The future of mathematical oncology in the age of AI.

BORIS DOI
Publisher DOI
PubMed ID
41588010
Description
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.
Date of Publication
2026-01-26
Publication Type
Article
Subject(s)
Language(s)
en
Contributor(s)
Rockne, Russell C
Andersen, Morten
Anderson, Alexander R A
Basanta, David
Bentivegna, Angela
Benzekry, Sebastien
Branciamore, Sergio
Conte, Martina
Farahpour, Farnoush
Karolak, Aleksandra
Köhn-Luque, Alvaro
Lorenzo, Guillermo
Manookian, Babgen
Rodin, Andrei S
Schmalenstroer, Lara
Soler, Juan
Tomasetti, Cristian
Urbaniak, Konstancja
Additional Credits
Series
npj Systems Biology and Applications
Publisher
Nature Research
ISSN
2056-7189
Access(Rights)
open.access
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