Applications of Artificial Intelligence (AI) in Enhancing Laboratory Procedures and Antimicrobial Resistance Detection
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Abstract
The role of artificial intelligence (AI) in providing global solutions and public awareness in regards to antimicrobial resistance can never be overemphasized. This review aims at explaining key roles AI has demonstrated in creating global solutions and awareness of antibiotic resistance through media orientation, data integration, use of highly sophisticated Chabot and AI methods which can potentially augment the detection of antibiotic resistance genes and enhance the discovery of novel drugs by predicting the efficacy of new compounds and potential antimicrobial agents and modifying existing ones. It further explains how to employ AI technology against the rational use of antibiotics and its combinations, more so, commonly used AI algorithms for antibiotic resistance such as eXtreme Gradient Boosting (XGBoost), Random forest (RF), Decision tree (DT), Support vector machine (SVM), Naive Bayes (NB), Artificial neural network (ANN) Logistic Regression (LR) and more were discussed. It is therefore evident that AI renders a powerful toolkit for addressing global health challenges, particularly antimicrobial resistance by improving diagnostic capabilities, surveillance and public education. However, a holistic interdisciplinary approach to incorporate AI with technologies like synthetic biology and nano-medicine is recommended for promoting effective management and control of antimicrobial resistance and ensuring sustainability of antimicrobials for future generations.
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