163325 20251024172259.0 doi 10.3389/fdgth.2025.1637195 sideral 145761 ART-2025-145761 eng Daghir-Wojtkowiak, Emilia Leveraging knowledge for explainable AI in personalized cancer treatment: challenges and future directions 2025 Integrating multi-modal patient data to support personalized medicine has gained a lot of interest across different health domains over the past decade. Addressing this challenge requires the development and implementation of an informed, evidence-based AI-driven decision-support system continuously maintained and updated to align with the latest clinical guidelines. A key challenge to ensure its real-life adoption lies in translating the outcomes of complex AI-driven data integration and modeling into a form easily understood by the clinical audience. To ensure explainability, knowledge graphs have emerged as data models integrating multi-omics data sources and representing them as interconnected networks. Knowledge graphs offer a framework which AI models can progressively refine, highlighting the most influential features and relationships facilitating transparency of complex interactions and interdependencies. In this perspective we present major components and challenges upon developing a knowledge-based explainable AI system. Additionally, we showcase a current effort undertaken by the Knowledge at the Tips of your Fingers (KATY) consortium to develop the infrastructure for an explainable system supporting best treatment decision for a renal cancer patient. Access copy available to the general public Unrestricted info:eu-repo/grantAgreement/EC/H2020/101017453/EU/Knowledge At the Tip of Your fingers: Clinical Knowledge for Humanity/KATY This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 101017453-KATY info:eu-repo/semantics/openAccess by https://creativecommons.org/licenses/by/4.0/deed.es info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Alfaro, Javier Mastromattei, Michele Palkowski, Aleksander Stares, Mark Roca-Umbert, Ana Krajnc, Andraz Leoni, Riccardo Boland, Anne Nourbaksh, Aria Kallor, Ashwin Ducki, Camille Venditti, Davide Montesano, Carla Cipriani, Chiara Faria, Daniel Pflieger, Delphine Zago, Elisa Bardet, Etienne Serrano, Filipa Jeanneret, Florian Alouges, Damien Yin, Liangwei Coquelet, Elodine Bacquet, Apolline Bonchi, Francesco Maiorino, Francesco Torino, Francesco Bedran, Georges Long, Jean-Alexandre Balbi, Laura Guyon, Laurent Bevilacqua, Liana Fiorelli, Manuel Wagner, Marie-Catherine Reyes, Mario Roselli, Mario Silva, Marta Contreiras Waleron, Michal Dovrolis, Nikolas Filhol-Cochet, Odile Um, In Hwa Wolflein, Georg Eugénio, Patrícia Bazelle, Pauline Golnas, Pavlos Thorpe, Peter Bove, Pierluigi Borole, Piyush Bernardini, Roberta Kumar, Rohit Cicconi, Rosella Kaltenbrunner, Saskia Gravina, Saverio Brezar, Simona Symeonides, Stefan McGinn, Steven Nunes, Susana Hupp, Ted Gordienko, Yuri Varvaras, Dimitrios Stirenko, Sergii Xumerle, Luciano Mariani, Stefania Bouzit, Assilah Gazut, Stéphane Poth, Heiko Souliotis, Kyriakos Katifelis, Hector Verzoni, Elena Procopio, Giuseppe Schoch, Sarah Lupiáñez-Villanueva, Francisco Türk, Sandra Barud, Katarzyna Koroliouk, Dimitri Caubet, Juan Moreno, Yamir Universidad de Zaragoza (orcid)0000-0002-0895-1893 Descotes, Jean-Luc Golna, Christina Guadalupi, Valentina Garagnani, Paolo Gazouli, Maria Deleuze, Jean-François Folkvord, Frans Forgó, Nikolaus Harrison, David J. Axelson, Håkan Stellato, Armando Mattei, Maurizio Rajan, Ajitha Laird, Alexander Battail, Christophe Pesquita, Catia Zanzotto, Fabio Massimo 2004 405 Universidad de Zaragoza Dpto. Física Teórica Área Física Teórica 7 (2025), [10 pp.] Frontiers in digital health 2673-253X 645194 http://zaguan.unizar.es/record/163325/files/texto_completo.pdf Versión publicada 2350846 http://zaguan.unizar.es/record/163325/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:163325 articulos driver 2025-10-24-16:57:09 ARTICLE