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<p>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.</p>

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The future of mathematical oncology in the age of AI

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月26日

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  • 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,
  • Guillermo Lorenzo,
  • Babgen Manookian,
  • Andrei S. Rodin,
  • Lara Schmalenstroer,
  • Juan Soler,
  • Cristian Tomasetti,
  • Konstancja Urbaniak

摘要

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.