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The escalating impacts of climate change on human health necessitate innovative approaches to integrate environmental data with clinical records for enhanced risk assessment and decision-making. This conceptual manuscript proposes the Environmental-Clinical Synergy Risk Orchestrator (ECSRO), a novel intelligence architecture designed for seamless fusion of heterogeneous data sources. Drawing on theoretical foundations in healthcare analytics and AI system infrastructure, ECSRO comprises layered components, including data ingestion gateways, fusion engines, risk intelligence cores, and governance monitors. The architecture addresses interoperability challenges by incorporating standardized exchange frameworks and adaptive governance models, ensuring ethical deployment in clinical workflows. Theoretically, it models risk propagation through interpretive formulas that capture interactions between climatic variables and clinical vulnerabilities, while emphasizing feedback topologies for continuous system refinement. Without empirical evaluations, this work synthesizes the literature on clinical AI ecosystems to highlight ECSRO’s potential to mitigate health risks exacerbated by environmental stressors, such as extreme weather events and pollution. By fostering proactive intelligence, ECSRO aims to transform reactive healthcare into anticipatory systems, promoting resilience in vulnerable populations. Future implications include scalable infrastructure for global health surveillance, underscoring the need for interdisciplinary collaboration in AI-driven integration of environmental and clinical data.

Type 1 diabetes mellitus requires exogenous insulin and accurate glucose forecasting is critical for closed-loop artificial pancreas systems. While continuous glucose monitoring provides real-time data, purely data-driven recurrent neural networks may produce physiologically implausible predictions, and purely mechanistic models cannot fully capture individual variability in insulin sensitivity, meal absorption, or exercise response. This framework proposes a physics-guided recurrent neural network that integrates insulin delivery records, carbohydrate intake, and physical activity data. It combines a mechanistic glucose–insulin compartmental model with a residual LSTM network that learns patient-specific deviations, supported by a physics-based loss function enforcing physiological constraints such as non-negativity and realistic glucose dynamics. By merging physiological modeling with deep learning, the system preserves biological plausibility while adapting to individual patient patterns. Incorporating multimodal wearable and device data enables more accurate, longer-horizon glucose predictions, supporting safer and more proactive insulin dosing in closed-loop diabetes management.