Inteligencia artificial en salud: entre la eficiencia tecnológica y el derecho a la atención humana

Autores/as

Palabras clave:

atención humanizada; ética; inteligencia artificial; medicina; salud

Resumen

Introducción: La inteligencia artificial se ha consolidado como una tecnología clave en la transformación de los sistemas de salud, generando mejoras en eficiencia, precisión y capacidad de respuesta, pero también ha planteado importantes desafíos éticos relacionados con equidad, autonomía y humanización de la atención.

Objetivo: Sintetizar y analizar la evidencia disponible sobre las aplicaciones de la inteligencia artificial en salud y los dilemas éticos asociados entre la eficiencia tecnológica y el derecho a la atención humana.

Métodos: Se realizó una revisión sistemática siguiendo las directrices PRISMA 2020 en las bases de datos Scopus y Web of Science, con estudios publicados entre 2021 y 2026. Tras el proceso de selección y evaluación de calidad metodológica, se incluyeron 27 estudios para la síntesis cualitativa.

Resultados: Las principales aplicaciones de la inteligencia artificial se concentran en el diagnóstico asistido, la predicción de riesgos, la medicina personalizada, el monitoreo remoto y la automatización de procesos, generando mejoras significativas en eficiencia operativa y calidad clínica. Sin embargo, se identificaron dilemas éticos relevantes, como el sesgo algorítmico, la falta de transparencia, los riesgos sobre la privacidad, la ambigüedad en la responsabilidad y la deshumanización de la atención, los cuales reflejan una tensión estructural entre eficiencia y derechos fundamentales.

Conclusiones: La evidencia respalda la implementación de estrategias como la explicabilidad, la supervisión humana continua, la mitigación de sesgos y el fortalecimiento de marcos éticos y regulatorios, como condiciones necesarias para integrar la inteligencia artificial en salud de manera segura, equitativa y centrada en el paciente.

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Citas

1. Chassang G, Béranger J, Rial-Sebbag E. The Emergence of AI in Public Health Is Calling for Operational Ethics to Foster Responsible Uses. International Journal of Environmental Research and Public Health. 3 abr 2025;22(4). DOI: 10.3390/ijerph22040568.

2. Poon EG, Lemak CH, Rojas JC, Guptill J, Classen D. Adoption of artificial intelligence in healthcare: survey of health system priorities, successes, and challenges. J Am Med Inform Assoc. 1 jul 2025;32(7):1093-100. DOI: 10.1093/jamia/ocaf065.

3. Tang L, Li J, Fantus S. Medical artificial intelligence ethics: A systematic review of empirical studies. DIGITAL HEALTH. 1 ene 2023;9:20552076231186064. DOI: 10.1177/20552076231186064.

4. Giansanti D. Revolutionizing Medical Imaging: The Transformative Role of Artificial Intelligence in Diagnostics and Treatment. Diagnostics. 17 jun 2025;15(12). DOI: 10.3390/diagnostics15121557.

5. OPS. Organización Panamericana de la Salud [Internet]. 2026 [citado 2026 mar 30]. Derechos Humanos y Salud. Disponible en: https://www.paho.org/es/temas/derechos-humanos-salud

6. ONU. United Nations [Internet]. United Nations; 1948 [citado 2026 mar 30]. La Declaración Universal de los Derechos Humanos. Disponible en: https://www.un.org/es/about-us/universal-declaration-of-human-rights

7. ONU. OHCHR [Internet]. [citado 2026 mar 30]. Pacto Internacional de Derechos Económicos, Sociales y Culturales. Disponible en: https://www.ohchr.org/es/instruments-mechanisms/instruments/international-covenant-economic-social-and-cultural-rights

8. Smith H, Ives J. Developing professional ethical guidance for healthcare AI use (PEG-AI): an attitudinal survey pilot. AI & Soc. 1 oct 2025;40(7):5439-55. DOI: 10.1007/s00146-025-02276-z.

9. Cross JL, Choma MA, Onofrey JA. Bias in medical AI: Implications for clinical decision-making. PLOS Digital Health. 2024 nov 7;3(11):e0000651. DOI: 10.1371/journal.pdig.0000651.

