This paper presents the results of an extensive study investigating the opinions on Artificial Intelligence (AI) of a sample of 4,006 European citizens from eight distinct countries (France, Germany, Italy, Netherlands, Poland, Romania, Spain, and Sweden). The aim of the study is to gain a better understanding of people’s views and perceptions within the European context, which is already marked by important policy actions and regulatory processes. To survey the perceptions of the citizens of Europe, we design and validate a new questionnaire (PAICE) structured around three dimensions: people’s awareness, attitude, and trust. We observe that while awareness is characterized by a low level of self-assessed competency, the attitude toward AI is very positive for more than half of the population. Reflecting on the collected results, we highlight implicit contradictions and identify trends that may interfere with the creation of an ecosystem of trust and the development of inclusive AI policies. The introduction of rules that ensure legal and ethical standards, along with the activity of high-level educational entities, and the promotion of AI literacy are identified as key factors in supporting a trustworthy AI ecosystem.We make some recommendations for AI governance focused on the European context and conclude with suggestions for future work.

Precision medicine aims to overcome the traditional one-model-fits-the-whole-population approach that is unable to detect heterogeneous disease patterns and make accurate personalized predictions. Heterogeneity is particularly relevant for patients with complications of type 2 diabetes, including diabetic kidney disease (DKD). We focus on a DKD longitudinal dataset, aiming to find specific subgroups of patients with characteristics that have a close response to the therapeutic treatment. We develop an approach based on some particular concepts of category theory and cluster analysis to explore individualized modelings and achieving insights onto disease evolution. This paper exploits the visualization tools provided by category theory, and bridges category-based abstract works and real datasets. We build subgroups deriving clusters of patients at different time points, considering a set of variables characterizing the state of patients. We analyze how specific variables affect the disease progress, and which drug combinations are more effective for each cluster of patients. The retrieved information can foster individualized strategies for DKD treatment.

Diabetic kidney disease (DKD) is a serious complication of type-2 diabetes, defined prominently by a reduction in estimated glomerular filtration rate (eGFR), a measure of renal waste excretion capacity. However DKD patients present high heterogeneity in disease trajectory and response to treatment, making the one-model-fits-all protocol for estimating prognosis and expected response to therapy as proposed by guidelines obsolete. As a solution, precision or stratified medicine aims to define subgroups of patients with similar pathophysiology and response to the therapy, allowing to select the best drug combinations for each subgroup. We focus on eGFR when aiming to identify eGFR decline trends by clustering patients according to their eGFR trajectory shape-similarity.The study involved 256 DKD patients observed annually for four years. Using the Fréchet distance, we built clusters of patients according to the similarity of their eGFR trajectories to identify distinct clusters. We formalized the trajectory-clustering approach through category theory. Characteristics of patients within different progression clusters were compared at the baseline and over time.We identified five clusters of eGFR progression over time. We noticed a bifurcation of eGFR mean trajectories and a switch between two other mean trajectories. This particular clustering approach identified different mean eGFR trajectories. Our findings suggest the existence of distinct dynamical behaviors in the disease progression.