Abstract

Snakebite envenoming is a significant global health crisis, disproportionately affecting rural populations in resource-limited regions. Current point-of-care diagnostics, such as Snake Venom Detection Kits (SVDKs), are hampered by qualitative limitations, an inability to perform precise species identification, and reliance on a stable cold chain, often leading to delayed or incorrect clinical interventions (WHO, n.d.). This paper presents a proof-of-concept for an integrated, field-deployable system designed to overcome these challenges. The proposed methodology integrates four technological pillars: 1) In-situ venom profiling using portable Raman or Fourier Transform Infrared (FTIR) spectroscopy to generate a unique molecular signature from a blood sample. 2) Secure, wireless transmission of spectral data via Bluetooth Low Energy (BLE), formatted in JSON and structured for healthcare interoperability using the FHIR protocol. 3) A cloud-based Artificial Intelligence (AI) platform for data interpretation, utilizing machine learning models for venom identification and a Large Language Model (LLM) for clinical decision support. 4) A forward-looking capability for on-demand therapeutic synthesis, where the AI designs a neutralizing molecule (e.g., a miniprotein) for subsequent on-site manufacturing. An analysis of current and emerging technologies confirms the system's feasibility. While significant challenges related to therapeutic-grade manufacturing, the compilation of a comprehensive venom library, and regulatory navigation remain, the foundational technologies are sufficiently mature to enable a paradigm shift toward rapid, precise, and personalized snakebite treatment at the point of care.