CARE: Clinical AI predictor for posterior urethal valves - design, explainability and evaluation

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 %.