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Antibiotics
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Digital Health and AI in Antibiotics Management and Antimicrobial Resistance...
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Digital Health and AI in Antibiotics Management and Antimicrobial Resistance Surveillance

A special issue of Antibiotics (ISSN 2079-6382).

Deadline for manuscript submissions: 31 August 2026 | Viewed by 397

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,

This Special Issue explores how digital health technologies and artificial intelligence are transforming antibiotics management, antimicrobial stewardship, and resistance surveillance. It invites original research articles and reviews addressing clinical applications, research innovations, and regulatory challenges in this critical field.

Digital Tools in Antibiotic Therapy

Digital health solutions, including clinical decision support systems (CDSS) and mobile apps, optimize antibiotic prescribing by integrating guidelines and real-time data. Studies show artificial intelligence-enhanced CDSS reduces unnecessary prescriptions, shortens de-escalation times by up to 24 h, and improves adherence to protocols in settings like outpatient telemedicine. For instance, machine learning models predict resistance patterns with AUROC scores around 0.80, aiding empirical therapy in urinary tract infections and pneumonia.

AMS and Surveillance artificial intelligence and machine learning enable predictive analytics for resistance detection via biosensors and IoT networks, supporting One Health surveillance across clinical, veterinary, and environmental domains. Reviews highlight ML's role in antimicrobial stewardship programs, identifying low-risk cases for discontinuation (60% detection rate) and reducing broad-spectrum use by 17–28%. Original research can demonstrate hybrid artificial intelligence-biosensor systems for rapid AMR tracking, addressing data scarcity through scalable models.

Regulatory and Ethical

Considerations Emerging frameworks from EMA and FDA outline principles for trustworthy artificial intelligence in medicine, emphasizing risk-based oversight, transparency, lifecycle management, and data governance for tools in stewardship. These include human-centric design, explainability, and ethical data sharing to mitigate biases in AMR applications. Contributions should examine compliance with global initiatives like WHO's AMR Action Plan and challenges in equitable deployment.

Submission Scope:

We welcome original articles on empirical evaluations (e.g., RCTs of AI-CDSS) and systematic reviews synthesizing evidence on digital antimicrobial stewardship impacts. Topics span telemedicine integration, biosurveillance algorithms, and policy analyses, with a focus on multicenter data from diverse settings. Submissions undergo peer review; aim for concise, impactful manuscripts advancing evidence-based stewardship in the artificial intelligence era.

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 250 words) can be sent to the Editorial Office for assessment.

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

  • digital tools
  • artificial intelligence
  • antibiotics management
  • antimicrobial stewardship

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Published Papers (1 paper)

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Research

12 pages, 998 KB  
Article
The Confounder in Plain Sight: A Retrospective Pilot Analysis on the Impact of Comorbidity on C-Reactive Protein Utility for Differentiating Bacterial vs. Viral Infections
by Alessandro Perrella, Paola Salvatore, Pierpaolo Di Micco, Ugo Trama, Antimo Di Spirito, Claudia Tiberio, Mariano Bernardo, Nicolina Capoluongo, Giusy Di Flumeri, Rita Boenzi and Francesca Futura Bernardi
Antibiotics 2026, 15(5), 510; https://doi.org/10.3390/antibiotics15050510 - 18 May 2026
Viewed by 172
Abstract
Background: The antimicrobial resistance crisis is driven by antibiotic overuse, often due to the difficulty in distinguishing bacterial from viral infections. In the European Union, acute respiratory tract infections account for about 38% of all antibiotic prescriptions in community and emergency settings, [...] Read more.
Background: The antimicrobial resistance crisis is driven by antibiotic overuse, often due to the difficulty in distinguishing bacterial from viral infections. In the European Union, acute respiratory tract infections account for about 38% of all antibiotic prescriptions in community and emergency settings, and an estimated 30–50% of these prescriptions are potentially inappropriate. Point-of-care C-reactive protein (CRP) testing can support the distinction between bacterial and viral infections, but its diagnostic accuracy is often compromised by chronic inflammatory comorbidities that elevate baseline CRP levels. Objective: This exploratory, hypothesis-generating study evaluated the diagnostic utility of CRP in an Emergency Department (ED) cohort and proposed a novel “Comorbidity Confounder Score” (CCS) prototyped pilot tool as support to identify patient subgroups in whom CRP retains high diagnostic value. Methods: We conducted an exploratory, hypothesis-driven retrospective cohort study of 92 patients presenting to a tertiary ED with acute flu-like symptoms between 2023 and 2025. Microbiological diagnoses were confirmed using culture and PCR. ROC curve analysis and AUC comparisons were performed using the pROC package in R (v4.2.0; DeLong method). A post hoc power analysis confirmed 81% power at alpha = 0.05. The diagnostic performance of CRP (Area Under the Curve—AUC) was assessed in the total cohort and stratified into “Low-Utility” (high comorbidity, CCS ≥ 2) and “High-Utility” (low comorbidity, CCS < 2) subgroups. Results: In the unselected total cohort, CRP demonstrated suboptimal diagnostic performance (AUC = 0.61, 95% CI: 0.49–0.73). However, exploratory post hoc stratification revealed divergence. In the “Low-Utility” group, CRP had no diagnostic value (AUC = 0.52). In the “High-Utility” group, a preliminary signal of improved CRP discriminatory performance was observed (AUC = 0.84; DeLong test vs. total cohort, p = 0.004), subject to the optimistic bias inherent in derivation-cohort stratification. The AUC improvement was statistically significant (DeLong test, p = 0.004). The empirically derived optimal cutoff in the High-Utility group was 31.5 mg/L (Youden Index J = 0.54). Conclusions: These exploratory, post hoc findings are a first step into evaluation based on a pilot ML tool and require prospective multicenter validation before any conclusions regarding clinical utility can be drawn. The CCS represents a hypothesis-generating construct only and must not be used for clinical decision-making in its current form. Full article
(This article belongs to the Special Issue Digital Health and AI in Antibiotics Management and Antimicrobial Resistance Surveillance)
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