Inteligencia Artificial en Salud Digital: Cuestiones y dimensiones de los aspectos éticas

  • Fredrick R. Ishengoma The University of Dodoma
Palabras clave: Inteligencia artificial, Sanidad digital, Ética, Machine Learning

Resumen

La inteligencia artificial (IA) está transformando el sistema de atención médica a un ritmo vertiginoso al mejorar los servicios, la investigación y el rendimiento de la atención médica digital, impulsados por la combinación de grandes datos y sólidos algoritmos de aprendizaje automático. Como resultado, las aplicaciones de IA se están empleando en dominios de atención médica digital, algunos de los cuales antes se consideraban realizados solo por experiencia humana. Sin embargo, a pesar de los beneficios de la IA en los servicios de atención médica digital, es necesario abordar los problemas y las preocupaciones éticas. Utilizando la metodología de revisión de mapeo, se presenta y discute una taxonomía de problemas y preocupaciones éticas que rodean el empleo de la IA en el cuidado de la salud. Además, se presentan recomendaciones de política y direcciones de investigación futuras.

Descargas

La descarga de datos todavía no está disponible.
Citas

Saxena, A., Brault, N., & Rashid, S. (2021). Big Data and Artificial Intelligence for Healthcare Applications (1st ed.). CRC Press. https://doi.org/10.1201/9781003093770.

Goyal, L.M., Saba, T., Rehman, A., & Larabi-Marie-Sainte, S. (Eds.). (2021). Artificial Intelligence and Internet of Things: Applications in Smart Healthcare (1st ed.). CRC Press. https://doi.org/10.1201/9781003097204

Sun, L., Gupta, R.K. & Sharma, A. Review and potential for artificial intelligence in healthcare. Int J Syst Assur Eng Manag (2021). https://doi.org/10.1007/s13198-021-01221-9

Kishor, A., Chakraborty, C. Artificial Intelligence and Internet of Things Based Healthcare 4.0 Monitoring System. Wireless Pers Commun (2021). https://doi.org/10.1007/s11277-021-08708-5

Johnson, M., Albizri, A. & Simsek, S. Artificial intelligence in healthcare operations to enhance treatment outcomes: a framework to predict lung cancer prognosis. Ann Oper Res (2020). https://doi.org/10.1007/s10479-020-03872-6

Ostherr, K. Artificial Intelligence and Medical Humanities. J Med Humanit (2020). https://doi.org/10.1007/s10912-020-09636-4

Amann, J., Blasimme, A., Vayena, E. et al. Explainability for artificial intelligence in healthcare: a multidisciplinary perspective. BMC Med Inform Decis Mak 20, 310 (2020). https://doi.org/10.1186/s12911-020-01332-6

Bohr, A., & Memarzadeh, K. (2020). The rise of artificial intelligence in healthcare applications. Artificial Intelligence in Healthcare, 25–60. https://doi.org/10.1016/B978-0-12-818438-7.00002-2

Hindocha, S., Badea, C. Moral exemplars for the virtuous machine: the clinician's role in ethical artificial intelligence for healthcare. AI Ethics (2021). https://doi.org/10.1007/s43681-021-00089-6

Winfield, A.F., Michael, K., Pitt, J., Evers, V.: Machine ethics: the design and governance of ethical ai and autonomous systems. Proc. IEEE 107, 509–517 (2019).

Char, D.S., Shah, N.H., Magnus, D.: Implementing machine learning in health care' addressing ethical challenges. N. Engl. J. Med. 378, 981–983 (2018).

Keskinbora, K.H.: Medical ethics considerations on artificial intelligence. J. Clin. Neurosci. 64, 277–282 (2019).

Hagendorff, T.: The ethics of AI ethics—an evaluation of guidelines. Minds Mach. (2019). https://doi.org/10.1007/s11023-020-09517-8

McDougall, R.J.: Computer knows best? The need for value-flexibility in medical AI. J. Med. Ethics 45, 156–160 (2019)

Rigby, M.J.: Ethical dimensions of using artificial intelligence in health care. AMA J. Ethics 21, 121–124 (2019)

Broome, D.T., Hilton, C.B. & Mehta, N. Policy Implications of Artificial Intelligence and Machine Learning in Diabetes Management. Curr Diab Rep 20, 5 (2020). https://doi.org/10.1007/s11892-020-1287-2

Mirbabaie, M., Stieglitz, S. & Frick, N.R.J. Artificial intelligence in disease diagnostics: A critical review and classification on the current state of research guiding future direction. Health Technol. 11, 693–731 (2021). https://doi.org/10.1007/s12553-021-00555-5

