Federica Corso, Aleksandra Zec, Margherita Favali, Marta Ligero, Lars Hilgers, Luca Mauro Invernizzi, Laura Mazzeo, Adria Marcos Morales, Gustav Anton Müller-Franzes, Ines Prata Machado, Felix Busch, Vanja Mišković, Francesco Trovò, Claudia Proto, Giuseppe Lo Russo, Mario Occhipinti, Marta Brambilla, Teresa Beninato, Susan Halabi, Marina Chiara Garassino, Alessandra Laura Giulia Pedrocchi, Anna Pellat, Lisa Adams, Julien Calderaro, Daniel Truhn, Suzette Delaloge, Julien Vibert, Jana Lipkova, Miriam Koopman, Raquel Perez-Lopez, Mireia Crispin-Ortuzar, Jakob Nikolas Kather, Arsela Prelaj
Foundation models (FMs) and large language models (LLMs) are transforming cancer AI by integrating heterogeneous data sources, including medical imaging, electronic health records, and molecular profiles. By learning from large-scale, unstructured, and label-free inputs, these models may support diagnosis, biomarker discovery, prognostic assessment, treatment personalization, and workflow automation. In this narrative review, we propose the paradigm of "Leave No Data Behind" to describe the promise that broad oncology data integration may generate clinically meaningful outputs. We critically assess whether this paradigm is supported by current evidence and identify the key challenges that must be addressed to harness the full potential of FMs and LLMs for clinical implementation in oncology.