The future of mathematical oncology in the age of AI

npj Systems Biology and Applications 2026 Open access

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Authors: Russell C. Rockne, Morten Andersen, Alexander R.A. Anderson, David Basanta, Angela Bentivegna, Sebastien Benzekry, Sergio Branciamore, Sarah C. Brüningk, Martina Conte, Farnoush Farahpour, Aleksandra Karolak, Alvaro Köhn-Luque
+7 more Guillermo Lorenzo, Babgen Manookian, Andréi S. Rodin, Lara Schmalenstroer, J. D. Soler, Cristian Tomasetti, Konstancja Urbaniak
DOI: 10.1038/s41540-026-00656-9
Cited by: 2
References: 63

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.

Fields of Study & Subjects

Mathematical Biology Tumor Growth Artificial Intelligence in Healthcare and Education Radiomics and Machine Learning in Medical Imaging

Publication details

Journal / source
npj Systems Biology and Applications
Publisher
Springer Science and Business Media LLC
Volume
12
Issue
1
Pages
22–22
Publication date
26 January 2026
Language
EN