Ishii, K. Comparative legal study on privacy and personal data protection for robots equipped with artificial intelligence: looking at functional and technological aspects. AI & Soc 34, 509–533 (2019). https://doi.org/10.1007/s00146-017-0758-8

Luan, F., Gao, X., Zhao, S. et al. The Roles of Plastic Surgeons in Advancing Artificial Intelligence in Plastic Surgery. Aesth Plast Surg (2021). https://doi.org/10.1007/s00266-021-02302-7

Meskó, B., Hetényi, G. & Győrffy, Z. Will artificial intelligence solve the human resource crisis in healthcare?. BMC Health Serv Res 18, 545 (2018). https://doi.org/10.1186/s12913-018-3359-4

Pandey, S.K., Janghel, R.R. Recent Deep Learning Techniques, Challenges and Its Applications for Medical Healthcare System: A Review. Neural Process Lett 50, 1907–1935 (2019). https://doi.org/10.1007/s11063-018-09976-2

Matsumura, N. From genetic analysis to precision medicine. Int Canc Conf J 10, 159 (2021). https://doi.org/10.1007/s13691-021-00492-0

Arnold, M.H. Teasing out Artificial Intelligence in Medicine: An Ethical Critique of Artificial Intelligence and Machine Learning in Medicine. Bioethical Inquiry 18, 121–139 (2021). https://doi.org/10.1007/s11673-020-10080-1

Wong, J., Murray Horwitz, M., Zhou, L. et al. Using Machine Learning to Identify Health Outcomes from Electronic Health Record Data. Curr Epidemiol Rep 5, 331–342 (2018). https://doi.org/10.1007/s40471-018-0165-9

Crowson, M.G., Chan, T.C.Y. Machine Learning as a Catalyst for Value-Based Health Care. J Med Syst 44, 139 (2020). https://doi.org/10.1007/s10916-020-01607-5

Alanazi, H.O., Abdullah, A.H. & Qureshi, K.N. A Critical Review for Developing Accurate and Dynamic Predictive Models Using Machine Learning Methods in Medicine and Health Care. J Med Syst 41, 69 (2017). https://doi.org/10.1007/s10916-017-0715-6

Havaei, F., Ji, X.R., MacPhee, M. et al. Identifying the most important workplace factors in predicting nurse mental health using machine learning techniques. BMC Nurs 20, 216 (2021). https://doi.org/10.1186/s12912-021-00742-9

Srividya, M., Mohanavalli, S. & Bhalaji, N. Behavioral Modeling for Mental Health using Machine Learning Algorithms. J Med Syst 42, 88 (2018). https://doi.org/10.1007/s10916-018-0934-5

Payrovnaziri, S. N., Chen, Z., Rengifo-Moreno, P., Miller, T., Bian, J., Chen, J. H., Liu, X., & He, Z. (2020). Explainable artificial intelligence models using real-world electronic health record data: a systematic scoping review. Journal of the American Medical Informatics Association: JAMIA, 27(7), 1173–1185. https://doi.org/10.1093/jamia/ocaa053

Adadi A, Berrada M.. Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). IEEE Access 2018; 6: 52138–60

Tjoa E, Guan C. A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI. IEEE Trans Neural Netw Learn Syst. 2021 Nov;32(11):4793-4813. DOI: 10.1109/TNNLS.2020.3027314. Epub 2021 Oct 27. PMID: 33079674.

Amann, J., Blasimme, A., Vayena, E. et al. Explainability for artificial intelligence in healthcare: a multidisciplinary perspective. BMC Med Inform Decis Mak 20, 310 (2020). https://doi.org/10.1186/s12911-020-01332-6

London AJ. Artificial intelligence and black-box medical decisions: accuracy versus explainability. Hastings Cent Rep 2019;49:1521. Available from: https://doi.org/ 10.1002/hast.973.

