Systematic review evaluates AI and ML potential for improving antimicrobial resistance monitoring in Sri Lanka's veterinary sector, suggesting digital transformation.
Title: Artificial Intelligence Applications in Veterinary Antimicrobial Resistance Surveillance: A Systematic Review of Potential for Sri Lanka Summary: This systematic review investigates the integration of Artificial Intelligence (AI) and Machine Learning (ML) to address the escalating crisis of Antimicrobial Resistance (AMR) within the veterinary sector of Sri Lanka. Using PRISMA guidelines, the study synthesizes current global AI methodologies—specifically smartphone-based computer vision for automated AST and predictive analytics—and evaluates their technical feasibility within low-resource diagnostic frameworks. The findings highlight a critical need for high-throughput, automated surveillance to combat high resistance rates in local poultry and dairy sectors. This work serves as an independent evidence-based proposal for digital transformation in regional One Health surveillance. Authors: Hirudika, W.A.K.R., Vihanga, M.K.R., Srimal, R.P.Y.S.
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Hirudika et al. (2026) studied this question.
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