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Diagnostics
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AI and Digital Health for Disease Diagnosis and Monitoring, 2nd...
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AI and Digital Health for Disease Diagnosis and Monitoring, 2nd Edition

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: 31 August 2026 | Viewed by 549

Special Issue Editors

Dr. AKM Azad

E-Mail Website
Guest Editor
Department of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 13318, Saudi Arabia
Interests: bioinformatics; computational biology; systems biology; network biology; artificial intelligence; deep learning; drug repositioning
Special Issues, Collections and Topics in MDPI journals
Dr. Mohammad Ali Moni

E-Mail Website
Guest Editor
Artificial Intelligence and Cyber Futures Institute, Charles Sturt University, Bathurst, NSW, Australia
Interests: artificial intelligence; machine learning; deep learning; computer vision; digital health data science; health informatics; bioinformatics; medical image processing (MRI, fMRI, Ultrasound, X-ray); neuroimaging; EEG; ECG analysis; explainable AI and transparent decision-making systems
Special Issues, Collections and Topics in MDPI journals
Dr. Hussain Mohammed Dipu Kabir

E-Mail Website
Guest Editor
1. Artificial Intelligence and Cyber Futures Institute, Charles Sturt University, Orange, NSW 2800, Australia
2. Rural Health Research Institute, Charles Sturt University, Orange, NSW 2800, Australia
Interests: artificial intelligence; uncertainty quantification; imbalanced data
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to invite you to contribute to our Special Issue, "AI and Digital Health for Disease Diagnosis and Monitoring, 2nd Edition." Chronic diseases, such as cancer, cardiovascular disease, diabetes, and chronic respiratory conditions, pose significant long-term health challenges and economic burdens worldwide. The integration of Artificial Intelligence (AI) and digital health technologies into healthcare has shown immense potential in revolutionizing the diagnosis, monitoring, and prediction of these chronic conditions. By leveraging AI and digital tools, we can enhance early diagnosis, personalize treatment plans, and improve patient outcomes. This Special Issue aims to bring together cutting-edge research that explores the application of AI and digital health in disease diagnosis and monitoring, offering innovative solutions to these persistent health challenges.

This Special Issue aims to provide a comprehensive overview of the advancements and challenges in the application of AI and digital health for disease diagnosis, monitoring, and prediction. We seek to explore how these technologies can be effectively utilized to improve the accuracy, efficiency, and scalability of chronic disease management. Aligning with Diagnostics' focus on technological innovations and their impacts on health outcomes, this Special Issue will contribute to the ongoing discourse on the transformative potential of AI and digital health in healthcare. We encourage submissions that offer novel insights, propose new methodologies, and present real-world applications in clinical and diagnostic contexts.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • AI-based diagnostic tools for chronic diseases;
  • Machine learning and deep learning models for disease prediction and progression monitoring;
  • Real-time monitoring systems using digital health technologies;
  • AI-driven personalized treatment plans and decision support systems;
  • Integration of AI with wearable devices and mobile health technologies;
  • Big data analytics and digital biomarkers in disease diagnosis and management;
  • AI applications in public health for disease prevention and early detection.

We look forward to hearing from you.

Dr. AKM Azad
Dr. Mohammad Ali Moni
Dr. Hussain Mohammed Dipu Kabir
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

  • artificial intelligence
  • chronic disease
  • disease monitoring
  • disease prediction
  • machine learning
  • personalized treatment
  • health technology
  • big data analytics
  • public health
  • wearable health devices

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

Published Papers (2 papers)