10. Singh S, Singh PK, Kumar R, Vaidyar R, Singh S, Singh PK, et al. Addressing Bias, Privacy, Security, and Patient Autonomy in Artificial Intelligence (AI)-Driven Healthcare: A Review of Current Guidelines. Cureus. 2026 feb 20;18. DOI: 10.7759/cureus.103999.

11. Balasubramaniam N, Kauppinen M, Rannisto A, Hiekkanen K, Kujala S. Transparency and explainability of AI systems: From ethical guidelines to requirements. Information and Software Technology. 2023 jul 1;159:107197. DOI: 10.1016/j.infsof.2023.107197.

12. UNESCO. Artificial Intelligence and emerging technologies [Internet]. 2026 [citado 2026 mar 19]. Ethics of Artificial Intelligence-AI. Disponible en: https://www.unesco.org/en/artificial-intelligence/recommendation-ethics

13. Chinta SV, Wang Z, Palikhe A, Zhang X, Kashif A, Smith MA, et al. AI-Driven Healthcare: A Review on Ensuring Fairness and Mitigating Bias [Internet]. arXiv; 2025 [citado 2026 mar 19]. Disponible en: http://arxiv.org/abs/2407.19655 DOI: 10.48550/arXiv.2407.19655.

14. Carrera-Alarcón J, Silva-Sánchez C. Bioethical perspectives on dehumanization in nursing care in the context of artificial intelligence. SAP Multidisciplinary Open. 2026 mar 4;4:257. DOI: 10.62486/mo2026257.

15. Ihaddouchen I, Buijsman S, Pozzi G, Sande D van de, Reis AA, Townsend R, et al. Responsible artificial intelligence in healthcare: a systematic review on the use of ethical principles in the development and deployment of artificial intelligence. BMJ Digit Health. 2025 nov 13;1(1). DOI: 10.1136/bmjdhai-2025-000086 PubMed PMID: 10.1136/bmjdhai-2025-000086.

16. Moldovan AMN, Vescan A, Grosan C. Healthcare Bias in AI: A Systematic Literature Review. In. 2026 [citado 2026 mar 20]. p. 835–42. Disponible en https://www.scitepress.org/Link.aspx?doi=10.5220/0013480300003928

17. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021 mar 29;372:n71. DOI:10.1136/bmj.n71 PubMed PMID: 33782057.

18. Osborne A, Usani K. A fairness-aware machine learning framework for maternal health in Ghana: integrating explainability, bias mitigation, and causal inference for ethical AI deployment. BioData Mining. 2025 dic 5;19(1):3. DOI: 10.1186/s13040-025-00505-1.

19. Martinho A, Kroesen M, Chorus C. A healthy debate: Exploring the views of medical doctors on the ethics of artificial intelligence. Artificial Intelligence in Medicine. 2021 nov 1;121:102190. DOI: 10.1016/j.artmed.2021.102190-

20. Schilder MB, Keyser A, Hees S van, Sbrizzi A, Boon WPC. Anticipating Moral and Economic Considerations, Opportunities, and Potential Frictions for AI in Medical Imaging: Multistakeholder Cocreation Study. Journal of Medical Internet Research. 2026 feb 25;28(1):e83407. DOI: 10.2196/83407

21. Alam MS, Rai P, Tiwari RK. Artificial Intelligence and Machine Learning in Precision Medicine: Applications, Challenges, and Ethical Perspectives. Journal of Computers, Mechanical and Management. 2025 jun 30;4(3):1–6. DOI: 10.57159/jcmm.4.3.25205.

22. İçen S, Köken AH. Artificial intelligence guidance in ethically challenging clinical scenarios in child and adolescent psychiatry: a qualitative study in the context of Turkiye. BMC Med Ethics. 2025 dic 30;26(1):183. DOI: 10.1186/s12910-025-01323-0.

23. Gundlack J, Negash S, Thiel C, Buch C, Schildmann J, Unverzagt S, et al. Artificial Intelligence in Medical Care – Patients’ Perceptions on Caregiving Relationships and Ethics: A Qualitative Study. Health Expectations. 2025;28(2):e70216. DOI: 10.1111/hex.70216.

24. Landau AY, Blanchard A, Kulkarni P, Althobaiti S, Idnay B, Patton DU, et al. Designing a Machine Learning-Based Model Integrating Clinical Orders for Child Abuse and Neglect Identification with Focus on Reducing Socio-economic Bias. Int Journal on Child Malt. 2025 jun 1;8(2):209–25. DOI: 10.1007/s42448-025-00223-5.

