The future of mathematical oncology in the age of AI
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.
Authors
- Cristian Tomasetti (ORCID: https://orcid.org/0000-0003-3277-4804)
- David Basanta
- Babgen Manookian
- Aleksandra Karolak
- Angela Bentivegna (ORCID: https://orcid.org/0000-0001-7535-846X)
- Andréi S. Rodin (ORCID: https://orcid.org/0000-0002-2570-0332)
- Farnoush Farahpour (ORCID: https://orcid.org/0000-0002-4510-8483)
- Alvaro Köhn-Luque
- Martina Conte
- Morten Andersen (ORCID: https://orcid.org/0000-0001-7582-0685)
- Konstancja Urbaniak
- J. D. Soler (ORCID: https://orcid.org/0000-0002-0294-4465)
- Guillermo Lorenzo
- Sarah C. Brüningk
- Lara Schmalenstroer
- Russell C. Rockne
- Alexander R. A. Anderson
Institutions
- Roskilde University (DK)
- Universidade da Coruña (ES)
- University of Bern (CH)
- Oslo University Hospital (NO)
- Centre National de la Recherche Scientifique (FR)
- Bern University of Applied Sciences (CH)
- Inserm (FR)
- Universidad de Granada (ES)
- Politecnico di Torino (IT)
- University Hospital of Bern (CH)
- Moffitt Cancer Center (US)
- Quantitative BioSciences (US)
- Numerical Method (China) (CN)
- Essen University Hospital (DE)
- Centre de Recherche en Cancérologie de Marseille (FR)
- University of Duisburg-Essen (DE)
- University of Milano-Bicocca (IT)
- The University of Texas at Austin (US)
Publication Details
- Journal
- npj Systems Biology and Applications
- Published
- 2026-01-26
- DOI
- https://doi.org/10.1038/s41540-026-00656-9
- Primary Topic
- Mathematical Biology Tumor Growth
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Syracuse University
- Beckman Research Institute, City of Hope