Artificial Intelligence in Biomedical Diagnostics and Analysis 2025

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: 30 April 2026 | Viewed by 622

Special Issue Editors


E-Mail Website
Guest Editor
Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia
Interests: machine learning; artificial intelligence; deep learning; neural networks; data science; information and communication management
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Digital Forensics Engineering, Technology Faculty, Firat University, 23119 Elazig, Turkey
Interests: image processing; signal processing; data hiding; feature engineering; visual secret sharing
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey
Interests: feature engineering; machine learning; biomedical image and signal processing; pattern recognition; computer forensics; mobile forensics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) has allowed us to propose algorithms/methods that make many tasks easier, and smart assistants have been proposed using artificial intelligence. These smart assistants, now being used in daily life, are of great importance for shortening processes. As a result, the quality of services has increased, especially with smart systems used in the healthcare field.

In this Special Issue, we plan to publish articles on next-generation machine learning methods. In particular, machine learning models are expected to be proposed using signals such as electroencephalograms (EEG), electromyograms (EMG), electrocardiograms (ECG), heart rate (HR) signals, computed tomography (CT), magnetic resonance (MR), X-rays and other medical images or videos. Additionally, explainable artificial intelligence (XAI) explains how machine learning methods perform classification. We hope to publish smart health applications that incorporate XAI-based next-generation methods.

Artificial intelligence applications in healthcare are an important research area, and these models/architectures/networks have also been used in precision medicine applications. With the Internet of Medical Things (IoMT), precision medical data can be accessed instantly, and information can be obtained from these data via machine learning methods.

Proteins and genomes are crucial to bioinformatics. Using genomic data, disorders and their associations can be identified. Therefore, we are interested in AI-based biomedical informatics methods, since biomedical informatics is crucial to understand the causes of disorders.

We have proposed a new Special Issue to contribute to the study area of healthcare with artificial intelligence and to publish high-quality research articles. We look forward to receiving your submissions on feature engineering, deep learning, and XAI-based models, as well as their uncertainty and implementation.

Dr. Prabal Datta Barua
Dr. Turker Tuncer
Dr. Sengul Dogan
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 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

  • medical diagnosis
  • artificial intelligence
  • deep learning
  • medical signal and image processing
  • internet of medical things (IoMT)
  • bioinformatics
  • explainable artificial intelligence
  • machine learning
  • uncertainty

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

14 pages, 1003 KB  
Article
Machine Learning Model with Fourier-Transform Infrared Spectroscopy (FTIR) as a Proof-of-Concept Tool for Predicting Group A Streptococcus (GAS) emm-Type in the Pediatric Population
by Valeria Fox, Gianluca Vrenna, Martina Rossitto, Serena Raimondi, Marco Cristiano, Venere Cortazzo, Marilena Agosta, Barbara Lucignano, Manuela Onori, Vanessa Tuccio Guarna Assanti, Maria Stefania Lepanto, Nour Essa, Isabella Tarissi De Jacobis, Andrea Campana, Massimiliano Raponi, Alberto Villani, Carlo Federico Perno and Paola Bernaschi
Diagnostics 2025, 15(23), 3041; https://doi.org/10.3390/diagnostics15233041 - 28 Nov 2025
Viewed by 288
Abstract
Background: Since 2022, invasive Group A Streptococcus (GAS) infections have increased, mainly due to the spread of specific emm-types, such as emm1. As therapy may depend on the emm-type, rapid and cost-effective identification is crucial. Fourier-transform infrared spectroscopy (FTIR) [...] Read more.
Background: Since 2022, invasive Group A Streptococcus (GAS) infections have increased, mainly due to the spread of specific emm-types, such as emm1. As therapy may depend on the emm-type, rapid and cost-effective identification is crucial. Fourier-transform infrared spectroscopy (FTIR) has emerged as a promising alternative to sequencing for GAS typing. We applied machine learning (ML) to FTIR spectra to build a predictive model for emm-type identification. Methods: Twenty-four GAS strains were analyzed by whole-genome sequencing and FTIR. The model was trained on twenty-one strains (emm-types: 1, 3, 4, and 6), using leave-one-out cross validation (LOOCV). To test the model’s ability to avoid false positive results, the model was also tested with three strains belonging to emm-types not included in the training of the model (emm-types: 12, 89, and 75). Results: An artificial neural network trained for 400 cycles achieved the highest accuracy (90.7%) out of the thirteen different models tested. When the three strains belonging to emm-types not included in the model were predicted with this model, it produced low score values, confirming its ability to avoid false positive results. Conclusions: We developed a preliminary and proof-of-concept model capable of accurately predicting the four most-prevalent emm-types in our setting, including the highly virulent emm1. These findings support FTIR combined with ML as a rapid, low-cost tool for GAS typing, with potential for real-time clinical applications to guide timely treatment decisions. However, as a proof-of-concept study, the relatively small sample size and limited emm-type diversity underline the need for further validation with larger and more diverse datasets. Full article
(This article belongs to the Special Issue Artificial Intelligence in Biomedical Diagnostics and Analysis 2025)
Show Figures

Figure 1

Back to TopTop