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Neural networks experienced great deal of success in many domains of machine intelligence. In tasks such as object detection, speech recognition or natural language processing is performance of neural networks close to that of human. This allows penetration of neural networks in many domains. The medicine is one of the domains that can successfully harvest methodological advances in neural networks. Medical personnel has to deal with huge amount of data that are used for patients’ diagnosis, monitoring and treatment. Application of neural networks in diagnosis and decision support systems have proven to add more objectivity to diagnosis, allow for quicker and more accurate decision and provide more personalized treatment. In this brief review we describe several main architectures of neural networks together with their applications. We provide description of convolutional neural networks, auto-encoders and recurrent neural networks together with their applications such as medical image segmentation, processing of electrocardiogram for arrhythmia detection and many others.

Synthesis and characterization of the graphene hydrogels with three different metallic nanoparticles, that is Au, Ag and Cu, respectively is presented. Synthesized in a one-pot approach graphene hydrogels with embedded metallic nanoparticles were tested as heterogeneous catalysts in a model reaction of 4-nitrophenol reduction. The highest activity was obtained for graphene hydrogel with Cu nanoparticles and additional reaction of methylene blued degradation was evaluated using this system. The obtained outstanding catalytic activity arises from the synergistic effect of graphene and metallic nanoparticles. The hydrogel form of the catalyst benefits in the easiness in separation from the reaction mixture (for example using tweezers) and reusability.