Comprehensive Summary
This study investigates the application of Artificial Intelligence (AI) and Machine Learning (ML) models in detecting antibiotic residue in food products and environmental samples. Venkateswaramurthy et al. conducted a systematic literature review using studies published between 2020 and 2024 from PubMed and other databases. The review focused on keywords such as “AI”, “Machine Learning”, “Antibiotic Residue Detection,” and “Food Safety”. The analysis revealed that AI-enhanced biosensors, optical systems, and electrochemical platforms significantly outperform traditional methods for detecting antibiotic residues in food products. Electrochemical sensors demonstrated a 99% accuracy score for classification, while ML-powered optical immunosensors had detection limits as low as 0.03–0.4 ng/mL. Convolutional Neural Networks (CNNs) were able to distinguish overlapping signals accurately (R² > 0.984), and smartphone-based tools could detect antibiotics with high precision. Despite these improvements, there is still a need for standardized methods and testing across different sample types. Overall, using AI and ML models represents a major step forward in detecting antibiotic residues.
Outcomes and Implications
This research is important because antibiotic residues are a serious public health concern, contributing to the rise of antimicrobial resistance and causing toxic effects from food consumption. The integration of AI/ML models into detection systems has the potential to improve food safety monitoring by reducing the risk of resistant infections and enhancing sensitivity and specificity in detection methods. These models can increase the chances of early intervention along with accurate detection rates. Overall, AI-driven detection methods have the potential to become standard tools for ensuring food safety and regulations.