Description
Antimicrobial resistance is one of the emerging global “silent threats”, increasing the burden on healthcare and the economy. The mortality rate will increase up to 10 million per year by 2050. Thus it becomes vital for the early detection and prevention of AMR. The reliance on Artificial Intelligence (AI) and Machine Learning (ML) is increasing due to their higher data acquisition and processing precision. This supports the healthcare industry by reducing the time in studying the global scenario of AMR. AI finds its application in designing various predictive and forecasting models to detect and differentiate AMR from typical infections in an individual. They also help devise algorithms such as decision trees, random forests, deep neural networking, support vector machine, etc., for accurate prescriptions, including the regimen and dose required for the drug therapy, thus promoting the rational use of personalized medicines. The central focus of the research is to set up a worldwide surveillance system and a stewardship program to monitor antimicrobial-resistant infections. The AI can also help screen millions of compounds within a short period against multiple pathogens to discover new antibiotics. The application of AI in AMR is not to replace the expertise of physicians but to accelerate detection and prevention.