25. Khan UA, Alamäki A. Designing an Ethical and Secure Pain Estimation System Using AI Sandbox for Contactless Healthcare. International Journal of Online and Biomedical Engineering (iJOE). 2023 oct 25;19(15):166–201. DOI: 10.3991/ijoe.v19i15.43663.

26. Smith LA, Cahill JA, Lee JH, Graim K. Equitable machine learning counteracts ancestral bias in precision medicine. Nat Commun. 2025 mar 10;16(1):2144. DOI:10.1038/s41467-025-57216-8.

27. Chis DI, Dumitrache I. Ethical AI Triplet: A Framework for Stress-Testing Fairness in Digital Twins in Healthcare. Journal of Control Engineering and Applied Informatics. 2025 sep 25;27(3):118–26. DOI: 10.61416/ceai.v27i3.9745.

28. Elgin CY, Elgin C. Ethical implications of AI-driven clinical decision support systems on healthcare resource allocation: a qualitative study of healthcare professionals’ perspectives. BMC Med Ethics. 2024 dic 21;25(1):148. DOI: 10.1186/s12910-024-01151-8.

29. Olawade DB, Clement David-Olawade A, Aderinto N, Wada OZ. Ethical oversight of Artificial Intelligence in Nigerian Healthcare: A qualitative analysis of ethics committee members’ perspectives on integration and regulation. International Journal of Medical Informatics. 2026 feb 1;206:106140. DOI: 10.1016/j.ijmedinf.2025.106140.

30. Nouis SC, Uren V, Jariwala S. Evaluating accountability, transparency, and bias in AI-assisted healthcare decision- making: a qualitative study of healthcare professionals’ perspectives in the UK. BMC Med Ethics. 2025 jul 8;26(1):89. DOI: 10.1186/s12910-025-01243-z.

31. Vrudhula A, Kwan AC, Ouyang D, Cheng S. Machine Learning and Bias in Medical Imaging: Opportunities and Challenges. Circ Cardiovasc Imaging. 2024 feb;17(2):e015495. DOI: 10.1161/CIRCIMAGING.123.015495 PubMed PMID: 38377237; PubMed Central PMCID: PMC10883605.

32. Vicente L, Matute H, Fregosi C, Cabitza F. Machine learning systems as mentors in human learning: A user study on machine bias transmission in medical training. International Journal of Human-Computer Studies. 2025 abr 1;198:103474. DOI: 10.1016/j.ijhcs.2025.103474.

33. Rony MKK, Numan SM, Akter K, Tushar H, Debnath M, Johra F tuj, et al. Nurses’ perspectives on privacy and ethical concerns regarding artificial intelligence adoption in healthcare. Heliyon. 2024 sep 15;10(17). DOI: 10.1016/j.heliyon.2024.e36702.

34. Lopez-Ramos LM, Pluktaite G, Bui CKT, Amann J, Haven T, Madai VI, et al. Operationalizing AI ethics in medicine a co-creation workshop study. BMC Med Ethics. 2025 oct 29;26(1):150. DOI:10.1186/s12910-025-01317-y.

35. Shin H, De Gagne JC, Kim SS, Hong M. The Impact of Artificial Intelligence-Assisted Learning on Nursing Students’ Ethical Decision-making and Clinical Reasoning in Pediatric Care: A Quasi-Experimental Study. CIN: Computers, Informatics, Nursing. 2024 oct;42(10):704. DOI:10.1097/CIN.0000000000001177.

36. Abbott EE, Rehman T, Rosania A, Lum DL, Taylor TB, Kirk AJ, et al. Understanding and Addressing Bias in Artificial Intelligence Systems: A Primer for the Emergency Medicine Physician. JACEP Open. 2026 feb 1;7(1):100311. DOI: 10.1016/j.acepjo.2025.100311.

37. Maas J, Franssen S, Petkovic M, Cardona Cano S, Dingemans AE, van Oosterzee AM, et al. Artificial Intelligence in Eating Disorder Treatment: A Qualitative Analysis of Clinical Opportunities, Barriers, and Ethical Considerations From Multi-Disciplinary Focus Groups. International Journal of Eating Disorders. 2026;59(2):299-310. DOI:10.1002/eat.24579.

