TY - GEN
T1 - Implementaciones contemporáneas de Deep Learning en el sector salud
T2 - 5th LACCEI International Multiconference on Entrepreneurship, Innovation and Regional Development - Entrepreneurship with Purpose: Social and Technological Innovation in the Age of AI, LEIRD 2025
AU - Quispe Arqque, Dario Ruben
AU - Sierra-Liñan, Fernando
AU - Moquillaza Henríquez, Santiago Domingo
N1 - Publisher Copyright:
© LEIRD 2025.All rights reserved.
PY - 2025
Y1 - 2025
N2 - The growing saturation of healthcare systems, due to operational overload and the complexity of data management, has driven interest in more efficient technological solutions. This systematic review aimed to identify how Deep Learning models have been applied in the medical field recently. Forty scientific articles indexed in Scopus, a database recognized for its high rigor and academic prestige, were analyzed, selected using the PRISMA protocol. The results showed that the most studied anatomical areas were the respiratory system (16%), endocrine (15%), and musculoskeletal (13%). The most frequently addressed clinical tasks included classification (47.5%) and prediction (32.5%), with recurring specialties such as oncology and radiology in both categories. The most frequently used model families were CNN (32.5%), ResNet (32.5%), and specialized models (32.5%), applied primarily to medical images. A growing interest was also identified in more advanced architectures and the use of diverse clinical data. These findings provide a current overview of the field and open the way to new opportunities to develop more scalable and adaptable solutions in the healthcare sector.
AB - The growing saturation of healthcare systems, due to operational overload and the complexity of data management, has driven interest in more efficient technological solutions. This systematic review aimed to identify how Deep Learning models have been applied in the medical field recently. Forty scientific articles indexed in Scopus, a database recognized for its high rigor and academic prestige, were analyzed, selected using the PRISMA protocol. The results showed that the most studied anatomical areas were the respiratory system (16%), endocrine (15%), and musculoskeletal (13%). The most frequently addressed clinical tasks included classification (47.5%) and prediction (32.5%), with recurring specialties such as oncology and radiology in both categories. The most frequently used model families were CNN (32.5%), ResNet (32.5%), and specialized models (32.5%), applied primarily to medical images. A growing interest was also identified in more advanced architectures and the use of diverse clinical data. These findings provide a current overview of the field and open the way to new opportunities to develop more scalable and adaptable solutions in the healthcare sector.
KW - CNN
KW - Deep learning
KW - Health
KW - Medical imaging.
KW - ResNet
UR - https://www.scopus.com/pages/publications/105032466452
U2 - 10.18687/LEIRD2025.1.1.337
DO - 10.18687/LEIRD2025.1.1.337
M3 - Contribución a la conferencia
AN - SCOPUS:105032466452
T3 - Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology
BT - Proceedings of the 5th LACCEI International Multiconference on Entrepreneurship, Innovation and Regional Development - Entrepreneurship with Purpose
A2 - Larrondo Petrie, Maria M.
A2 - Texier, Jose
A2 - Rivas Matta, Rodolfo Andr�s
Y2 - 1 December 2025 through 3 December 2025
ER -