Artificial intelligence in optimizing antimicrobial therapy for gastro-renal disorders.

Area of Science:

• Medical Informatics
• Infectious Diseases
• Pharmacology

Background:

• Antimicrobial therapy is crucial for gastrointestinal and urinary tract infections but faces challenges from resistance and microbiome disruption.
• Gastro-renal settings present unique pharmacokinetic variability and narrow therapeutic margins, increasing risks of treatment failure and recurrence.
• Current empiric regimens may exacerbate resistance and disease recurrence, necessitating innovative management strategies.

Purpose of the Study:

• To review current artificial intelligence (AI)-driven strategies for optimizing antimicrobial therapy in gastro-renal infections.
• To highlight shared pathophysiological challenges, clinical applications, and limitations of AI in this context.
• To propose an integrated framework for AI-assisted antimicrobial optimization.

Main Methods:

• Literature review synthesizing current AI and machine learning applications in antimicrobial therapy.
• Analysis of data integration strategies (clinical, microbiological, multi-omics) for predicting resistance and guiding treatment.
• Examination of challenges to clinical translation, including data heterogeneity and regulatory hurdles.

Main Results:

• AI and machine learning offer potential to predict antimicrobial resistance, guide antibiotic selection and dosing, and support stewardship.
• Integration of diverse data types can personalize antimicrobial therapy for complex infections.
• Significant limitations exist, including data heterogeneity, need for validation, and regulatory considerations.

Conclusions:

• AI presents a promising avenue for enhancing antimicrobial therapy efficacy in gastro-renal infections.
• An integrated AI framework can help mitigate antimicrobial resistance and preserve microbiome integrity.
• Continued research and validation are essential for successful clinical translation of AI-driven antimicrobial optimization.