Explainable Causal Reinforcement Learning for precision oncology clinical workflows under real-time policy constraints

Explainable Causal Reinforcement Learning for precision oncology clinical workflows under real-time policy constraints A Personal Journey into the Heart of AI-Driven Medicine My journey into this fascinating intersection of AI and oncology began during a late-night research session about three years ago. I was experimenting with standard reinforcement learning (RL) for treatment optimization when I encountered a particularly troubling case study. The model had recommended a treatment regimen that, statistically, should have worked—but the patient experienced severe adverse effects. While exploring the model's decision-making process, I discovered a fundamental limitation: traditional RL could identify correlations between treatments and outcomes, but it couldn't distinguish causation from mere association. This realization sent me down a rabbit hole of causal inference literature, structural equation modeling, and eventually to the frontier of explainable AI. In my research of precisio