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
- Konstancja Urbaniak
- Guillermo Lorenzo
- Lara Schmalenstroer
- Russell C. Rockne
- Alexander R. A. Anderson
- Juan Soler
- Morten Andersen
- Sebastien Benzekry
- Sarah C. Brüningk
- Martina Conte
Publication Details
- Journal
- Open Access CRIS of the University of Bern
- Published
- 2026-01-26
- DOI
- https://doi.org/10.48620/94248
- Primary Topic
- Mathematical Biology Tumor Growth
- Type
- article
- Field-Weighted Citation Impact
- 0.00