Reinforcement Learning Enhanced AI-AugETM for Adaptive Real-Time Dose Adjustment in Oncology Phase I Trials
Keywords:
reinforcement learning, AI-AugETM, dose adjustment, Phase I clinical trials, oncology, adaptive systems, real-time decision support, algorithmic fairnessAbstract
The acceleration of oncology drug development relies heavily on the efficiency and safety of Phase I dose escalation trials, which traditionally employ rule-based or model-guided designs to identify the maximum tolerated dose. Recent advances in artificial intelligence have introduced exposure-toxicity joint modeling frameworks such as AI-AugETM, which integrate pharmacokinetic and toxicity data to personalize dose recommendations. However, these systems operate under static assumptions and lack the capacity for real-time adaptation to evolving patient responses. This paper proposes a reinforcement learning enhanced extension of AI-AugETM that transforms the dose adjustment process into a continuous, adaptive decision-making framework capable of learning optimal dosing policies on the fly. We examine the architectural integration of reinforcement learning with the existing AI-AugETM infrastructure, focusing on state representation, reward design, and policy learning under high uncertainty. The system-level implications of deploying such an adaptive agent in regulated clinical environments are analyzed, including robustness to noisy data, trade-offs between exploration and patient safety, fairness across heterogeneous patient populations, and governance challenges related to interpretability and regulatory approval. Deployment considerations such as computational sustainability, integration with electronic health record systems, and real-time safety monitoring are discussed. By situating the technical innovation within a broader socio-technical context, this paper argues that reinforcement learning enhanced AI-AugETM offers a promising path toward truly personalized dose optimization, but its success hinges on carefully designed governance structures that balance algorithmic autonomy with clinician oversight. The framework also raises important questions about equity in algorithmic decision-making and the need for transparent, auditable models. We conclude by outlining a research agenda for future empirical validation and policy development.
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