This study illustrates how combining mathematical modelling with the power of AI could have significant impact on the clinical treatment of cancer, increasing effectiveness and reducing cost. Adaptive therapy strategies, which dynamically adjust treatment to suppress the growth of treatment-resistant populations, have emerged as a promising alternative. In our computational simulations, these schedules consistently outperform clinical standard-of-care protocols for cancer treatment as well as generic adaptive therapy, demonstrating how these results could be translated to support clinical decision-making. It was also robust to changes or uncertainty in both the patient’s treatment response and the time interval between treatments, crucial for the real-world application of this approach. The study ‘Mathematical Model-Driven Deep Learning Enables Personalized Adaptive Therapy’ has been published in Cancer Research.