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Diagnostics
Special Issues
Application of Artificial Intelligence in Infectious Disease Diagnosis
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Application of Artificial Intelligence in Infectious Disease Diagnosis

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Diagnostic Microbiology and Infectious Disease".

Deadline for manuscript submissions: closed (28 February 2026) | Viewed by 1196

Special Issue Editor

Dr. Thomas Foster Scherr

E-Mail Website
Guest Editor
Department of Chemistry, Vanderbilt University, 1234 Stevenson Center Lane, Nashville, TN 37212, USA
Interests: mobile health; diagnostics; disease surveillance; immunoassays; data science
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Infectious disease diagnostics stand at a transformative crossroads, where clinical medicine converges with disruptive technologies to redefine healthcare delivery. The convergence of mobile health (mHealth) platforms, artificial intelligence (AI)-driven analytics, and miniaturized diagnostic technologies is ushering in a new era of decentralized precision diagnostics.

This Special Issue highlights innovations that

  • Democratize testing through smartphone-based devices, wearable sensors, and frugal diagnostics for low-resource settings;
  • Harness AI/ML for real-time pathogen detection, antimicrobial resistance prediction, and outbreak forecasting;
  • Bridge the diagnostic gap between clinics and communities via telemedicine-integrated platforms and cloud-based surveillance networks;
  • Push the boundaries of assay design with CRISPR-based diagnostics, lab-on-a-chip systems, and multi-omics integration.

We invite contributions that explore technology development, clinical validation, or implementation science. Original research articles and reviews are welcome. Important, novel and interesting short communications and interesting images will also be considered. Research areas will mainly include (but are not limited to) the following:

  • AI-enhanced mobile diagnostics (e.g., image-based pathogen identification);
  • Digital epidemiology tools (e.g., contact-tracing apps);
  • Point-of-care device innovations (e.g., paper-based assays, nanomaterial sensors).

Dr. Thomas Foster Scherr
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. Diagnostics is an international peer-reviewed open access semimonthly 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 2600 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

  • infectious diseases
  • mobile health
  • machine learning
  • epidemiology
  • disease surveillance
  • diagnostics

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Published Papers (1 paper)

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Research

18 pages, 2233 KB  
Article
Machine Learning Model for Predicting Multidrug Resistance in Clinical Klebsiella pneumoniae Isolates
by Yuksel Akkaya, Irfan Aydin, Handan Tanyildizi-Kokkulunk, Ayse Erturk and Ibrahim Halil Kilic
Diagnostics 2026, 16(4), 555; https://doi.org/10.3390/diagnostics16040555 - 13 Feb 2026
Viewed by 760
Abstract
Background/Objectives: Klebsiella pneumoniae is an opportunistic pathogen increasingly resistant to carbapenems and broad-spectrum antibiotics, complicating timely infection management. In critical cases like septic shock, where initiating effective antibiotics within 3 h improves survival, culture-based resistance testing is often too slow. This study [...] Read more.
Background/Objectives: Klebsiella pneumoniae is an opportunistic pathogen increasingly resistant to carbapenems and broad-spectrum antibiotics, complicating timely infection management. In critical cases like septic shock, where initiating effective antibiotics within 3 h improves survival, culture-based resistance testing is often too slow. This study evaluates machine learning (ML) algorithms for faster antimicrobial resistance prediction than conventional methods. Methods: In this retrospective study, antibiogram results of 607 Klebsiella pneumoniae isolates collected between 2017 and 2024 were combined with demographic and clinical information of the patients from whom the isolates were obtained. Four different ML algorithms, namely Decision Tree (DT), Support Vector Classifier (SVC), K-Nearest Neighbors (KNN) and Random Forest (RF), were applied to classify the resistance status for 22 antibiotics. Model performances were evaluated using accuracy, precision, recall, F-score, AUC and feature importance metrics. Results: The RF model showed the highest overall performance in accurately predicting resistance to 22 antibiotics, achieving an average AUC value of 0.96. In particular, it predicted resistance to treatment-critical antibiotics such as Ertapenem (100%), Imipenem (93%) and Meropenem (95%) with high accuracy. Conclusions: ML models, especially RF, offer a powerful tool for rapid antibiotic resistance prediction, supporting accurate empirical treatment decisions and antimicrobial stewardship. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Infectious Disease Diagnosis)
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