Antimicrobial Resistance (AMR) is a rising global problem and is a major challenge for modern healthcare systems. To prevent AMR, Antimicrobial Stewardship Programs (AMS) were introduced, and their implementation has led to significant changes in the regulation, surveillance, and usage of antimicrobial drugs with a primary goal to prevent the transmission of multidrug-resistant pathogens. However, several factors, such as the delay in microbiological diagnosis and the variability in each individual's pharmacokinetics, constrain these efforts. This phenomenon leads to irrational prescription of broad-spectrum antibiotics, thereby leading to higher rates of resistance. Artificial intelligence (AI), from advanced machine‑learning algorithms to natural language processing and autonomous systems, is increasingly being integrated into healthcare, offering a transformative, data‑driven way to support clinical decision‑making. This comprehensive literature review explores how AI is currently being used in antimicrobial stewardship and provides an in‑depth look at key performance metrics that highlight how algorithmic models can outperform traditional methods in choosing empirical therapies and improving diagnostic accuracy. The review also looks into how machine learning tools are helping to improve pharmacokinetic and pharmacodynamic dosing. Finally, the study addresses major social and technical challenges such as algorithmic bias, the lack of transparency often seen in deep neural networks, and the significant infrastructure gaps that exist between high‑income countries and low‑ and middle‑income regions that limit the wider adoption of these technologies.
Dr. Swathi Gurajala*1, Dr. Gayathri Pandurangam2, Ms. Sai Saranya3 (Sat,) studied this question.