PERSPECTIVE article
Front. Digit. Health
Sec. Health Informatics
Volume 7 - 2025 | doi: 10.3389/fdgth.2025.1637195
This article is part of the Research TopicAI in Healthcare: Transforming Clinical Risk Prediction, Medical Large Language Models, and BeyondView all articles
Leveraging Knowledge for Explainable AI in Personalized Cancer Treatment: Challenges and Future Directions
Provisionally accepted
- 1Uniwersytet Gdanski, Gdask, Poland
- 2University of Rome Tor Vergata, Roma, Italy
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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.
Keywords: Personalized cancer treatment, Knowledge graphs, Explainability, AI, Foundation models, clinical decision-making
Received: 28 May 2025; Accepted: 15 Aug 2025.
Copyright: © 2025 Daghir-Wojtkowiak, Alfaro and Zanzotto. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Fabio Massimo Zanzotto, University of Rome Tor Vergata, Roma, Italy
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