An extensive amount of information is currently available to clinical specialists, ranging from details ofclinical symptoms to various types of biochemical data and outputs of imaging devices. Each type of dataprovides information that must be evaluated and assigned to a particular pathology during the diagnosticprocess. To streamline the diagnostic process in daily routine and avoid misdiagnosis, artificial intelligencemethods (especially computer aided diagnosis and artificial neural networks) can be employed. Theseadaptive learning algorithms can handle diverse types of medical data and integrate them into categorizedoutputs. In this paper, we briefly review and discuss the philosophy, capabilities, and limitations of artificialneural networks in medical diagnosis through selected examples

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Deep learning; biometrics; fingerprint; ocular recognition; convolutional neural networks; electrocardiogram Deep learning has been established in the last few years as the gold standard for data processing, achieving peak performance in image, text and audio understanding. At the same time, digital security is of utmost importance in this day and age, where everyone could get into our personal devices like cellphones or laptops, where we store our most valuable information. One of the possible ways to prevent this is via advanced and personalized security: biometrics. In this survey, it is considered how the scientific advances in the field of deep learning are applied to biometrics in order to enhance the protection of our data. Firstly, a study will be conducted on tackling ocular identification of twins using deep learning. Then, an improved method for avoiding fingerprint spoofing will be presented, thus solving this method's biggest issue. Finally, a brand new method for biometric identification is proposed based on the usage of the user's electrocardiogram. On every one of these methods, the results manage to top the standard alternative performances.