* Reducing the Heart Failure Burden in Romania by Predicting Congestive Heart Failure Using Artificial Intelligence: Proof of Concept [17]
* Nonlinear Random Forest Classification, a Copula-Based Approach [18]
* A Novel Unsupervised Computational Method for Ventricular and Supraventricular Origin Beats Classification [19]
* A Systematic Review of Federated Learning in the Healthcare Area: From the Perspective of Data Properties and Applications [20]
* On Combining Feature Selection and Over-Sampling Techniques for Breast Cancer Prediction [21]  
The aforementioned papers refer to the following main topics within the healthcare scenario: (i) COVID-19 datasets and models [14,15], (ii) large dataset annotation [12], (iii) cyber security [13], (iv) federated learning [20], (v) smart biomedical systems and devices [16], and (vi) artificial intelligence approaches [17, 18, 19, 21]. More in detail, in [12], a methodology for reducing the manual effort needed to annotate a biomedical-named entity recognition (B-NER) corpus was presented, exploiting both active learning and distant supervision, respectively, based on Deep Learning models (e.g., Bi-LSTM, word2 vec FastText, ELMo, and BERT) and biomedical knowledge bases to speed up the annotation task. The proposed approach is also able to limit class imbalance issues. The results showed that this method allows us to annotate an effective and large B-NER corpus with a fraction of the time required by a fully manual annotation, addressing the lack of annotated corpora in the biomedical domain [22]. The authors also analysed the most effective embedding model to represent the input words [23] and the applicability of this approach to other domains. In [13], a novel methodology for the cyberattack path discovery to ensure security within the healthcare ecosystem is presented. This approach is based on the Common Vulnerability Scoring System (CVSS), so that base metrics and exploitability features can be used to determine and prioritise the possible attack paths based on the threat actor capability, asset dependency, and target user profile and evidence of indicator of compromise. The work includes a real example from the healthcare use case to demonstrate the methodology used for attack path generation. The result from the studied context, which processes Big Data from healthcare applications, shows that the uses of various parameters such as CVSS metrics, threat actor profile, and the indicator of compromise are able to generate realistic attack paths. In this way, healthcare practitioners can be supported in identifying the controls that are required to secure the overall healthcare ecosystem. The authors of [14] presented a methodology that is used determine COVID-19 vulnerability risk and its change over time in association with the state health care system, turnover, and transport to support the crisis management decision-making process. In detail, this method aims to determine the COVID-19 Vulnerability Index (CVI) based on the selected criteria. The risk assessment was carried out with methodology that includes the application of a multi-criteria analysis and spatio-temporal aspects of available data. Particularly, the Spatial Multicriteria Analysis (SMCA) compliant with the Analytical Hierarchy Process (AHP), which incorporated selected population and environmental criteria were used to analyse the ongoing pandemic. The influence of combining several factors in an analysis of the pandemic was illustrated, and the static and dynamic factors to COVID-19 vulnerability risk were determined to prevent and control the spread of COVID-19 at the early stages of the pandemic. As a result, areas with a certain level of risk in different periods of time were determined. Furthermore, the number of people exposed to a COVID-19 vulnerability risk was presented with time. The results obtained proved that the this approach can support the decision-making process by showing the area where preventive actions should be considered.