38. Ordóñez SAC, Castro R, Celi LA, Reyes RD, Engelmann J, Ercole A, et al. Beyond overconfidence: Embedding curiosity and humility for ethical medical AI. PLOS Digital Health. 2026 ene 5;5(1):e0001013. DOI: 10.1371/journal.pdig.0001013.

39. Arjmandi N, Sebzari AR, Molaei F, Rezaei S, Rezaie-Yazdi M, Rezaie-Yazdi M. Clinical validation of AI-assisted contouring in prostate radiation therapy treatment planning: Highlighting automation bias and the need for standardized quality assurance. Journal of Applied Clinical Medical Physics. 2026;27(1):e70425. DOI:10.1002/acm2.70425.

40. Fatima M, Pachauri P, Akram W, Parvez M, Ahmad S, Yahya Z. Enhancing retinal disease diagnosis through AI: Evaluating performance, ethical considerations, and clinical implementation. Informatics and Health. 2024 sep 1;1(2):57-69. DOI: 10.1016/j.infoh.2024.05.003.

41. Redrup Hill E, Mitchell C, Brigden T, Hall A. Ethical and legal considerations influencing human involvement in the implementation of artificial intelligence in a clinical pathway: A multi-stakeholder perspective. Front Digit Health. 2023 mar 13;5. DOI:10.3389/fdgth.2023.1139210.

42. Nwebonyi N, McKay F. Exploring bias risks in artificial intelligence and targeted medicines manufacturing. BMC Med Ethics. 2024 oct 17;25(1):113. DOI:10.1186/s12910-024-01112-1.

43. Buslón N, Cirillo D, Rios O, Rosario SP del. Exploring Gender Bias in AI for Personalized Medicine: Focus Group Study With Trans Community Members. Journal of Medical Internet Research. 2025 jul 29;27(1):e72325. DOI:10.2196/72325.

44. Kahraman F, Aktas A, Bayrakceken S, Çakar T, Tarcan HS, Bayram B, et al. Physicians’ ethical concerns about artificial intelligence in medicine: a qualitative study: “The final decision should rest with a human.” Front Public Health. 2024 nov 27;12. DOI:10.3389/fpubh.2024.1428396.

45. Qiu P, Zhang H, Han Y, Lei J, Zhang X. Ethical issues and coping strategies for artificial intelligence in medical research. Translational Dental Research. 2025 oct 1;1(4):100051. DOI: 10.1016/j.tdr.2025.100051.

46. Sena AGN, Schutt-Aine J, Arenas J, Akaba S. Momentos clave en el camino hacia la equidad en salud en la Organización Panamericana de la Salud. Rev Panam Salud Publica. 15 may 2023;47:e42. DOI:10.26633/RPSP.2023.42.

47. WHO. Ethics and Governance of Artificial Intelligence for Health – IAenSalud [Internet]. 2024 [citado 30 mar 2026]. Disponible en: https://iaensalud.es/docs/doc-ethics-and-governance-of-artificial-intelligence-for-health

48. Chichande XSF, Gallardo NMM, Márquez TBM, Rodriguez MCT. La ética en la inteligencia artificial desafíos y oportunidades para la sociedad moderna. Sage Sphere International Journal. 6 de septiembre de 2024;1(1):1-24. DOI:10.63688/78e7m802.

49. Mitelman CZ. Inteligencia Artificial en el ámbito del derecho de la salud. Revista Derecho y Salud | Universidad Blas Pascal. 2024;8(9):125-36. DOI:10.37767/2591-3476(2024)08

50. Ratti E, Morrison M, Jakab I. Ethical and social considerations of applying artificial intelligence in healthcare—a two-pronged scoping review. BMC Med Ethics. 2025 may 27;26(1):68. DOI:10.1186/s12910-025-01198-1.

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Publicado

2026-10-07

Cómo citar

Santillán Castillo, I. de L., Moreno Tapia, C. B., Salgado Oviedo, G. S., Procel Hidalgo, K. J., & Torres Tarabata, B. R. (2026). Inteligencia artificial en salud: entre la eficiencia tecnológica y el derecho a la atención humana. Anales De La Academia De Ciencias De Cuba, 16, e3254. Recuperado a partir de https://revistaccuba.sld.cu/index.php/revacc/article/view/3254

Número

Sección

Ciencias Biomédicas