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

Globally, over 4.4 million deaths attributable to injuries occur annually, with a substantial proportion transpiring during the critical pre-hospital "golden hour," encompassing out-of-hospital cardiac arrest (OHCA), major trauma, acute stroke, and severe anaphylaxis [1]. Contemporary emergency medical services (EMS) are encumbered by fragmented instrumentation, engendering mean treatment delays of 15 min [2] and protracted cognitive workloads (NASA-TLX scores ≈65) [3]. Herein, we delineate EMERGENCE, an integrated, ruggedized platform that amalgamates continuous multi-parameter physiological monitoring, microfluidic point-of-care (POC) diagnostics, automated therapeutic administration, edge-based artificial intelligence (AI), and secure telemedicine into a singular apparatus. Engineered for autonomous functionality exceeding 72 h, EMERGENCE attains IP67 ingress protection and adheres to ISO 13485, IEC 60601-1, and FDA Software as a Medical Device (SaMD) paradigms [4]. The AI subsystem furnishes evidence-based, confidence-calibrated decision support, mandating dual authorization (biometric authentication coupled with remote clinician oversight or AI confidence exceeding 95%) for high-acuity interventions [5]. Targeted unit cost is $1200, facilitating scalability in low- and middle-income countries (LMICs). This exposition articulates the rigorous theoretical underpinnings, architectural schema, mathematical formulations, safety engineering protocols, bias attenuation strategies, ethical imperatives, and a 36-month validation trajectory. Closed-form simulations leveraging physiologically calibrated synthetic datasets prognosticate a 40%–60% diminution in time-to-treatment, commensurate with an absolute OHCA survival augmentation of up to 22 percentage points [6].