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Artificial intelligence in antimicrobial stewardship: prediction, clinical applications, and implementation
Takahiro Matsuo1, Masayuki Nigo2, Fabio Borgonovo3
1Department of Infectious Diseases, Infection Control and Employee Health, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Summary
Artificial intelligence (AI) can enhance antimicrobial stewardship by improving resistance prediction and optimizing treatment. Successful implementation requires careful integration into clinical workflows and clinician oversight.
Area of Science:
- Infectious Diseases
- Medical Informatics
- Computational Biology
Background:
- Antimicrobial resistance (AMR) poses a significant threat to healthcare.
- Antimicrobial stewardship programs (ASPs) face challenges due to increasing complexity and data volume.
- Artificial intelligence (AI) offers potential solutions for optimizing antimicrobial use.
Purpose of the Study:
- To review current AI applications in antimicrobial stewardship.
- To emphasize AI's role in resistance prediction, risk stratification, and therapy selection.
- To discuss implementation challenges of AI in clinical practice.
Main Methods:
- Review of studies on AI and machine learning for AMR prediction.
- Evaluation of AI in diagnostic stewardship and antimicrobial optimization.
- Analysis of AI implementation in real-world clinical settings.
Main Results:
- AI models can predict AMR and identify high-risk patients using electronic health records.
- AI aids in optimizing diagnostic tests and interpreting results.
- AI supports therapeutic decisions like de-escalation and treatment duration adjustment.
Conclusions:
- AI can enhance antimicrobial stewardship with precise, scalable, and integrated decision support.
- Future research should focus on prospective evaluation and careful implementation.
- AI should augment, not replace, clinical judgment in antimicrobial stewardship.
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