Tackling Antimicrobial Resistance with Artificial Intelligence: Challenges and Opportunities

Abstract

The abuse of antibiotics and bacterial genetics have led to the serious worldwide health concern known as antibiotic resistance (AMR). Through a variety of techniques, artificial intelligence (AI) provides promising solutions. Early resistance marker discovery is made easier by AI-driven genetic analysis, and antibiotic use is optimized by AI-powered decision support systems that suggest appropriate treatments based on patient data. AI speeds up the discovery of novel antibacterial agents and forecasts the potency of compounds in drug development. By enhancing pathogen identification, resistance prediction, and epidemiological surveillance, machine learning (ML) improves microbiological diagnostics. Prominent developments like AlphaFold2 have transformed the prediction of protein structures, influencing industry and medication development. AI enhances antimicrobial stewardship by facilitating quick antimicrobial susceptibility testing and increasing diagnosis accuracy. A multidisciplinary approach is needed to address issues like data quality and model interpretability. AI must be integrated with developing technologies, and medical and technology areas must work together to solve these problems.