AUTHOR=Daghir-Wojtkowiak Emilia , 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 , 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 

TITLE=Leveraging knowledge for explainable AI in personalized cancer treatment: challenges and future directions

JOURNAL=Frontiers in Digital Health

VOLUME=Volume 7 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2025.1637195

DOI=10.3389/fdgth.2025.1637195

ISSN=2673-253X

ABSTRACT=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.