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Review
. 2025 Jan 7:7:1479855.
doi: 10.3389/frai.2024.1479855. eCollection 2024.

A systematic review of Machine Learning and Deep Learning approaches in Mexico: challenges and opportunities

José Luis Uc Castillo  1 , Ana Elizabeth Marín Celestino  2 , Diego Armando Martínez Cruz  3 , José Tuxpan Vargas  2 , José Alfredo Ramos Leal  1 , Janete Morán Ramírez  2
Affiliations
Review

A systematic review of Machine Learning and Deep Learning approaches in Mexico: challenges and opportunities

José Luis Uc Castillo et al. Front Artif Intell. .

Abstract

This systematic review provides a state-of-art of Artificial Intelligence (AI) models such as Machine Learning (ML) and Deep Learning (DL) development and its applications in Mexico in diverse fields. These models are recognized as powerful tools in many fields due to their capability to carry out several tasks such as forecasting, image classification, recognition, natural language processing, machine translation, etc. This review article aimed to provide comprehensive information on the Machine Learning and Deep Learning algorithms applied in Mexico. A total of 120 original research papers were included and details such as trends in publication, spatial location, institutions, publishing issues, subject areas, algorithms applied, and performance metrics were discussed. Furthermore, future directions and opportunities are presented. A total of 15 subject areas were identified, where Social Sciences and Medicine were the main application areas. It observed that Artificial Neural Networks (ANN) models were preferred, probably due to their capability to learn and model non-linear and complex relationships in addition to other popular models such as Random Forest (RF) and Support Vector Machines (SVM). It identified that the selection and application of the algorithms rely on the study objective and the data patterns. Regarding the performance metrics applied, accuracy and recall were the most employed. This paper could assist the readers in understanding the several Machine Learning and Deep Learning techniques used and their subject area of application in the Artificial Intelligence field in the country. Moreover, the study could provide significant knowledge in the development and implementation of a national AI strategy, according to country needs.

Keywords: Deep Learning; Machine Learning; Mexico; artificial intelligence; data science; state-of-the-art.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Brief definitions of artificial intelligence, machine learning and deep learning concepts.
Figure 2
Figure 2
Timeline chart of some relevant events in ML and DL history.
Figure 3
Figure 3
Timeline chart of some relevant events in ML and DL history.
Figure 4
Figure 4
PRISMA flow diagram of this systematic review.
Figure 5
Figure 5
Reviewed algorithms and their subject area of application in Mexico.
Figure 6
Figure 6
Publication trends on ML and DL in Mexico.
Figure 7
Figure 7
Publishing characteristics of the reviewed articles. (A) Journals. (B) Editorials.
Figure 8
Figure 8
Spatial distribution by country, based on corresponding author affiliation.
Figure 9
Figure 9
Mexican institutions that published articles related to topic.
Figure 10
Figure 10
Spatial distribution by State of the reviewed articles (n = 78).
Figure 11
Figure 11
Percentage of subject areas in the reviewed research papers.
Figure 12
Figure 12
Applied algorithms in the reviewed articles. (A) Percentage of use. (B) Artificial Neural Network types.
Figure 13
Figure 13
Analysis of applied performance metrics (PM’s). (A) Percentage distribution of individual PM’s. (B) Combination frequency of PM’s.
See this image and copyright information in PMC

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