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.
Rockne, R., Andersen, M., Anderson, A., Basanta, D., Bentivegna, A., Benzekry, S., et al. (2026). The future of mathematical oncology in the age of AI. NPJ SYSTEMS BIOLOGY AND APPLICATIONS, 12(1) [10.1038/s41540-026-00656-9].
The future of mathematical oncology in the age of AI
Bentivegna, Angela;
2026
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.
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