Alemzadeh, H., Raman, J., Leveson, N., Kalbarczyk, Z., & Iyer, R. K. (2016). Adverse Events in Robotic Surgery: A Retrospective Study of 14 Years of FDA Data. PloS one, 11(4), e0151470. https://doi.org/10.1371/journal.pone.0151470

Sujan Sarker, Lafifa Jamal, Syeda Faiza Ahmed, Niloy Irtisam, Robotics and artificial intelligence in healthcare during COVID-19 pandemic: A systematic review, Robotics and Autonomous Systems, Volume 146, 2021,

Liyanage, H., Liaw, S. T., Jonnagaddala, J., Schreiber, R., Kuziemsky, C., Terry, A. L., & de Lusignan, S. (2019). Artificial Intelligence in Primary Health Care: Perceptions, Issues, and Challenges. Yearbook of medical informatics, 28(1), 41–46. https://doi.org/10.1055/s-0039-1677901

Liu, N., Shapira, P., & Yue, X. (2021). Tracking developments in artificial intelligence research: constructing and applying a new search strategy. Scientometrics, 126(4), 3153–3192. https://doi.org/10.1007/s11192-021-03868-4

Ntoutsi, E, Fafalios, P, Gadiraju, U, et al. Bias in data-driven artificial intelligence systems—An introductory survey. WIREs Data Mining Knowl Discov. 2020; 10:e1356. https://doi.org/10.1002/widm.1356 ry. 10. 10.1002/widm.1356.

Vaid, S., Kalantar, R. & Bhandari, M. Deep learning COVID-19 detection bias: accuracy through artificial intelligence. International Orthopaedics (SICOT) 44, 1539–1542 (2020). https://doi.org/10.1007/s00264-020-04609-7

Kaissis, G.A., Makowski, M.R., Rückert, D. et al. Secure, privacy-preserving and federated machine learning in medical imaging. Nat Mach Intell 2, 305–311 (2020). https://doi.org/10.1038/s42256-020-0186-1

Sri S., Ferry Fadzlul R., Muhammad N., Muhammad R., Kresna F., Rusni M., Artificial intelligence in healthcare: opportunities and risk for future, Gaceta Sanitaria, Volume 35, Supplement 1, 2021, Pages S67-S70, ISSN 0213-9111.

Manne, Ravi & Kantheti, Sneha. (2021). Application of Artificial Intelligence in Healthcare: Chances and Challenges. Current Journal of Applied Science and Technology. 40. 78-89. 10.9734/CJAST/2021/v40i631320.

McNally R, Alborz A. Developing methods for systematic reviewing in health services delivery and organization: an example from a review of access to health care for people with learning disabilities. Part 1. Identifying the literature. Health Info Libr J 2004;21:182–92. 10.1111/j.1471-1842.2004.00512.x

Ye K., Liu Y., Xu G., Xu CZ. (2018) Fault Injection and Detection for Artificial Intelligence Applications in Container-Based Clouds. In: Luo M., Zhang LJ. (eds) Cloud Computing – CLOUD 2018. CLOUD 2018. Lecture Notes in Computer Science, vol 10967. Springer, Cham. https://doi.org/10.1007/978-3-319-94295-7_8

Molnár-Gábor F. (2020) Artificial Intelligence in Healthcare: Doctors, Patients and Liabilities. In: Wischmeyer T., Rademacher T. (eds) Regulating Artificial Intelligence. Springer, Cham. https://doi.org/10.1007/978-3-030-32361-5_15

Zielke T. (2020) Is Artificial Intelligence Ready for Standardization?. In: Yilmaz M., Niemann J., Clarke P., Messnarz R. (eds) Systems, Software and Services Process Improvement. EuroSPI 2020. Communications in Computer and Information Science, vol 1251. Springer, Cham. https://doi.org/10.1007/978-3-030-56441-4_19 Russia, Evgeniy. (2020).

Standardization of Artificial Intelligence for the Development and Use of Intelligent Systems. Advances in Wireless Communications and Networks. 6. 1. 10.11648/j.awcn.20200601.11.

Coghlan, S. Robots and the Possibility of Humanistic Care. Int J of Soc Robotics (2021). https://doi.org/10.1007/s12369-021-00804-7

Martani, A., Geneviève, L.D., Poppe, C. et al. Digital pills: a scoping review of the empirical literature and analysis of the ethical aspects. BMC Med Ethics 21, 3 (2020). https://doi.org/10.1186/s12910-019-0443-1

Neri, E., Coppola, F., Miele, V. et al. Artificial intelligence: Who is responsible for the diagnosis?. Radiol med 125, 517–521 (2020). https://doi.org/10.1007/s11547-020-01135-9

Recibido: 2021-12-15
Aceptado: 2022-02-08
Publicado: 2022-03-30
Cómo citar
[1]
F. R. Ishengoma, «Inteligencia Artificial en Salud Digital: Cuestiones y dimensiones de los aspectos éticas», Innov. softw., vol. 3, n.º 1, pp. 81-108, mar. 2022.
Sección
Artículos de revisión