How Does AI Support Antibiotic Stewardship Programs?

Author: Rasit Dinc

Introduction

Antimicrobial resistance (AMR) represents a significant global health crisis, threatening the efficacy of modern medicine. The overuse and misuse of antibiotics are primary drivers of this escalating problem, leading to increased morbidity, mortality, and healthcare costs [1]. In response, healthcare systems worldwide have implemented Antibiotic Stewardship Programs (ASPs) to optimize antibiotic use. These programs aim to ensure that patients receive the right antibiotic, at the right dose, for the right duration. Recently, the integration of Artificial Intelligence (AI) and Machine Learning (ML) has begun to revolutionize ASPs, offering powerful new tools to combat AMR. This article explores the pivotal role of AI in supporting and enhancing antibiotic stewardship efforts.

AI-Powered Predictive Analytics for Resistance Patterns

One of the most significant contributions of AI to antibiotic stewardship is its ability to predict antibiotic resistance patterns. By analyzing vast datasets from electronic health records (EHRs), AI algorithms can identify trends and forecast outbreaks of resistant bacteria. Machine learning models, such as logistic regression and gradient-boosting machines, can effectively differentiate between patients who require a change in their antibiotic regimen and those who do not [2]. This predictive capability allows clinicians to make more informed decisions, moving from a reactive to a proactive approach in managing infections.

AI-powered predictive analytics can identify patterns of resistance, forecast outbreaks, and guide personalized antibiotic therapies by leveraging large-scale data [3].

Optimizing Empirical Antibiotic Selection

Choosing the correct empirical antibiotic—the antibiotic prescribed before the specific causative pathogen is known—is critical for patient outcomes. AI-driven clinical decision support systems (CDSS) can assist clinicians in this complex process. These systems analyze patient-specific data, including clinical symptoms, laboratory results, and local resistance patterns, to recommend the most effective antibiotic. Studies have confirmed that machine learning plays a crucial role in optimizing empirical antibiotic selection and enhancing therapy appropriateness, which has the potential to reduce mortality rates [2]. By personalizing antibiotic recommendations, AI helps to improve the effectiveness of initial treatments and reduce the likelihood of treatment failure.

Precision in Antibiotic Dosing

Achieving the optimal dose of an antibiotic is a delicate balance between ensuring efficacy and minimizing toxicity. This is particularly challenging for antibiotics with a narrow therapeutic index, such as vancomycin. AI and ML algorithms have shown great promise in optimizing antibiotic dosing. By analyzing pharmacokinetic and pharmacodynamic data, these models can create personalized dosing regimens that maximize therapeutic success while minimizing adverse effects. This level of precision was previously unattainable and marks a significant step forward in patient safety and treatment efficacy [2].

Enhancing Adherence to Stewardship Guidelines

The success of any ASP depends on the consistent adherence of clinicians to its guidelines. AI can play a crucial role in monitoring and improving this adherence. AI systems can automatically audit antibiotic prescriptions in real-time, flagging those that deviate from established protocols. This provides an opportunity for immediate intervention and feedback, reinforcing best practices and ensuring that stewardship efforts are consistently applied. Furthermore, AI can help to identify and synthesize antimicrobial stewardship alerts and algorithms, prioritizing them to reduce alert fatigue among clinicians [4].

Conclusion

Artificial intelligence is rapidly transforming the landscape of antibiotic stewardship. From predicting resistance patterns and optimizing antibiotic selection to personalizing dosing and enhancing guideline adherence, AI offers a multifaceted approach to combating the threat of AMR. While challenges remain, particularly in bridging the implementation gap between high-income and low- and middle-income countries, the potential of AI to enhance the effectiveness of ASPs is undeniable. As AI technologies continue to evolve, their integration into routine clinical practice will be essential in preserving the efficacy of our most critical medicines for generations to come.

References

[1] World Health Organization. (2023). Antimicrobial resistance. https://www.who.int/news-room/fact-sheets/detail/antimicrobial-resistance

[2] Harandi, H., Shafaati, M., Salehi, M., Roozbahani, M. M., Mohammadi, K., Akbarpour, S., ... & Seifi, A. (2025). Artificial intelligence-driven approaches in antibiotic stewardship programs and optimizing prescription practices: A systematic review. Artificial Intelligence in Medicine, 162, 103089. https://www.sciencedirect.com/science/article/pii/S0933365725000247

[3] Pennisi, F., Lanza, G., Bellinvia, S., & Bella, G. D. (2025). The Role of Artificial Intelligence and Machine Learning in Antimicrobial Resistance Surveillance and Control. Microorganisms, 14(2), 134. https://www.mdpi.com/2079-6382/14/2/134

[4] Minnesota Department of Health. (n.d.). Artificial Intelligence for Antimicrobial Stewardship. https://www.health.state.mn.us/communities/onehealthabx/event/may16bl.pdf