AI時代の数理腫瘍学の未来
Russell C Rockne1, Morten Andersen2, Alexander R A Anderson3
1Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope, CA, USA. rrockne@coh.org.
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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