According to one description, ML is “the fundamental technology required to meaningfully process data that exceed the capacity of the human brain to comprehend” [6]. A large number of data points is used to train ML computer-based models. Existing information about specific data items and relationships between data elements is learned via repeated cycles of mapping between inputs and outputs, rather than being explicitly coded into the model. Therefore, cooperation between ML and clinical specialists is critical, and there are many computational approaches that include various degrees of clinical experience into model parameters [7]. Currently, the generation of ML models is mainly grouped into four categories: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Briefly, the output labels, such as a disorder, are known in advance in supervised ML models. In fact, the objective is to generate a computational tool for predicting an output from a set of input data (i.e., the output is usually termed a target value, response variable, or label, while inputs are termed predictors or features). The method “learns” the best model by analyzing the data contained in the training set, which include many observations, each of which holds values for its characteristics as well as its label. Additionally, there are two kinds of supervised ML: classification and regression. In the classification, the output variable is divided into categories, such as “present” or “absent”, “disorder” or “no-disorder”, or “grading” (Grade1, Grade2, etc.), while in regression the output variable is an actual value, such as “weight”, “dose”, or “concentration” (IC₅₀, EC₅₀, TC₅₀, etc.) for performing predictions on novel samples. Such ML may be employed in medical imaging in a variety of areas, including radiology, pathology, and other imaging fields, as well as in epidemiology. Recently, with the outbreak of the COVID-19 pandemic, some ML approaches have been applied for predicting the infection rate, starting from an epidemiology dataset [8], as well as from environmental conditions [9]. Furthermore, in recent years, supervised ML has also been used in drug discovery and development ^[10][11][12][10,11,12]. These approaches employing supervised ML are valuable, but they must be approached with prudence because they need huge and reliable data sets containing high-quality data to become accurate, and the data must be correctly categorized [13].

On the other hand, unsupervised ML models aim to identify relationships in data that we would not see otherwise. In particular, there are no labels on the data sets, but they do contain features. As a result, the unsupervised ML algorithm must produce groups and classes based on data set similarities. Unsupervised ML, in contrast to supervised ML, predicts unknown outcomes, uncovering previously undiscovered patterns.

Unsupervised ML is exemplified through clustering. This latter is the process of dividing data into various groups or clusters. Accordingly, when the exact information about the clusters is unknown, we can utilize unsupervised ML to cluster them [14]. Various scientific fields benefit from the application of unsupervised ML. For instance, in a recent report, the unsupervised ML technique was applied for identifying subjects showing a high likelihood of dementia in a population-based survey, with no need of a medical diagnosis of dementia in the subsample [15]. Another study investigated healthcare professionals’ feelings toward a digital simulator, technology, and mentality for elucidating their effects on neonatal resuscitation performance in simulation-based assessments [16]. In general pathology, unsupervised ML is becoming a crucial tool for accelerating the transition to autonomous pathological tissue analysis [17]. In another study, an unsupervised ML approach was used to discover patient clusters established by genetic signatures [18]. Additionally, in this case, in drug discovery and development, unsupervised ML has been successfully applied in atomistic simulations or to understand the comportment of chemicals (e.g., drugs) as well as materials [19]. Recently, in a randomized clinical study, unsupervised ML was applied to cluster septic patients to determine optimal treatment (NCT03752489). To further clarify the difference between supervised and unsupervised ML models, a supervised ML model can be used to identify which subjects will develop a given disorder, a known entity, while an unsupervised ML model will be able to identify unknown subgroups of patients suffering from a given pathology since unsupervised models assume that the output labels are unknown. Most of the computer-based models incorporated into clinical workflows, as clinical decision support, are supervised ML models. For improving the performances of ML models, unsupervised and supervised ML can be combined into semi-supervised ML (Figure 1). Ma and colleagues successfully reported a combination of the two strategies for phenotyping complex diseases. They applied this technique to obstructive sleep apnea, highlighting that the phenotyping framework constructed by combining unsupervised and supervised ML techniques can be employed for other heterogeneous, complex diseases to phenotype patients, distinguishing significant features for high-risk phenotypes [20]. Omta and coworkers, by combining unsupervised and supervised ML-based tools, showed that they have a great capacity to increase the capability of detecting new knowledge in functional genomics screening. Firstly, they applied unsupervised exploratory ML models to the dataset to gain better insight into the quality of the data. This latter approach enhances the selection and labeling of data for establishing reliable training sets prior to applying ML. For demonstrating the validity of the approach, they used a high-content genome-wide small interfering RNA (siRNA) screen. By applying unsupervised ML models, they easily identified four robust phenotypes that were consequently used as a training set for developing a high-quality random forest (RF) ML tool for differentiating four phenotypes (accuracy = 91.1%; kappa = 0.85). The reported approach significantly improved the ability to obtain novel information from a screening compared with the usage of unsupervised ML techniques alone [21].

However, it is important to highlight that the accuracy of these analyses is strongly dependent on the quality of the training sets employed to generate ML models. Finally, the reinforcement ML method allows the computational tool to learn from its failures, generating an algorithm based on what it has learned. Thus, this learning is constructed upon the trial-and-error process [22]. In the scientific field, for example, different tasks can include training an algorithm to understand the treatment regimens of medical registry data and to find the optimal strategy for treating patients with chemotherapy. A recent study reported the successful use of a reinforcement ML model for establishing an effective formulation of clinical trial dosing. The algorithm was trained with proper dosing regimens for reducing tumor diameters in patients treated by means of chemotherapy and radiation [23]. In Figure 1 is reported a schematic illustration of the mentioned ML approaches. This brief excursion about the different ML techniques and how they can be applied to scientific fields, for improving and enhancing the understanding of complex systems, highlights the potential of ML methods. To this end, there is growing attention being paid to the application of these methodologies in the framework of translational medicine, enhancing the ability of translational scientists to provide novel effective treatments and diagnostics for healthcare.