Qualis/CAPES  B1 (2021-2024) Google Scholar   Citations: 922   |   h‑index: 13   |   i10‑index: 25   |   h5‑index: 66   |   h5‑median: 8 Impact: CUIDEN 0.107 RIC est.  SJIF 3.138 (2021)
Artificial intelligence applied to healthcare: innovation, ethics, and humanization of care
PDF (Português (Brasil))
PDF

Keywords

Artificial Intelligence
Innovation in Healthcare
Humanization of Care
Ethics in Nursing
Patient Safety

How to Cite

1.
Silva PCP de O da, Assad LG, Oliveira JA de, Higa GJ de O, Santos MO dos, Jeronimo JSL, Barros N dos SM de, Teixeira I de P, Chicharo SCR, Duarte AC da S. Artificial intelligence applied to healthcare: innovation, ethics, and humanization of care. Glob Acad Nurs [Internet]. 2026 May 18 [cited 2026 May 19];7(Spe.1):e552. Available from: https://globalacademicnursing.com/index.php/globacadnurs/article/view/689

Abstract

The aim was to analyze the impact of AI on healthcare, highlighting innovations, ethical challenges, and the humanization of care. This is a reflective study guided by Grant and Booth's typology. A search was conducted in the Medline/PubMed, LILACS, and SciELO databases using controlled terms and Boolean operators. After screening and applying inclusion and exclusion criteria, the final corpus was submitted to Bardin's Content Analysis, structured in the phases of pre-analysis, material exploration, and results treatment. Two analytical categories emerged: "The transformative potential of AI in reorienting healthcare practices" and "Ethical dilemmas and humanization as a pillar of care in the digital age." The potential of AI in precision diagnoses, personalized therapies, and optimized management was verified, but significant ethical dilemmas were revealed, such as data privacy, algorithmic biases, and accountability issues, which threaten equity and the centrality of human care. AI is revolutionary when used as a support tool, amplifying empathy and active listening. The incorporation of models such as IAC (Information, Evaluation, Consent) and explainability frameworks (XAI) translates ethical principles into implementable steps, ensuring fair, efficient, and human-centered systems.

https://doi.org/10.5935/2675-5602.20200552
PDF (Português (Brasil))
PDF

References

Moraes JJ, Barbosa MCMA, Vieira PHC, Costa ACMSF da, Romeiro ET, Terebinto DV, et al. Impacto da tecnologia de inteligência artificial na medicina diagnóstica. Rev Ibero-Am Humanidades Ciênc Educ. 2023;9(7):1303–14. DOI: 10.51891/rease.v9i7.10676

Raulin MLF, Angel DJ. Inteligência artificial na medicina: impactos e desafios. REASE. 2024;10(5):2802-15. DOI: 10.51891/rease.v10i5.14215

Alcântara HS, Almeida DM, Pinto EV. Inteligência artificial no cuidado de enfermagem: um estudo acerca do futuro da profissão. Rev Ibero-Am Humanidades Ciênc Educ. 2024;10(12):1290–305. DOI: 10.51891/rease.v10i12.17293

Adamson AS, Smith A. Machine learning and health care disparities in dermatology. JAMA Dermatol. 2018;154(11):1247–8. DOI: 10.1001/jamadermatol.2018.2348

Grant MJ, Booth A. A typology of reviews: an analysis of 14 review types and associated methodologies. Health Info Libr J. 2009;26(2):91–108. DOI: 10.1111/j.1471-1842.2009.00848.x

Bardin L. Análise de conteúdo. São Paulo: Edições 70; 2016.

Fogel AL, Kvedar JC. Artificial intelligence powers digital medicine. NPJ Digit Med. 2018;1(1):5. DOI: 10.1038/s41746-017-0012-2

Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44–56. DOI: 10.1038/s41591-018-0300-7

Liu Y, Chen Y, Wang Z, Xu C, He Z. A systematic review of deep learning in medical image analysis: from the perspective of network structure. J Healthc Eng. 2021;2021:6653549. DOI: 10.1155/2021/6653549

Reddy S, Allan S, Coghlan S, Cooper P. A governance model for the application of AI in health care. J Am Med Inform Assoc. 2020;27(3):491–7. DOI: 10.1093/jamia/ocz192

Hashimoto DA, Rosman G, Rus D, Meireles OR. Artificial intelligence in surgery: promises and perils. Ann Surg. 2018;268(1):70-6. DOI: 10.1097/SLA.0000000000002693

McKinney SM, Sieniek M, Godbole V, et al. International evaluation of an AI system for breast cancer screening. Nature. 2020;577(7788):89–94. DOI: 10.1038/s41586-019-1799-6

Kuo YL, Chen SC, Lee YJ. AI in precision oncology. Cancers (Basel). 2023;15(3):607. DOI: 10.3390/cancers15030607

Onishi S, Kuwahara T, Tajika M, et al. Artificial intelligence for body composition assessment focusing on sarcopenia. Sci Rep. 2025;15:1324. DOI: 10.1038/s41598-024-83401-8

Ribeiro AH, Ribeiro MH, Paixão GMM, Oliveira DM, Gomes PR, Canazart JA, et al. Automatic diagnosis of the 12-lead ECG using a deep neural network. Nat Commun. 2020;11(1):1760. DOI: 10.1038/s41467-020-15432-4

Frizzo M. Análise de equidade em algoritmos de IA na área da saúde: um estudo sobre viés de dados, medidas de pós-processamento e correlações de atributos [Trabalho de Conclusão de Curso]. São Paulo: Universidade Federal de São Paulo; 2023.

Morley J, Machado V, Burr C, Cowls J, Joshi I, Taddeo M, et al. The ethics of AI in health care: a mapping review. Soc Sci Med. 2020;260:113172. DOI: 10.1016/j.socscimed.2020.113172

Chen IY, Szolovits P, Ghassemi M. A new baseline for fairness in machine learning for healthcare. In: Proceedings of the 5th Machine Learning for Healthcare Conference; 2020 Aug 7-8; Virtual. p. 436-457. DOI: 10.48550/arXiv.1905.08898

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Copyright (c) 2026 Global Academic Nursing Journal

Downloads

Download data is not yet available.