In [15], a new pre-trained neural language model based on the BERT model [24] was introduced. This model was named CovBERT, and it was specifically designed to improve the overall review task performances on the COVID-19 literature with respect to the classic BERT model. CovBERT was pretrained on a very large corpus formed by scientific publications in the biomedical domain related to COVID-19. The CovBERT was tested on the classification task of short texts of biomedical articles. The obtained results demonstrated significant improvements. In addition, the authors also made a COVID-19 corpus available, entitled _COV-Dat-20_. The authors of [16] proposed a wearable system that takes advantage of sensors embedded in a smart device to collect data for movement identification (running, walking, falling, and daily activities) of an older adult user in real-time. To provide high efficiency in fall detection, the sensor readings were analysed using a neural network. If a fall is detected, an alert is sent though a smartphone connected via Bluetooth. The proposed system was tested in both inside and outside environments, and the results of the experiments showed that it is extremely portable and is able to provide high success rates in fall detection in terms of accuracy and loss. In [17], a noncontact system that can predict heart failure exacerbation through vocal analysis was studied and implemented. The system was designed to evaluate the voice characteristics of every patient, used to identify variations using a Machine Learning-based approach. The authors collected voice data from real hospitalised patients since their admission to a hospital, when their general status was critical, until the day of discharge, when they were clinically stable. Each patient was classified adopting the New York Heart Association Functional Classification (NYHA) classification system for heart failure in order to include them in different stages based on their clinical evolution. Different ML algorithms were tested, namely Artificial Neural Networks (ANN), Support Vector Machine (SVM), and K-Nearest Neighbours (KNN), trained on voice data. The experiments demonstrated that the KNN obtained the best results and was able to correctly classify the NYHA stages of the patients exploiting only their voice recording, with an accuracy of 0.945. In [18], a study on the copula-based approach to selecting the most important features for a Random Forest classification was used to classify a label-valued outcome. The methodology was simulated on a real dataset of COVID-19 and diabetes. In detail, based on associated copulas between these features, the authors carried out this feature selection and then embedded the selected features into a Random Forest algorithm to classify a label-valued outcome. This algorithm allowed us to select the most relevant features when the features are not necessarily connected by a linear function, and it can stop the classification when the desired level of accuracy is reached. The experimental assessment successfully applied the proposed method on a simulation study as well as a real dataset of COVID-19 and for a diabetes dataset. The study presented in [19] focused on a new unsupervised algorithm that adapts to every patient using the heart rate and morphological features of the ECG beats to classify beats between supraventricular origin and ventricular origin in order to predict arrhythmia. The results of the experiments performed obtained F-scores equal to 0.88, 0.89, and 0.93 for the ventricular origin beats for three popular ECG databases and around 0.99 for the supraventricular origin for the same databases, comparable with supervised approaches presented in other works, opening a new path to making use of ECG data to classify heartbeats without the assistance of a physician. The work presented in [20] is a review paper, where a comprehensive and up-to-date review of research employing Federated Learning in healthcare applications was provided.