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This article aims to explore how AI can enhance mathematical oncology by integrating mechanistic modeling and machine learning.

February 8, 2026Open Access

The future of mathematical oncology in the age of AI.

Key Points

This article aims to explore how AI can enhance mathematical oncology by integrating mechanistic modeling and machine learning.
Discusses emerging advances in mechanistic modeling and machine learning.
Evaluates the limitations of current AI approaches in oncology.
Proposes a vision for AI augmenting clinical decision-making and predictive modeling.
Highlights how AI can accelerate discovery in mathematical oncology.
Suggests that AI can improve predictive modeling for better outcomes.
Emphasizes the need for human creativity alongside mechanistic rigor in future applications.

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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Authors

Russell C. Rockne

Morten Andersen

Alexander R. A. Anderson

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The future of mathematical oncology in the age of AI.

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Abstract

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