Focusing on real-time biomechanical feedback, which is still a scarce application nowadays, specific areas are identified as useful in this arena, such as 3D-motion capture, anthropometry, biomechanical modelling, sensing technology, and artificial intelligence. As a synthesis, a two-chain body model integrates the inertial measurement units with deep learning technology, and the availability of massive data seems to be the key for this scheme yielding an advantageous alternative to existing and conventional methods. Cardiac signals are always strongly present in the machine learning literature. In [15], the early detection of lethal arrhythmia ventricular fibrillation is addressed from electrocardiographic signals, with the hard-to-achieve real time requirement. Time-frequency representations are used to yield an image representation of the cardiac bioelectric potentials, thus avoiding the need for previous feature extraction and selection methods before feeding this input into a combination of classifiers. In [16], classification of heart sound signals from phonocardiographic measurements obtained in digital stethoscopes is addressed by using one normal and five abnormal categories. Features are extracted from their cepstral coefficients and their wavelet transform, and they are fed to a deep neural network, exhibiting competitive accuracy. Medical images: Deep learning techniques have yielded a number of advantages when used with multimedia images. It is not surprising that their use for medical images is being increasingly explored and gaining in sophistication and use specialization. In [17], the early detection of atherosclerotic plaque rupture in sudden coronary deaths is addressed in terms of thin cup fibroatheroma or vulnerable plaque detection from virtual histology intravascular ultrasound images, with geometric and texture features, and using different classifiers for this purpose including back-propagation neural networks, nearest neighbors, and support vector machines). In [18], an automatic system for fluorescence intensity classification is designed to support the autoimmune diagnostics with human epithelial cells from indirect immuno-fluorescence indirect images, using convolutional neural networks and support vector machines. In [19], skull stripping in brain magnetic resonance imaging is designed as an alternative to the time-consuming manual segmentation task by using a 3-D U-net for automatic segmentation of the full brain with satisfactory results. In [20], early detection of polyps are a convenient target to prevent colorectal cancer. Aiming to reduce false negatives during manual inspection and support screening tests, convolutional neural networks are trained in this work to mark the regions, and features are reinforced with transfer learning and fine tuning among databases. Technical shared elements: Some technical considerations have a transverse interest for many applications of the techniques targeted in this special issue. The input space and its expression in terms of feature extraction and selection, the data imbalance, and other topics, should devote specific attention and benefit from development of new approaches and by adequate adaptation of existing ones in other fields. In [21], novelty detection is addressed in terms of deep autoencoders with density based clustering by sending the results to a density-based cluster, so that points not involved in any group are considered as novelties. This represents a likely convenient approach to diagnostic formulations from patient data in those situations where labels are not so easy to obtain. In [22], data imbalance is analyzed in terms of its impact on machine learning algorithms, based on the analysis of patient data from electronic health records. The inclusion of multi-labeling tasks in allowing to give several diagnoses for each patient is a natural requirement which degrades the performance of most of the learning machines, and it is alleviated here with the use of regularized ensembles.