Implementaciones contemporáneas de Deep Learning en el sector salud: una revisión de la literatura

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.