Conference Agenda

This article analyzes the impact of artificial intelligence on business operational efficiency through a systematic literature review based on the PICOC methodology. A total of 219 records were collected in Scopus, of which 44 met the inclusion criteria after applying the PRISMA statement. The results identified factors limiting operational efficiency, such as lack of automation, human error and resistance to change. AI emerged as a key driver for improving productivity, automating processes and optimizing decisions, with the greatest impact in sectors such as manufacturing, logistics and finance. Companies that adopt AI show greater flexibility, cost reduction and resource optimization compared to those that do not. It is concluded that AI transforms operational efficiency, with more marked effects in sectors where automation is crucial, highlighting it as a strategic tool to improve business performance.

This study examines Artificial Intelligence (AI) models' effectiveness in detecting and classifying gastrointestinal disorders (GID) based on complex patterns and biometric data. The research highlights the impact of different AI approaches, focusing on Deep Learning (DL), Machine Learning (ML), and hybrid ML+DL models. The results show that CNN-based DL models perform exceptionally well when handling large volumes of data, achieving high accuracy, especially in identifying conditions such as polyps, ulcers, and Crohn's disease. Hybrid models that combine ML and DL architectures offer superior performance, with lower variability in results and higher diagnostic accuracy.

Abstract– This Systematic Literature Review (SLR), conducted under the PRISMA methodology, aims to identify recently proposed
artificial intelligence models for the early identification of diabetes patients through foot images. It analyzes the challenges these models
address, the Deep Learning (DL) techniques utilized, and the performance metrics applied. Additionally, the results obtained are examined,
highlighting innovations compared to classical methods. Out of 237 articles identified in SCOPUS using the PICO methodology, 23 were
selected through PRISMA. These studies explore deep learning approaches for detecting diabetic neuropathy and early signs of diabetes
through foot images. The most commonly used models include convolutional neural networks (CNN) and deep neural networks (DNN),
which stand out for addressing challenges such as image variability and quality. However, the lack of homogeneous databases for
meaningful comparisons was identified. The approaches combine CNNs with optimization algorithms and hybrid methods, achieving
accuracies ranging from 81.18%, as achieved by Inception-ResNet-v2, to 99.4%, attained by EfficientNet technology. Key innovations
include advanced preprocessing techniques and the integration of diverse datasets to improve generalization. Finally, recommendations are
proposed to optimize these models, such as developing homogeneous and standardized databases, implementing modern architectures like
EfficientNet and Inception-ResNet-v2, and exploring hybrid approaches that integrate RGB, thermal, and spectroscopic images. These
measures aim to enhance diagnostic capabilities and overcome current limitations, facilitating their clinical application.
Keywords-- Deep learning; diabetic foot; diabetes; early detection; foot images.

Abstract— The advancement in the development of mobile
technologies has revolutionized the mental health sector,
offering new therapeutic tools for patients with autism
spectrum disorder (ASD). The application of these mobile
technologies is the primary focus of this systematic literature
review (SLR), which aims to study their effectiveness in
addressing the different needs of patients. A search was
conducted using the PICO technique in the SCOPUS database,
yielding 213 articles. Subsequently, the PRISMA technique
was applied for screening and selection, resulting in the
inclusion of 23 articles for analysis. The findings highlight that
60.87% of the studies focus on improving communicative and
interactive skills, 39.13% of the developed technologies are
based on augmented reality, and 30.30% are oriented towards
assistive technologies. Artificial intelligence (AI), present in
21.74% of the studies, shows potential for personalizing and
adjusting therapies. Despite these advances, limitations were
also identified, such as the dependence on external resources,
lack of user adaptation, and economic barriers that hinder
access to these technologies. The need to design inclusive,
sustainable, and adaptable solutions is recommended,
combining innovations such as artificial intelligence (AI) with
the growing trend of augmented reality technologies, to
personalize treatments and enhance their effectiveness. In
summary, this work contributes to the academic field by
identifying current trends in mobile technologies for the
treatment of ASD, guiding future research towards more
accessible, personalized, and innovative solutions, and
promoting the integration of AI and machine learning with the
growing development of augmented reality for greater efficacy
and adaptability.

The systematic review on the use of artificial intelligence (AI) as assistive technology aims to analyze its impact on improving the quality of life of people with special abilities. Through the review of 18 articles obtained from Scopus and using the PRISMA methodology with inclusion criteria such as publications in Spanish/English and a specific focus on AI applied to various disabilities, it was found that these tools significantly enhance autonomy and mobility, fostering social inclusion. However, limitations such as accessibility and low personalization were identified. These issues are linked to specific factors like age and prior experience with technology, highlighting the need for more intuitive and easily adaptable systems. This study provides a comprehensive analysis of the subject, identifying potential challenges and proposing improvements to expand the use of these technologies, ensuring their continued contribution to the daily lives of those who need them.

This systematic literature review assessed the impact of brain tumor classification on magnetic resonance imaging using convolutional neural networks (CNN). The PRISMA strategy was used to organize and document the study selection and evaluation process. From an initial total of 448 articles identified in Scopus and Web of Science, duplicates were eliminated, and inclusion and exclusion criteria were applied, resulting in the evaluation of 103 highly relevant and quality studies. The most used architectures, including ResNet-50, VGG16 and EfficientNet-B4, were identified and analyzed, assessing their effectiveness in terms of accuracy, sensitivity and F1-score. The results showed that these architectures can achieve accuracy levels above 98%, making them effective for potential implementation in clinical settings. In addition, key pre-processing techniques, such as normalization and denoising, were highlighted as contributing to improved image quality and reduced variability. However, important barriers were identified, such as the scarcity of high-quality data and privacy constraints, which limit the generalizability and robustness of the models. The review concludes that the creation of standardized datasets and the development of innovative methodological approaches are essential to advance the clinical applicability of CNNs in brain tumor diagnosis.