Regulatory and scientific frameworks for AI-driven biosensor technologies in antimicrobial resistance monitoring

Antimicrobial resistance (AMR) diagnostics require evidence matched to the intended clinical or surveillance use. This narrative review synthesizes biosensor research, artificial intelligence (AI) analytics, and device oversight through a regulatory-scientific lifecycle perspective. Electrochemical and optical transduction are distinguished from microfluidic sample handling and nanomaterial enhancement. Pathogen identification, resistance-gene detection, phenotypic antimicrobial susceptibility testing (AST), minimum inhibitory concentration (MIC) prediction, and population surveillance are compared by output, reference standard, and validation requirement. Representative Raman-machine-learning and microfluidic studies illustrate both analytical promise and limitations in independent clinical validation. The review maps these findings to US and European device pathways, software controls, interoperability, cybersecurity, and data governance. Its proposed five-phase framework adapts established total-product-lifecycle, quality-management, and risk-management principles to AMR-specific challenges, including emerging resistance mechanisms, breakpoint revisions, specimen shifts, and algorithm changes. The framework is a conceptual implementation tool, not a new regulatory pathway or an empirically validated model. Safe translation requires claim-specific validation and controlled updates rather than extrapolation from isolated prototype performance.