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Research

26 pages, 1023 KB  
Article
Non-Glycemic Clinical Data for Type 2 Diabetes Detection in Mexican Adults: A Comparative Analysis of Atherogenic Indices, Statistical Transformations, and Machine Learning Algorithms
by Martin Hazael Guerrero-Flores, Valeria Maeda-Gutiérrez, Carlos E. Galván-Tejada, Jorge I. Galván-Tejada, Miguel Cruz, Luis Alberto Flores-Chaires, Karina Trejo-Vázquez, Rafael Magallanes-Quintanar and Javier Saldívar
Diagnostics 2026, 16(1), 53; https://doi.org/10.3390/diagnostics16010053 - 23 Dec 2025
Abstract
Background: Type 2 diabetes (T2D) is a growing public health problem in Mexico. Lipid profile alterations have been shown to appear years before changes in glycemic biomarkers, and some of the latter are limited in availability, especially in underserved settings. Therefore, anthropometric variables [...] Read more.
Background: Type 2 diabetes (T2D) is a growing public health problem in Mexico. Lipid profile alterations have been shown to appear years before changes in glycemic biomarkers, and some of the latter are limited in availability, especially in underserved settings. Therefore, anthropometric variables and lipids represent relevant early indicators for the early detection of the disease. This study evaluates the capacity of non-glycemic clinical data—including lipid profile and anthropometric indicators—to detect T2D using machine learning, and compares the performance of different feature engineering approaches. Methods: Using more than a thousand clinical records of Mexican adults, three experiments were developed: (1) a distribution and normality analysis to characterize the variability of lipid variables; (2) an evaluation of the predictive power of multiple atherogenic indices (Castelli I, Castelli II, TG/HDL, and AIP); and (3) the implementation of statistical transformations (logarithmic, quare-root, and Z-standardization) to stabilize variance and improve feature quality. Logistic regression, SVM-RBF, random forest, and XGBoost models were trained on each feature set and evaluated using accuracy, sensitivity, specificity, F1-score, and area under the ROC curve. Results: The AIP index showed the greatest discriminatory power among the atherogenic indices, while normality-based transformations improved the performance of distribution-sensitive models, such as SVM. In the final experiment, the SVM-RBF and XGBoost models achieved AUC values greater than 0.90, demonstrating the feasibility of a diagnostic approach based exclusively on non-glycemic data. Conclusions: The findings indicate that the transformed lipid profile and anthropometric variables can constitute a solid and accessible alternative for the early detection of T2D in clinical and public health contexts, offering a robust methodological framework for future predictive applications in the absence of traditional glycemic biomarkers. Full article
(This article belongs to the Special Issue AI and Digital Health for Disease Diagnosis and Monitoring, 2nd Edition)
26 pages, 5565 KB  
Article
Explainable Federated Learning for Multi-Class Heart Disease Diagnosis via ECG Fiducial Features
by Tanjila Alam Sathi, Rafsan Jany, AKM Azad, Salem A. Alyami, Naif Alotaibi, Iqram Hussain and Md Azam Hossain
Diagnostics 2025, 15(24), 3110; https://doi.org/10.3390/diagnostics15243110 - 7 Dec 2025
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Abstract
Background/Objectives: Cardiovascular disease (CVD) remains a leading cause of mortality and disability worldwide, with timely diagnosis critical for preventing long-term functional impairment. Electrocardiograms (ECGs) provide essential biomarkers of cardiac function, but their interpretation is often complex, particularly across multi-institutional datasets. Methods: This study [...] Read more.
Background/Objectives: Cardiovascular disease (CVD) remains a leading cause of mortality and disability worldwide, with timely diagnosis critical for preventing long-term functional impairment. Electrocardiograms (ECGs) provide essential biomarkers of cardiac function, but their interpretation is often complex, particularly across multi-institutional datasets. Methods: This study presents an explainable federated learning framework with long short-term memory (FL-LSTM) for multi-class heart disease classification, capable of distinguishing arrhythmia, ischemia, and healthy states while preserving patient privacy. Results: The model was trained and evaluated on three heterogeneous ECG datasets, achieving 92% accuracy, 99% AUC, and 91% F1 score, outperforming existing federated approaches. Model interpretability is provided via SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME), highlighting clinically relevant ECG biomarkers such as P-wave height, R-wave height, QRS complex, RR interval, and QT interval. Conclusions: By integrating temporal modeling, federated learning, and interpretable AI, the framework enables secure and collaborative cardiac diagnosis while supporting transparent clinical decision-making in distributed healthcare settings. Full article
(This article belongs to the Special Issue AI and Digital Health for Disease Diagnosis and Monitoring, 2nd Edition)
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