Antimicrobial Resistance Pathogen Detection by AI and ML

Antimicrobial resistance is a global issue that is becoming worse. Machine learning and artificial intelligence applications in medicine are growing. The abundance of digital health information available has made it possible for researchers to use machine learning techniques to address a variety of medical issues. General bacterial infections that are inclined to incorrect diagnoses or excessive antibiotic use are known as antimicrobial resistance. New diagnostic techniques that offer quick identification of pathogens and antibiotic susceptibility testing are desperately required for proper, customized antibiotic treatment. In this study, we start by talking about new technologies that use AI and ML models to provide rapid diagnostic findings, especially for urinary tract infections. Afterward, we study genome sequencing data is used to guess antibiotic resistance using ML algorithms, enabling sequence-based diagnostics. Lastly, we look at several research that combine electronic health records with ML guide treatments without a urine culture, minimize the usage of antibiotics, diagnose urinary tract infections more accurately, and cut down on clinical burden and needless hospital stays.