EMERGENCE: A Human-Centered Artificial Intelligence Platform for Pre-Hospital Emergency Care

Injuries account for more than 4.4 million deaths annually worldwide, a large share of which occur during the pre-hospital "golden hour" and involve out-of-hospital cardiac arrest (OHCA), major trauma, acute stroke, and severe anaphylaxis. Contemporary emergency medical services (EMS) are hampered by fragmented instrumentation, with reported treatment delays on the order of 15 min and high operator cognitive load (NASA-TLX scores ≈ 65). We present EMERGENCE, an integrated, ruggedized platform that combines continuous multi-parameter physiological monitoring, microfluidic point-of-care (POC) diagnostics, automated therapeutic administration, edge artificial intelligence (AI), and secure telemedicine within a single device. Designed for autonomous operation exceeding 72 h, EMERGENCE targets IP67 ingress protection and alignment with ISO 13485, IEC 60601-1, and the FDA Software-as-a-Medical-Device (SaMD) framework. The AI subsystem provides evidence-based, confidence-calibrated decision support and requires dual authorization (biometric authentication together with remote clinician oversight, or an AI confidence exceeding 95%) for high-acuity interventions. The targeted unit cost is 1200 USD, supporting scalability in low- and middle-income countries (LMICs). This article sets out the theoretical basis, system architecture, mathematical formulations, safety-engineering protocols, bias-mitigation strategy, ethical framework, an explicit scientific and technical risk assessment, a falsifiability-oriented validation roadmap, and a 36-month development plan. Using a physiologically calibrated synthetic dataset, a fully reproducible classification pipeline attains an internal test-set area under the receiver operating characteristic curve (AUC) of 0.9988. We emphasize that this value reflects the deliberately high separability of the synthetic distributions and constitutes a pipeline-consistency benchmark rather than an estimate of clinical performance; sensitivity and robustness analyses quantify how performance degrades under increasing distributional overlap, sensor noise, and label noise. A literature-anchored survival model projects a 40–60% reduction in time-to-treatment, corresponding to an absolute OHCA survival gain of up to ≈ 22 percentage points (number needed to treat, NNT ≈ 5).