM, Metz H, Manders T, Colavin A, Kobayashi Y, et al. Applications of artificial intelligence in clinical laboratory genomics. Am J Med Genet C Semin Med Genet. 2023;193(3):e32057.
Alloghani M, Al-Jumeily D, Aljaaf AJ, Khalaf M, Mustafina J, Tan SY. The Application of Artificial Intelligence Technology in Healthcare: A Systematic Review. En: Khalaf MI, Al-Jumeily D, Lisitsa A, editores. Applied Computing to Support Industry: Innovation and Technology. Cham: Springer International Publishing; 2020. p. 248–61. (Communications in Computer and Information Science).
Khan ZF, Alotaibi SR. Applications of Artificial Intelligence and Big Data Analytics in m-Health: A Healthcare System Perspective. J Healthc Eng. 2020;2020:e8894694.
Antoniadi AM, Du Y, Guendouz Y, Wei L, Mazo C, Becker BA, et al. Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review. Appl Sci. 2021;11(11):5088.
Zhou Q, Chen Z hang, Cao Y heng, Peng S. Clinical impact and quality of randomized controlled trials involving interventions evaluating artificial intelligence prediction tools: a systematic review. Npj Digit Med. 2021;4(1):1–12.
Schwalbe N, Wahl B, Song J, Lehtimaki S. Data Sharing and Global Public Health: Defining What We Mean by Data. Front Digit Health. 2020;2:612339.
Sarkar IN. Transforming Health Data to Actionable Information: Recent Progress and Future Opportunities in Health Information Exchange. Yearb Med Inform. el 4 de diciembre de 2022;31(1):203–14.
Chan KS, Zary N. Applications and Challenges of Implementing Artificial Intelligence in Medical Education: Integrative Review. JMIR Med Educ. 2019;5(1):e13930.
Masters K. Artificial intelligence in medical education. Med Teach. 2019;41(9):976–80.
Nagi F, Salih R, Alzubaidi M, Shah H, Alam T, Shah Z, et al. Applications of Artificial Intelligence (AI) in Medical Education: A Scoping Review. En: Healthcare Transformation with Informatics and Artificial Intelligence. IOS Press; 2023. p. 648–51.
Sun L, Yin C, Xu Q, Zhao W. Artificial intelligence for healthcare and medical education: a systematic review. Am J Transl Res. 2023;15(7):4820–8.
Pupic N, Ghaffari-zadeh A, Hu R, Singla R, Darras K, Karwowska A, et al. An evidence-based approach to artificial intelligence education for medical students: A systematic review. PLOS Digit Health. 2023;2(11):e0000255.
Roppelt JS, Kanbach DK, Kraus S. Artificial intelligence in healthcare institutions: A systematic literature review on influencing factors. Technol Soc. 2024;76:102443.
Wang F, Preininger A. AI in Health: State of the Art, Challenges, and Future Directions. Yearb Med Inf. 28(1):016–26.
Iliashenko O, Bikkulova Z, Dubgorn A. Opportunities and challenges of artificial intelligence in healthcare. E3S Web Conf. 2019;110:02028.
Chen M, Zhang B, Cai Z, Seery S, Gonzalez MJ, Ali NM, et al. Acceptance of clinical artificial intelligence among physicians and medical students: A systematic review with cross-sectional survey. Front Med. el 31 de agosto de 2022;9.
Briganti G, Le Moine O. Artificial Intelligence in Medicine: Today and Tomorrow. Front Med. 2020;7.
Preiksaitis C, Rose C. Opportunities, Challenges, and Future Directions of Generative Artificial Intelligence in Medical Education: Scoping Review. JMIR Med Educ. 2023;9(1):e48785.
Han ER, Yeo S, Kim MJ, Lee YH, Park KH, Roh H. Medical education trends for future physicians in the era of advanced technology and artificial intelligence: an integrative review. BMC Med Educ. 2019;19(1):460.
Baclic O, Tunis M, Young K, Doan C, Swerdfeger H, Schonfeld J. Challenges and opportunities for public health made possible by advances in natural language processing. Can Commun Dis Rep. 2020;46(6):161–8.
Wubineh BZ, Deriba FG, Woldeyohannis MM. Exploring the opportunities and challenges of implementing artificial intelligence in healthcare: A systematic literature review. Urol Oncol Semin Orig Investig. el 1 de marzo de 2024;42(3):48–56.
Llamas Covarrubias JZ, Mendoza Enríquez OA, Graff Guerrero M, Llamas Covarrubias JZ, Mendoza Enríquez OA, Graff Guerrero M. ENFOQUES REGULATORIOS PARA LA INTELIGENCIA ARTIFICIAL (IA). Rev Chil Derecho. diciembre de 2022;49(3):31–62.
Carter SM, Rogers W, Win KT, Frazer H, Richards B, Houssami N. The ethical, legal and social implications of using artificial intelligence systems in breast cancer care. The Breast. el 1 de febrero de 2020;49:25–32.
Sebastian AM, Peter D. Artificial Intelligence in Cancer Research: Trends, Challenges and Future Directions. Life. diciembre de 2022;12(12):1991.
Bartoletti I. AI in Healthcare: Ethical and Privacy Challenges. En: Riaño D, Wilk S, ten Teije A, editores. Artificial Intelligence in Medicine. Cham: Springer International Publishing; 2019. p. 7–10.
Cohen IG, Evgeniou T, Gerke S, Minssen T. The European artificial intelligence strategy: implications and challenges for digital health. Lancet Digit Health. el 1 de julio de 2020;2(7):e376–9.
Gerke S, Minssen T, Cohen G. Chapter 12 - Ethical and legal challenges of artificial intelligence-driven healthcare. En: Bohr A, Memarzadeh K, editores. Artificial Intelligence in Healthcare. Academic Press; 2020. p. 295–336.
Singh RP, Hom GL, Abramoff MD, Campbell JP, Chiang MF, on behalf of the AAO Task Force on Artificial Intelligence. Current Challenges and Barriers to Real-World Artificial Intelligence Adoption for the Healthcare System, Provider, and the Patient. Transl Vis Sci Technol. el 11 de agosto de 2020;9(2):45.
Mousavi Baigi SF, Sarbaz M, Ghaddaripouri K, Ghaddaripouri M, Mousavi AS, Kimiafar K. Attitudes, knowledge, and skills towards artificial intelligence among healthcare students: A systematic review. Health Sci Rep. 2023;6(3):e1138.
Lee J, Wu AS, Li D, Kulasegaram K (Mahan). Artificial Intelligence in Undergraduate Medical Education: A Scoping Review. Acad Med. 2021;96(11S):S62.
Strubell E, Ganesh A, McCallum A. Energy and Policy Considerations for Deep Learning in NLP. Univ Mass Amherst. el 5 de junio de 2019;
Weissglass DE. Contextual bias, the democratization of healthcare, and medical artificial intelligence in low- and middle-income countries. Bioethics. 2022;36(2):201–9.
Gibbons ED. Toward a More Equal World: The Human Rights Approach to Extending the Benefits of Artificial Intelligence. IEEE Technol Soc Mag. marzo de 2021;40(1):25–30.
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