2026-07-23T00:09:28Zhttps://iris.cnr.it/oai/request

oai:iris.cnr.it:20.500.14243/5852852026-06-08T05:19:35Zcom_20.500.14243_46com_20.500.14243_21col_20.500.14243_47ou_ou294ou_ou167

Advancements in Artificial Intelligence and Computer Vision for Biomedical Applications @SI-Lab Marco Cafiso Andrea Carboni Claudia Caudai Sara Colantonio Francesco Conti Mario D’Acunto Said Daoudagh Giulio Del Corso Riccardo Del Gratta Danila Germanese Giacomo Ignesti Gianmarco Lazzini Giuseppe Riccardo Leone Barbara Leporini Massimo Magrini Massimo Martinelli Davide Moroni Ali Reza Omrani Francesca Pardini Maria Antonietta Pascali Paolo Paradisi Laura Sebastiaini Marco Tampucci Valeria Tateo Federico Volpini Cafiso, Marco Carboni, Andrea Caudai, Claudia Colantonio, Sara Conti, Francesco D’Acunto, Mario Daoudagh, Said Del Corso, Giulio Del Gratta, Riccardo Germanese, Danila Ignesti, Giacomo Lazzini, Gianmarco Leone, Giuseppe Riccardo Leporini, Barbara Magrini, Massimo Martinelli, Massimo Moroni, Davide Omrani, Ali Reza Pardini, Francesca Pascali, Maria Antonietta Paradisi, Paolo Sebastiaini, Laura Tampucci, Marco Tateo, Valeria Volpini, Federico Physiological signals Neuromotor rehabilitation Dance movement therapy Artificial Intelligence Machine Learning Generative AI Explainable AI Uncertainty Quantification Trustworthy AI This paper summarizes recent research at the Signals and Images Lab (ISTI-CNR) leveraging artificial intelligence, machine learning, and computer vision to address complex biomedical challenges. We highlight advancements in trustworthy, explainable algorithmic foundations alongside their practical clinical applications across diagnostic imaging, physiological signal analysis and neuromotor rehabilitation. 2026 info:eu-repo/semantics/conferenceObject https://hdl.handle.net/20.500.14243/585285 eng ispartofbook:ITAL-IA 2026 info:eu-repo/semantics/openAccess ELETTRONICO license:Creative commons license uri:http://creativecommons.org/licenses/by-nc-nd/4.0/