Title: Artificial Intelligence Applications in Veterinary Antimicrobial Resistance Surveillance: A Systematic Review of Potential for Sri Lanka Introduction and Objectives: Antimicrobial resistance (AMR) is a critical One Health crisis in Sri Lanka, contributing to an estimated 2,610 attributable and 10,000 associated deaths in 2021. Within the veterinary sector, progress is constrained by limited laboratory capacity and fragmented surveillance. This systematic review evaluates the current and potential applications of artificial intelligence (AI) in veterinary AMR diagnostics and surveillance to identify viable solutions for the Sri Lankan context. Methods: Following PRISMA guidelines, a systematic review of peer-reviewed literature (2015–May 2026) was conducted using PubMed and Web of Science. The study synthesized AI methodologies applicable to low-resource settings, focusing on diagnostic accuracy and surveillance integration. Results: AI methodologies, including machine learning (e.g., Random Forests) and deep learning, are increasingly utilized for automated antimicrobial susceptibility testing (AST) and genomic interpretation. In Sri Lanka, critical data gaps exist in the poultry and dairy sectors, where recent 2025-2026 findings highlight a 75% resistance rate to tetracycline in biofilm-forming Escherichia coli. National isolates are further dominated by fluoroquinolone resistance, correlated with heavy veterinary enrofloxacin use. Our findings identify that AI-driven, smartphone-based computer-vision models for high-throughput AST screening can bypass laboratory infrastructure limitations by providing remote, standardized data interpretation. Conclusions: AI presents an immediate opportunity to integrate fragmented farm data and antibiotic sales to enhance national surveillance. AI-augmented diagnostics can significantly improve veterinary AMR control; however, success requires sustained investment in digital infrastructure and cross-sector One Health collaboration. Keywords: Antimicrobial Resistance, Artificial Intelligence, Veterinary Surveillance, One Health, Sri Lanka Authors: Hirudika, W.A.K.R., Vihanga, M.K.R., Srimal, R.P.Y.S. Status: Independent Student Research (2026)
Hirudika et al. (Thu,) studied this question.