Inteligencia artificial en radiología: beneficios, desafíos y aplicaciones actuales

Lorena Andrea Luque Ayala; Sofia Alejandra Rojas Rojas; Olga Lucia Arciniegas Ramirez

doi:10.22490/26194759.11940

La Edición Especial FIKLiv 2026 de la Revista Biociencias reúne investigaciones y experiencias interdisciplinarias en cinco ejes: 1) salud, territorio, interculturalidad y transformación social; 2) determinantes, prevención, ambiente y cuidado de la salud; 3) formación, innovación y nuevas estrategias para el cuidado; 4) inteligencia artificial, salud digital, bioética y gestión basada en datos; y 5) IA, educación, ciencia abierta y futuro de la academia.

Vol. 9 No. 1 (2026): EDICIÓN ESPECIAL FIKLIV

Published 2026-08-18

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

III.METASALUD

Artificial intelligence in radiology: benefits, challenges and current applications

DOI: https://doi.org/10.22490/26194759.11940

Lorena Andrea Luque Ayala Universidad Nacional Abierta y a Distancia

Sofia Alejandra Rojas Rojas Universidad Nacional Abierta y a Distancia

Olga Lucia Arciniegas Ramirez Universidad Nacional Abierta y a Distancia

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Abstract
References

Introducción: Artificial intelligence (AI) is taking on an increasingly relevant role in radiology, especially in the analysis of medical images and in supporting diagnostic processes. Objetive: The present work aimed to analyze the current applications, benefits, and main challenges associated with the use of artificial intelligence in radiology. A narrative-exploratory review was developed, with a qualitative focus, based on scientific literature published mainly between 2021 and 2025 and consulted in academic sources related to health sciences and radiology. Methodology: The selected information was analyzed comparatively and organized into categories related to diagnostic applications, process automation, workflow optimization, clinical validation, data biases, and ethical considerations. Results: The findings show that AI can support the detection and characterization of alterations in medical images, facilitate the prioritization of studies, automate repetitive tasks, and help optimize workflow times in radiology services. However, its implementation also poses challenges related to the quality and representativeness of the data, the validation of algorithms, technological costs, staff training, and the protection of patient information. Clonclusion: It is concluded that AI represents a supportive tool with the potential to strengthen radiological practice, provided that its incorporation is progressive, clinically validated, and accompanied by professional supervision, ethical criteria, and governance mechanisms.

keywords: Artificial intelligence, radiology, medical imaging, machine learning

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How to Cite

Luque Ayala, L. A., Rojas Rojas, S. A., & Arciniegas Ramirez, O. L. (2026). Artificial intelligence in radiology: benefits, challenges and current applications. Biociencias, 9(1). https://doi.org/10.22490/26194759.11940

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