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Antimicrobial Stewardship in the Digital Age: The Role of Artificial...
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Antimicrobial Stewardship in the Digital Age: The Role of Artificial Intelligence and Chatbots in Future Strategies

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

Deadline for manuscript submissions: 15 April 2025 | Viewed by 5773

Special Issue Editor

Dr. Alessandro Perrella

E-Mail Website
Guest Editor
Emerging Infectious Disease at High Countagiousness, AORN Ospedali dei Colli—P.O. D.Cotugno, 80131 Naples, Italy
Interests: infectious disease; transplants; immunology; AI; healthcare management; antimicrobial stewardship
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Antimicrobial resistance (AMR) is a significant global health threat that demands urgent attention. Antimicrobial stewardship (AMS) is an essential strategy for combatting AMR, focusing on the responsible use of antibiotics to preserve their efficacy. With the increasing availability of digital technologies, including artificial intelligence (AI) and chatbots, there is a growing opportunity to improve AMS strategies and outcomes. This Special Issue aims to explore the role of AI and chatbots in AMS, using ChatGPT or any other AI system as a support in decision making for new AMS approaches for future strategies. As a state-of-the-art AI model, ChatGPT can provide valuable insights and perspectives on the role of AI and chatbots in AMS.

Therefore, the Special Issue will focus on the following topics:

The current state of AMS and the challenges faced in implementing effective strategies. The potential of AI and chatbots in enhancing AMS efforts, including their applications in decision making, education, and communication with patients and healthcare providers. The ethical and legal considerations of using AI and chatbots in AMS case studies and best practices of AI and chatbot implementation in AMS, including the role of ChatGPT in supporting AMS efforts.

The Special Issue is intended for healthcare professionals, researchers, and policymakers interested in the fields of AMS, AI, and chatbots. Indeed, it has the potential to contribute significantly to the growing body of research on AMS and future strategies. Including ChatGPT and AI as supports would provide a unique and valuable perspective on the role of AI and chatbots in AMS, highlighting the potential of these technologies to enhance the responsible use of antibiotics and combat AMR.

Dr. Alessandro Perrella
Guest Editor

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

  • AI
  • ChatGpt
  • AMS
  • AMR
  • infection
  • hospital aquired infection

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Published Papers (2 papers)

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16 pages, 648 KiB  
Article
An Institutional Febrile Neutropenia Protocol Improved the Antibacterial Treatment and Encouraged the Development of a Computerized Clinical Decision Support System
by Zahit Taş, Gökhan Metan, Gülçin Telli Dizman, Eren Yavuz, Ömer Dizdar, Yahya Büyükaşık, Ömrüm Uzun and Murat Akova
Antibiotics 2024, 13(9), 832; https://doi.org/10.3390/antibiotics13090832 - 2 Sep 2024
Viewed by 1001
Abstract
We investigated the influence of a local guideline on the quality of febrile neutropenia (FN) management and the applicability of a computerized decision support system (CDSS) using real-life data. The study included 227 FN patients between April 2016 and January 2019. The primary [...] Read more.
We investigated the influence of a local guideline on the quality of febrile neutropenia (FN) management and the applicability of a computerized decision support system (CDSS) using real-life data. The study included 227 FN patients between April 2016 and January 2019. The primary outcome measure was the achievement of a 20% increase in the rate of appropriate empirical treatment of FN in bacteremic patients. The compatibility of the CDSS (the development of which was completed in November 2021) with local protocols was tested using standard patient scenarios and empirical antibiotic recommendations for bacteremic FN patients. In total, 91 patients were evaluated before (P1: between April 2016 and May 2017) and 136 after (P2: between May 2017 and January 2019) the guideline’s release (May 2017). The demographic characteristics were similar. Appropriate empirical antibacterial treatment was achieved in 58.3% of P1 and 88.1% of P2 patients (p = 0.006). The need for escalation of antibacterial treatment was significantly lower in P2 (49.5% vs. 35.3%; p = 0.03). In P2, the performance of the CDSS and consulting physicians was similar (CDSS 88.8% vs. physician 88.83%; p = 1) regarding appropriate empirical antibacterial treatment. The introduction of the local guideline improved the appropriateness of initial empirical treatment and reduced escalation rates in FN patients. The high rate of compliance of the CDSS with the local guideline-based decisions in P2 highlights the usefulness of the CDSS for these patients. Full article
(This article belongs to the Special Issue Antimicrobial Stewardship in the Digital Age: The Role of Artificial Intelligence and Chatbots in Future Strategies)
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20 pages, 990 KiB  
Systematic Review
Brave New World of Artificial Intelligence: Its Use in Antimicrobial Stewardship—A Systematic Review
by Rafaela Pinto-de-Sá, Bernardo Sousa-Pinto and Sofia Costa-de-Oliveira
Antibiotics 2024, 13(4), 307; https://doi.org/10.3390/antibiotics13040307 - 28 Mar 2024
Cited by 10 | Viewed by 4070
Abstract
Antimicrobial resistance (AMR) is a growing public health problem in the One Health dimension. Artificial intelligence (AI) is emerging in healthcare, since it is helpful to deal with large amounts of data and as a prediction tool. This systematic review explores the use [...] Read more.
Antimicrobial resistance (AMR) is a growing public health problem in the One Health dimension. Artificial intelligence (AI) is emerging in healthcare, since it is helpful to deal with large amounts of data and as a prediction tool. This systematic review explores the use of AI in antimicrobial stewardship programs (ASPs) and summarizes the predictive performance of machine learning (ML) algorithms, compared with clinical decisions, in inpatients and outpatients who need antimicrobial prescriptions. This review includes eighteen observational studies from PubMed, Scopus, and Web of Science. The exclusion criteria comprised studies conducted only in vitro, not addressing infectious diseases, or not referencing the use of AI models as predictors. Data such as study type, year of publication, number of patients, study objective, ML algorithms used, features, and predictors were extracted from the included publications. All studies concluded that ML algorithms were useful to assist antimicrobial stewardship teams in multiple tasks such as identifying inappropriate prescribing practices, choosing the appropriate antibiotic therapy, or predicting AMR. The most extracted performance metric was AUC, which ranged from 0.64 to 0.992. Despite the risks and ethical concerns that AI raises, it can play a positive and promising role in ASP. Full article
(This article belongs to the Special Issue Antimicrobial Stewardship in the Digital Age: The Role of Artificial Intelligence and Chatbots in Future Strategies)
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