A.I in One Health, a multi-threat mitigation strategy for enhancing global health security in Nairobi Kenya.

Background: The convergence of climate change, intensive livestock production, and rapid urbanization has escalated risks of zoonotic spillovers and antimicrobial resistance (AMR). This study presents a proactive diagnostic framework (OMNi-scanner) aligning with Kenya’s National Public Health Security Strategic Plan-2 (2026–2030) and the International Health Regulations (IHR 2005). The research facilitates a transition in disease monitoring from reactive models to an automated, AI-driven intelligence system.To validate OMNi as a multi-modal diagnostic tool for Kenya’s One Health strategy by: Systemic Fever Screening: Identifying febrile states indicative of zoonotic viral outbreaks. Localized Pathological Mapping: Utilizing thermal gradients to detect oncogenic tumors, internal injuries, and superficial anomalies. AMR Proxy Surveillance: Mapping thermographic signatures of injection sites to monitor antibiotic administration and avert residue risks.OMNi demonstrates that thermographic AI can unify disparate health sectors—pathology, biosecurity, and food safety—into a single screening event. Mapping oncogenic tumors alongside internal injuries allows for a more nuanced veterinary triage, while the ability to "see" injection sites provides a breakthrough tool for monitoring Antimicrobial Resistance (AMR) in livestock markets.OMNi represents a paradigm shift in One Health surveillance, transitioning from reactive veterinary care to proactive, data-driven biosecurity. By integrating AI-thermography into national health frameworks, Kenya can significantly enhance its capacity for real-time zoonotic risk mitigation and antimicrobial stewardship.