It is equally important to note that this work [4] highlights the well-known dependence of ML approaches on prior anatomical knowledge--i.e., anatomical or functional atlases--and on manual processing and decision, which can introduce human bias to the automatic prediction of the pathologies. The classification of neurological and neurodegenerative diseases can be supported by supervised learning, as well as by unsupervised learning. A novel approach of clustering, based on the affinity propagation algorithm, was proposed by [PERSON] et al. [5] with the purpose of investigating the natural structure of the cognitive profiles in [PERSON] disease and Parkinsonisms. The authors demonstrated the reliability of the use of cluster analysis in discovering intra- and inter-diagnostic heterogeneity in the cognitive profile of Parkinsonism patients, and, more importantly, showed how to transform a ML approach into a decision support tool for use in a clinical setting [5]. The last two works [6; 7] that belong to this Special Issue represent two interesting examples of the application of ML to learning how to do something better, faster, more efficiently, and with more precision. In the study of [PERSON] et al. [6], a B-spline surface-fitting algorithm was employed for improving existing approaches for lung segmentation and solving a peculiar issue occurring in the recognition of lung fissures. The proposed novel method [6] reduces the time and computing costs of elaboration compared to the literature, but, similarly to the work conducted by [PERSON] et al. [4], poses the problem of the dependence on prior anatomical structure knowledge when building reliable ML models. Indeed, the study by [PERSON] et al. [7] also highlights the high dependence on human expertise and manually annotated data for the automatic count of malaria parasites in thick smear blood. The proposed method [7] overcomes the problems of the time-consuming conventional approaches used for the identification and quantification of malaria parasitemia thanks to transfer learning, which is applied on digital images, with a Faster Regional Convolutional Neural Network (Faster R-CNN) and Single Shot Multibox Detector (SSD). The scientific contributions collected in this Special Issue represent valuable demonstrations of the application of ML in healthcare and biomedical applications, and, moreover, suggest several good practices for use in future works. For example, the importance of sufficiently large datasets [2; 3; 5; 7], their comparability in terms of demographic and clinical features [2], the use of separate validation sets [2; 4], as well as the sharing of data [6] are confirmed. This also demonstrates the utility of the hyperparameter tuning of ML algorithms [2], as well as the usefulness of introducing new constraints into models [3]. Two works [4; 6] show that a better representation of an object of interest through derived features could improve the performance of ML methods. This Special Issue raises a fundamental issue with regard to the need to reduce the amount of manual processing required and the need to avoid human bias in building ML models [4; 6; 7]. Finally, demand to increase the interpretability of ML findings has emerged [2; 4; 5], as the recent growing interest of the scientific community in Explainable Artificial Intelligence (XAI) demonstrates [8]. This work received no external funding. **Institutional Review Board Statement**: Not applicable. **Informed Consent Statement**: Not applicable. **Data Availability Statement**: Not applicable. **Acknowledgments**: The author wants to thank all the contributors of this Special Issue. Their original scientific works have allowed us to shed new light on the importance of machine learning in healthcare and biomedical applications. **Conflicts of Interest**: The author declares no conflict of interest.