An innovative artificial intelligence-based method to compress complex models into explainable, model-agnostic and reduced decision support systems with application to healthcare (NEAR)

Karim Kassem, Michela Sperti, Andrea Cavallo, Andrea Mario Vergani, Davide Fassino, Monica Moz, Alessandro Liscio, Riccardo Banali, Michael Dahlweid, Luciano Benetti, Francesco Bruno, Guglielmo Gallone, Ovidio De Filippo, Mario Iannaccone, Fabrizio D'Ascenzo, Gaetano Maria de Ferrari, Umberto Morbiducci, Emanuele Della Valle, Marco Agostino Deriu

Abstract: Highlights•Clinical guidelines, which guide decisions in clinical practice, do not consider individual patients’ characteristics.•Artificial intelligence-based systems could enhance the personalization of diagnosis, prognosis, and treatment.•Artificial intelligence models usually show high performance in clinical risk prediction lacking in transparency.•Transparent, personalized, and reliable artificial intelligence-based methods might improve clinical guidelines.