From mechanistic models to artificial intelligence: exploring the potential of digital twins in geriatric oncology.

This survey explores how machine learning and artificial intelligence (AI) can be integrated with mechanistic models to create more accurate, dynamic, predictive, and personalized representations of biological systems, commonly referred to as digital twins (DTs). Mechanistic models, such as pathway-based Boolean or differential equation frameworks, provide interpretable insights into biological processes; however, calibrating these models to represent individual variability across large, heterogeneous cohorts remains a significant challenge, as their physically constrained structures often lack the flexibility to capture complex, non-mechanistic nuances in patient data. Focusing on elderly cancer patients-a vulnerable population underrepresented in clinical research-we discuss how hybrid DTs can bridge the gap between interpretable mechanistic frameworks and flexible, predictive AI approaches, enabling continuous monitoring, risk stratification, and adaptive treatment planning. To illustrate these principles, we present a proof-of-concept case study involving a synthetic breast cancer dataset in which comprehensive geriatric assessment, clinical tests, and quality of life measures inform dosing decisions for older patients via a Markov Decision Process. By combining a synthesis of current literature with the application of a sequential decision-making framework optimized using longitudinal data, this work provides a foundational understanding for researchers and clinicians interested in leveraging DTs to improve personalization and outcomes in geriatric oncology.