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

A special issue of Antibiotics (ISSN 2079-6382). This special issue belongs to the section "Antibiotics Use and Antimicrobial Stewardship".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 8220

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Unit of Statistics, Faculty of Medicine, University Campus Bio-Medico of Rome, 00128 Rome, Italy
Interests: epidemiology; statistics; public health; artificial intelligence; predictive modeling; infectious diseases; data analysis
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Special Issue Information

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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Antibiotics is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • antimicrobial resistance
  • artificial intelligence
  • digital health
  • drug discovery
  • patient-centric care
  • therapeutic innovations

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Published Papers (2 papers)

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Review

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38 pages, 1336 KiB  
Review
Artificial Intelligence in Bacterial Infections Control: A Scoping Review
by Rasha Abu-El-Ruz, Mohannad Natheef AbuHaweeleh, Ahmad Hamdan, Humam Emad Rajha, Jood Mudar Sarah, Kaoutar Barakat and Susu M. Zughaier
Antibiotics 2025, 14(3), 256; https://doi.org/10.3390/antibiotics14030256 - 2 Mar 2025
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Abstract
Background/Objectives: Artificial intelligence has made significant strides in healthcare, contributing to diagnosing, treating, monitoring, preventing, and testing various diseases. Despite its broad adoption, clinical consensus on AI’s role in infection control remains uncertain. This scoping review aims to understand the characteristics of [...] Read more.
Background/Objectives: Artificial intelligence has made significant strides in healthcare, contributing to diagnosing, treating, monitoring, preventing, and testing various diseases. Despite its broad adoption, clinical consensus on AI’s role in infection control remains uncertain. This scoping review aims to understand the characteristics of AI applications in bacterial infection control. Results: This review examines the characteristics of AI applications in bacterial infection control, analyzing 54 eligible studies across 5 thematic scopes. The search from 3 databases yielded a total of 1165 articles, only 54 articles met the eligibility criteria and were extracted and analyzed. Five thematic scopes were synthesized from the extracted data; countries, aim, type of AI, advantages, and limitations of AI applications in bacterial infection prevention and control. The majority of articles were reported from high-income countries, mainly by the USA. The most common aims are pathogen identification and infection risk assessment. The most common AI used in infection control is machine learning. The commonest reported advantage is predictive modeling and risk assessment, and the commonest disadvantage is generalizability of the models. Methods: This scoping review was developed according to Arksey and O’Malley frameworks. A comprehensive search across PubMed, Embase, and Web of Science was conducted using broad search terms, with no restrictions. Publications focusing on AI in infection control and prevention were included. Citations were managed via EndNote, with initial title and abstract screening by two authors. Data underwent comprehensive narrative mapping and categorization, followed by the construction of thematic scopes. Conclusions: Artificial intelligence applications in infection control need to be strengthened for low-income countries. More efforts should be dedicated to investing in models that have proven their effectiveness in infection control, to maximize their utilization and tackle challenges. Full article
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15 pages, 3711 KiB  
Perspective
Implications of Artificial Intelligence in Addressing Antimicrobial Resistance: Innovations, Global Challenges, and Healthcare’s Future
by Francesco Branda and Fabio Scarpa
Antibiotics 2024, 13(6), 502; https://doi.org/10.3390/antibiotics13060502 - 29 May 2024
Cited by 13 | Viewed by 5111
Abstract
Antibiotic resistance poses a significant threat to global public health due to complex interactions between bacterial genetic factors and external influences such as antibiotic misuse. Artificial intelligence (AI) offers innovative strategies to address this crisis. For example, AI can analyze genomic data to [...] Read more.
Antibiotic resistance poses a significant threat to global public health due to complex interactions between bacterial genetic factors and external influences such as antibiotic misuse. Artificial intelligence (AI) offers innovative strategies to address this crisis. For example, AI can analyze genomic data to detect resistance markers early on, enabling early interventions. In addition, AI-powered decision support systems can optimize antibiotic use by recommending the most effective treatments based on patient data and local resistance patterns. AI can accelerate drug discovery by predicting the efficacy of new compounds and identifying potential antibacterial agents. Although progress has been made, challenges persist, including data quality, model interpretability, and real-world implementation. A multidisciplinary approach that integrates AI with other emerging technologies, such as synthetic biology and nanomedicine, could pave the way for effective prevention and mitigation of antimicrobial resistance, preserving the efficacy of antibiotics for future generations. Full article
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