Energy-Efficient and Privacy-Aware AI Framework for Smart Pediatric Hospitals: Integrating DC–DC Converter Design with Secure NLP-Based Decision Systems

Abstract

Smart pediatric hospitals combine pervasive sensing, edge compute, and AI-driven clinical decision support to improve care delivery, but they must balance energy constraints (battery-powered devices, local edge nodes), safety/regulatory requirements, and strict privacy for children’s health data. We propose an integrated framework that connects energy-aware DC–DC converter and power-management design at the hardware layer with a privacy-preserving, NLP-based decision stack at the software layer. At hardware level, modern low-voltage, high-efficiency non-isolated and integrated DC–DC converters (buck converters, charge pumps, on-chip regulators) and adaptive power-management policies minimize energy per inference for edge AI modules embedded in bedside monitors and mobile clinician devices. At system level, on-device and federated NLP models extract clinical signals from unstructured notes, nursing logs, device alerts and parental messages; model updates are coordinated with secure aggregation and differential-privacy mechanisms to avoid raw-data sharing and limit leakage. Energy-aware ML strategies (model sparsity, mixed precision, conditional compute), combined with hardware power states and converter-level energy scaling, reduce operational energy while preserving clinical utility. The framework includes (a) co-design guidelines — mapping workload profiles to converter operating points and power modes; (b) a privacy and governance stack (local de-identification, federated learning orchestration, DP budgets, secure aggregation); and (c) operational policies for safe automation of NLP-driven alerts and clinician-in-the-loop escalation. We outline an evaluation plan using lab bench measurements (converter efficiency curves, end-to-end energy per inference), retrospective EHR/NLP backtests for accuracy and privacy leakage analysis, and prospective pilot deployments in pediatric wards. We discuss trade-offs: tighter privacy budgets increase noise and can harm downstream sensitivity, and extreme energy minimization may reduce model capacity — both require careful co-optimization and human-centered governance. The proposed architecture enables resilient, low-energy, privacy-aware AI for pediatric hospitals while preserving clinician oversight and regulatory compliance.