In the last decades, the remarkable impact achieved by Artificial Intelligence (AI) in business and industry has not been mirrored in critical real-world applications. The industrial diffusion of AI in healthcare is facing some resistance due to the lack of uniform legal frameworks and general scepticism among society and medical personnel. This paper proposes a multidisciplinary approach to fill the gap between the theoretical AI-based framework and real clinical practice, tailored to the problem of Posterior Urethral Valves (PUVs) diagnosis in paediatric patients. The multidisciplinary core of the work allows tackling the problem not only under the technical lens, but also from a clinical and industrial perspective: through the adoption of classifier composition mechanisms, this study presents the lessons learned in developing a reliable PUV classifier, as well as in its empirical assessment against real-world data and within a structured diagnostic process. The main contribution of this study is the design of a clinical decision support system for medical experts, which evaluates the behaviour of the model clinically and validates the extracted rules using explainability techniques on real-world data. AI classifiers leveraging vertical training of specialised models were adopted, achieving an overall accuracy of 70 %.

Modern public and private healthcare structures are facing the problem of improving the quality of patient health without increasing costs. Smart healthcare is currently transforming the traditional medical practices, resulting in a more efficient, convenient and personalized healthcare. In this paper, a solution for a fair usage of economic resources is proposed: the FIDCARE approach. Based on a flexible software architecture, with the capability to be extended by external “plugins”, the FIDCARE platform conjugates both the needs. IoT technologies and AI algorithms are at the basis of the entire platform to enable a proper level of flexibility. The paper presents the approach with the case study of oncological therapy.

This paper describes a framework to develop cost-benefit analyses of the use of predictive maintenance technologies, i.e., sensors and network infrastructure, in order to optimise field maintenance activities in situations where there is a need for maintenance personnel to move across a wide geographical area. The proposed methodologies can be used both in the strategic phase, in which it is necessary to make the choice of the maintenance strategy, and in the operational phase, in which, through decision support systems, it is necessary to plan maintenance interventions in the current maintenance cycle. In order to illustrate the proposed methodologies, a toy problem is defined and developed via Monte Carlo simulations. Experiments show the effectiveness of the proposed framework.