Villa-Velasco S, Fortoul TI

Language: Spanish
References: 9
Page: 53-56
PDF size: 207.20 Kb.

ABSTRACT

The applications of artificial intelligence, in medicine in recent years, have grown exponentially, as well as its ethical challenges. The use of large data sets in conjunction with real-time diagnostic capabilities has been demonstrated to engender a reduction in waiting times and an enhancement in decision-making processes. This represents a significant benefit of the tool under consideration. It is evident that, due to its foundation on algorithms, AI possesses the capacity to instigate biases that may unwittingly perpetuate existing disparities within the healthcare sector.

REFERENCES

1. Dankwa-Mullan I. Health equity and ethical considerationsin using artificial intelligence in public health andmedicine. Prev Chronic Dis. 2024;21:E64.

2. Romero-Brufau S, Wyatt KD, Boyum P, Mickelson M,Moore M, Cognetta-Rieke C. Implementation of artificialintelligence-based clinical decision support to reducehospital readmissions at a regional hospital. Appl ClinInform. 2020;11(4):570-7.

3. Rashid J, Batool S, Kim J, Wasif Nisar M, Hussain A, JunejaS, et al. An augmented artificial intelligence approachfor chronic diseases prediction. Front Public Health.2022;10:860396.

4. Lanzagorta OD, Carrillo PD, Carrillo ER. Inteligenciaartificial en medicina: presente y futuro. Gac Med Mex.2023;64(3):249-56.

5. Johnson KB, Wei WQ, Weeraratne D, Frisse ME, MisulisK, Rhee K, et al. Precision medicine, AI, and the future ofpersonalized health care. Clin Transl Sci. 2021;14(1):86-93.

6. Albizua J, Pacheco P. Ethical considerations about artificialintelligence (AI) use in medical practice. Open RespirArch. 2025;7(1):100383.

7. García LA, Girón LF, Rosell D. La integración de la inteligenciaartificial en la atención médica: desafíos éticos yde implementación. Univ Med. 2023;64(3):1-10.

8. Nazer LH, Zatarah R, Waldrip S, Ke JXC, MoukheiberM, Khanna AK, et al. Bias in artificial intelligence algorithmsand recommendations for mitigation. PLOS DigitHealth. 2023;2(6):e0000278.

9. Mann H. Do all AI systems need to be explainable? StanfordSoc Innov Rev. 2023 Nov 15.