Archivio Istituzionale della RicercaThis 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.
The future of mathematical oncology in the age of AI / Rockne, R. C.; Andersen, M.; Anderson, A. R. A.; Basanta, D.; Bentivegna, A.; Benzekry, S.; Branciamore, S.; Bruningk, S. C.; Conte, M.; Farahpour, F.; Karolak, A.; Kohn-Luque, A.; Lorenzo, G.; Manookian, B.; Rodin, A. S.; Schmalenstroer, L.; Soler, J.; Tomasetti, C.; Urbaniak, K.. - In: NPJ SYSTEMS BIOLOGY AND APPLICATIONS. - ISSN 2056-7189. - 12:1(2026), pp. 1-7. [10.1038/s41540-026-00656-9]
The future of mathematical oncology in the age of AI
Conte M.;
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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https://hdl.handle.net/11583/3007808