Implementación y utilidad de la Inteligencia Artificial en las Ciencas de la Salud: Revisión Bibliográfica

Bryan Andrés Orellana Tapia; Antonella Fernanda Gallegos Mora; Marco Ruben Orellana Barros

doi:10.26871/killcanasalud.v7i1.1297

Authors

Bryan Andrés Orellana Tapia Universidad de la Rioja
Antonella Fernanda Gallegos Mora Medico
Marco Ruben Orellana Barros Clínica del Valle

DOI:

https://doi.org/10.26871/killcanasalud.v7i1.1297

Keywords:

inteligencia artificial, medicina, ciencias de la salud

Abstract

Introduction: Artificial intelligence (AI) has revolutionized medicine and oncology by improving diagnosis, treatment, and surgery. AI analyzes large volumes of data, identifies patterns, and enhances clinical accuracy. In oncology, AI and precision medicine enable a personalized therapeutic approach. In medical imaging, AI enhances the diagnosis of diseases such as lung cancer.

Objective: To analyze the implementation and usefulness of artificial intelligence in health sciences.

Methodology: A non-experimental, descriptive study was conducted using a literature review approach. Information was searched in PubMed, ScienceDirect, and Scielo using Boolean operators derived from DeCS and MESH.

Development: AI in healthcare utilizes machine learning algorithms and virtual assistants to provide accessible medical care. In surgery, AI complements robotic surgery and enhances precision. Although it presents challenges such as data quality and biases, ethical and technical aspects must be addressed. Privacy, security, and adequate medical training are essential.

Conclusion: AI transforms healthcare by improving diagnosis, treatment personalization, and access to medical care. Challenges must be addressed to harness its potential for the benefit of patients and society.

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References

Buchanan B, Shortliffe E. Rule-based expert systems: The MYCIN experiments of the Stanford Heuristic Programming Project. Addison-Wesley; 1983.

Mitchell T, Hutchinson R, Coiera R. A framework for understanding the nature of information in medical decision making. JAMIA. 1997;4(3):200-13.

Gong J, Liu C, Zhuang H. AI-based intelligent analysis of combination therapy in pancreatic cancer. Nat Commun. 2019;10(1):1-12.

Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44-56.

Lee JR. Anesthetic considerations for robotic surgery. Korean J Anesthesiol. 2014;66(1):3-11.

Collins FS, Varmus H. A New Initiative on Precision Medicine. New England Journal of Medicine. 2015;372(9):793-5.

Schwaederle M, Zhao M, Lee JJ, Eggermont AM, Schilsky RL, Mendelsohn J, et al. Impact of Precision Medicine in Diverse Cancers: A Meta-Analysis of Phase II Clinical Trials. JCO. 2015;33(32):3817-25.

Patel NM, Michelini VV, Snell JM, Balu S, Hoyle AP, Parker JS, et al. Enhancing Next-Generation Sequencing-Guided Cancer Care Through Cognitive Computing. Oncologist. 2018;23(2):179-85.

Smith A, Jones B, Johnson C. The role of artificial intelligence in medical imaging: an overview. Vancouver Journal of Medical Sciences. 2019;21(2):67-72.

Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115-8.

Bickmore TW, Schulman D, Sidner C. Automated Interventions for Multiple Health Behaviors Using Conversational Agents. Patient Educ Couns. 2013;92(2):142-8.

Marescaux J, Leroy J, Rubino F, Smith M, Vix M, Simone M, et al. Transcontinental robot-assisted remote telesurgery: feasibility and potential applications. Ann Surg. 2002;235(4):487-92.

Klein S, Staring M, Murphy K, Viergever M, Pluim J. Elastix: a toolbox for intensity-based medical image registration. IEEE Trans Med Imaging. 2010;29(1):196-205.

Behrmann J, Etmann C, Boskamp T, Casadonte R, Kriegsmann J, Maaß P. Deep learning for tumor classification in imaging mass spectrometry. Bioinformatics. 2018;34(7):1215-23.

Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, et al. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA. 2016;316(22):2402-10.

Sanchez A, Andrades P, Herrero F. Use of drones for the detection of infected herds in livestock

animals. J Med Syst. 2016;40(4):93.

Chiu A, Chang L, Birkett D, Babayan R. Technological advances in urology: robots and nanotechnology. Urology. 2003;62(2):174-9.

Johnson S. Ethical considerations in robotic surgery. Cancer J. 2013;19(2):130-3.

Reddy A, Ramanathan K, Hamarneh G. Challenges and Opportunities in Medical Image Analysis using Artificial Intelligence. Vancouver Medical Imaging Journal. 2020;29(3):115-22.

Torkamani A, Andersen KG, Steinhubl SR, Topol EJ. High-Definition Medicine. Cell. 2017;170(5):828-43.

O’Neill P, Ryan-Mosley T, Johnson P. A flood of coronavirus apps are tracking us. Now it’s time to keep track of them. MIT Technol Rev. 2020;528(24):12-4