Diabetes affects millions of people and requires reliable continuous glucose monitoring for early hypoglycemia warning. However, medical AI systems must be not only accurate and energy efficient, but also explainable, noise robust, and uncertainty aware. This work presents a 65 nm hypoglycemia forecasting engine based on probabilistic decision trees for trustworthy medical inference. The proposed hybrid architecture combines exact arithmetic evaluation for shallow tree layers with sampling based inference for deeper layers, reducing soft decision tree complexity from exponential to sample efficient traversal. A reconfigurable 4 by 24 by 24 probabilistic node array supports arbitrary tree structures with a maximum depth of 12, coordinated by an on chip low power RISC V core. Fabricated in 65 nm CMOS, the chip achieves 11.3 nJ per inference and a state of the art 30 min forecasting F1 score of 0.825 on continuous glucose monitoring data. Compared with conventional decision tree and random forest models, the proposed engine improves robustness to sensor noise and data point drop off by 4.1x to 16.1x. These results demonstrate an energy efficient, explainable, and uncertainty aware edge AI engine for trustworthy hypoglycemia forecasting.

翻译：糖尿病影响全球数百万人，需要可靠的连续血糖监测系统实现早期低血糖预警。然而，医疗人工智能系统不仅需要高精度与低能耗，还必须具备可解释性、噪声鲁棒性与不确定性感知能力。本文提出基于概率决策树的65纳米低血糖预测引擎，用于可信赖医疗推理。所提出的混合架构将浅层树的精确算术评估与深层树的采样推理相结合，将软决策树复杂度从指数级降低至样本高效遍历。可重构的4×24×24概率节点阵列支持最大深度为12的任意树结构，并由片上低功耗RISC-V内核协调控制。该芯片采用65纳米CMOS工艺制造，在连续血糖监测数据上实现单次推理11.3纳焦能耗，并达到30分钟预测F1分数0.825的先进水平。与传统决策树和随机森林模型相比，本引擎对传感器噪声和数据点缺失的鲁棒性提升4.1至16.1倍。上述结果证明，该边缘AI引擎兼具高能效、可解释性与不确定性感知能力，适用于可信赖的低血糖预测。