AI Risk Intelligence for Antimicrobial Resistance: A Policy-to-Implementation Blueprint for Stewardship and Biosecurity

Abstract: Antimicrobial resistance (AMR) is a slow-burning biosecurity threat that emerges from routine clinical and agricultural decisions, amplified by surveillance gaps, unequal access to diagnostics, and fragmented stewardship governance. AI risk intelligence can help identify emerging resistance patterns, forecast treatment failure risk, and target stewardship interventions, yet it also introduces new risks of overclaiming, biased allocation, privacy harms, and operational brittleness when deployed across heterogeneous settings. This manuscript develops a policy-to-implementation blueprint for AMR risk intelligence that treats analytics as a governed capability: claims are bounded by explicit assumptions, evaluated through robustness and sensitivity protocols, and monitored for drift and harm. Across five chapters, the book integrates foundations and research design under uncertainty; statistical modeling for explanation and causal inference about stewardship interventions; machine learning for prediction, generalization, and trustworthiness; big-data engineering for reproducible surveillance and MLOps-style operations; and applied sectoral playbooks spanning hospitals, communities, agriculture, and cross-border coordination. The research outputs are operational and audit-ready: AMR risk registers, evaluation covenants, monitoring charters, data governance templates, and decision tools for policymakers and implementers across South Asia, Europe, Africa, and the Americas. Keywords antimicrobial resistance, AI risk intelligence, stewardship governance, biosecurity, surveillance systems, causal inference, robust evaluation, sensitivity analysis, prediction and calibration, distribution shift, trustworthy machine learning, diagnostic pathways, privacy and confidentiality, health equity, One Health, reproducibility, MLOps, auditability, policy implementation, cross-border coordination