Rockne, Russell C.Lorenzo, Guillermo2026-05-252026-05-252026-01Rockne, R.C., Andersen, M., Anderson, A.R.A. et al. The future of mathematical oncology in the age of AI. npj Syst Biol Appl 12, 22 (2026). https://doi.org/10.1038/s41540-026-00656-92056-7189https://hdl.handle.net/2183/48368[Abstract]: This perspective article discusses emerging advances at the interface of mechanistic modeling and data-driven machine learning, highlighting opportunities for AI to accelerate discovery, improve predictive modeling, and enhance clinical decision-making. We address critical limitations of current AI approaches and propose a perspective on a future where AI augments mechanistic rigor, clinical relevance, and human creativity under the umbrella of a redefined understanding of Mathematical Oncology.engAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/Computational biologyBioinformaticsCancerMathematical oncologyreviewopen access10.1038/s41540-026-00656-9