Artificial Intelligence and Antimicrobial Resistance: Innovation, Global Challenges and the Future of Healthcare

Dear Colleagues,

The growing threat of antimicrobial resistance (AMR) has made finding innovative solutions in the field of antibiotics crucial. Traditional methods for identifying new antibiotic compounds often involve exhaustive trial-and-error processes that are slow and expensive. In contrast, artificial intelligence (AI) emerges as a powerful tool that can analyze large amounts of data, identify useful patterns and improve treatment protocols. AI thus promises to accelerate the discovery of new antibiotic compounds and personalize prescriptions to maximize therapeutic efficacy. In addition, AI-driven surveillance systems can detect emerging resistance trends and inform targeted interventions. This Special Issue aims to explore the potential of AI in combating antimicrobial resistance, with the goal of developing sustainable solutions to preserve antibiotic efficacy and ensure effective antimicrobial therapy in the treatment of infectious diseases.

The scope of this Special Issue includes the following topics:

1. AI-driven approaches for accelerating antibiotic discovery: Exploring machine learning algorithms and data analytics techniques to identify novel antibiotic compounds with enhanced efficacy and reduced likelihood of resistance development;
2. Personalized antibiotic therapy using AI: Investigating how AI algorithms can analyze patient data to tailor antibiotic prescriptions based on individual profiles, optimizing treatment outcomes while minimizing the risk of antimicrobial resistance;
3. AI-powered surveillance systems for monitoring antimicrobial resistance: Examining the role of AI in real-time data analysis to detect emerging resistance trends, identify high-risk populations and guide targeted interventions for containment;
4. AI-driven strategies for optimizing antibiotic stewardship programs: Assessing the effectiveness of AI-driven decision support systems in promoting judicious antibiotic use, reducing unnecessary prescriptions and mitigating the spread of resistant pathogens;
5. Interdisciplinary approaches to combatting antimicrobial resistance: Encouraging collaborations between AI researchers, microbiologists, pharmacologists and healthcare professionals to develop holistic solutions for addressing the complex challenges posed by antimicrobial resistance;
6. Future directions in AI research for antimicrobial resistance: Proposing novel methodologies, technologies and research priorities to advance the field of AI-driven antimicrobial resistance research and accelerate progress toward sustainable solutions for preserving antibiotic efficacy.

Dr. Fabio Scarpa
Dr. Francesco Branda
Guest Editors

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