arXiv:2606.10376v1 Announce Type: new Abstract: Cancer treatment is at the core a sequential decision-making problem with partial observability, latent patient heterogeneity, and explicit constraints on the budget for medical measurements. Unlike standard Reinforcement Learning (RL) approaches that control state trajectories, cancer treatments permanently modify patients' transition dynamics, changing how states evolve over time. We model cancer treatment as a belief-space planning problem using active inference, deriving an expected free-energy objective that unifies goal-directed control and
Advances in AI, particularly active inference and belief-space planning, are reaching a maturity where they can be applied to complex, partially observable real-world problems like personalized medicine. The shift from controlling state trajectories to modifying transition dynamics reflects a deeper understanding of biological systems.
This development represents a significant step towards truly personalized and adaptive cancer treatments, moving beyond static protocols to dynamic, AI-driven decision-making in highly uncertain medical environments. It could drastically improve treatment efficacy and patient outcomes by accounting for individual patient heterogeneity.
Cancer treatment moves from being a primarily protocol-driven sequence to an AI-optimized, adaptive control problem where the system continuously learns and adjusts based on patient response and latent factors. It introduces a new paradigm for medical decision-making that leverages AI's strengths in complex, uncertain environments.
- · AI-driven biotech companies
- · Oncology patients
- · Healthcare systems adopting AI
- · Medical AI researchers
- · One-size-fits-all treatment models
- · Traditional diagnostic companies
- · Clinical trial methodologies focused on large averages
Personalized cancer treatment regimens become significantly more effective and tailored, improving survival rates and quality of life.
The cost structure of oncology may shift as AI-driven precision reduces wasted treatments and enhances decision-making efficiency.
This approach could extend to other complex chronic diseases, accelerating the development of highly personalized and adaptive medical interventions across healthcare.
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Read at arXiv cs.AI