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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 3520

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 (6 papers)

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16 pages, 3543 KB  
Article
AI-Assisted Strabismus Diagnosis Using Eye-Tracking and Machine Learning
by Malrey Lee
Diagnostics 2026, 16(6), 910; https://doi.org/10.3390/diagnostics16060910 - 19 Mar 2026
Viewed by 62
Abstract
Background: Strabismus diagnosis via the Alternate Cover Test (ACT) lacks quantitative standardization. This study proposes an AI-assisted framework using eye-tracking and machine learning for objective screening. Methods: Gaze coordinates were captured using a 60 Hz infrared eye tracker during ACT. Of the 291 [...] Read more.
Background: Strabismus diagnosis via the Alternate Cover Test (ACT) lacks quantitative standardization. This study proposes an AI-assisted framework using eye-tracking and machine learning for objective screening. Methods: Gaze coordinates were captured using a 60 Hz infrared eye tracker during ACT. Of the 291 initially screened individuals considered, 50 participants were ultimately included after quality filtering, yielding 335 valid samples. Seven algorithms were evaluated, with the dataset split into 294 training and 41 testing samples. Performance was measured by accuracy, sensitivity, specificity, PPV, and NPV. Results: Random Forest showed the best performance, achieving 97.56% accuracy (40/41) on the test set. It demonstrated a sensitivity of 1.00, specificity of 0.95, PPV of 0.95, and NPV of 1.00. The confusion matrix confirmed minimal false negatives, ensuring reliable clinical screening. Conclusions: The proposed system provides a robust, objective tool for strabismus diagnosis, standardizing ACT interpretation and reducing clinical bias. Full article
(This article belongs to the Special Issue AI and Digital Health for Disease Diagnosis and Monitoring, 2nd Edition)
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23 pages, 606 KB  
Article
An Intelligent Hybrid Ensemble Model for Early Detection of Breast Cancer in Multidisciplinary Healthcare Systems
by Hasnain Iftikhar, Atef F. Hashem, Moiz Qureshi, Paulo Canas Rodrigues, S. O. Ali, Ronny Ivan Gonzales Medina and Javier Linkolk López-Gonzales
Diagnostics 2026, 16(3), 377; https://doi.org/10.3390/diagnostics16030377 - 23 Jan 2026
Viewed by 434
Abstract
Background/Objectives: In the modern healthcare landscape, breast cancer remains one of the most prevalent malignancies and a leading cause of mortality among women worldwide. Early and accurate prediction of breast cancer plays a pivotal role in effective diagnosis, treatment planning, and improving survival [...] Read more.
Background/Objectives: In the modern healthcare landscape, breast cancer remains one of the most prevalent malignancies and a leading cause of mortality among women worldwide. Early and accurate prediction of breast cancer plays a pivotal role in effective diagnosis, treatment planning, and improving survival outcomes. However, due to the complexity and heterogeneity of medical data, achieving high predictive accuracy remains a significant challenge. This study proposes an intelligent hybrid system that integrates traditional machine learning (ML), deep learning (DL), and ensemble learning approaches for enhanced breast cancer prediction using the Wisconsin Breast Cancer Dataset. Methods: The proposed system employs a multistage framework comprising three main phases: (1) data preprocessing and balancing, which involves normalization using the min–max technique and application of the Synthetic Minority Over-sampling Technique (SMOTE) to mitigate class imbalance; (2) model development, where multiple ML algorithms, DL architectures, and a novel ensemble model are applied to the preprocessed data; and (3) model evaluation and validation, performed under three distinct training–testing scenarios to ensure robustness and generalizability. Model performance was assessed using six statistical evaluation metrics—accuracy, precision, recall, F1-score, specificity, and AUC—alongside graphical analyses and rigorous statistical tests to evaluate predictive consistency. Results: The findings demonstrate that the proposed ensemble model significantly outperforms individual machine learning and deep learning models in terms of predictive accuracy, stability, and reliability. A comparative analysis also reveals that the ensemble system surpasses several state-of-the-art methods reported in the literature. Conclusions: The proposed intelligent hybrid system offers a promising, multidisciplinary approach for improving diagnostic decision support in breast cancer prediction. By integrating advanced data preprocessing, machine learning, and deep learning paradigms within a unified ensemble framework, this study contributes to the broader goals of precision oncology and AI-driven healthcare, aligning with global efforts to enhance early cancer detection and personalized medical care. Full article
(This article belongs to the Special Issue AI and Digital Health for Disease Diagnosis and Monitoring, 2nd Edition)
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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
Viewed by 420
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)
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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
Viewed by 880
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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43 pages, 6336 KB  
Systematic Review
A Systematic Literature Review of You Only Look Once Architectures (v1–v12) in Healthcare Systems
by Ozgur Koray Sahingoz, Gozde Karatas Baydogmus and Emin Kugu
Diagnostics 2026, 16(6), 935; https://doi.org/10.3390/diagnostics16060935 - 22 Mar 2026
Viewed by 194
Abstract
Background/Objectives: The integration of deep learning and computer vision into healthcare has improved medical diagnosis and image analysis. Among object detection algorithms, the YOLO family has attracted substantial attention due to its ability to analyze images in real time with reported improvements [...] Read more.
Background/Objectives: The integration of deep learning and computer vision into healthcare has improved medical diagnosis and image analysis. Among object detection algorithms, the YOLO family has attracted substantial attention due to its ability to analyze images in real time with reported improvements in detection performance across multiple studies. This systematic review examines the evolution of YOLO algorithms for diagnostic applications in healthcare from YOLOv1 to YOLOv12. Methods: Peer-reviewed scientific articles published up to 1 January 2026 were retrieved from major scientific databases in accordance with PRISMA 2020 guidelines. The included studies applied YOLO models to medical imaging tasks, including disease and lesion detection and support for clinical procedures. Performance was synthesized using reported metrics such as average precision, accuracy, inference time, and computational efficiency. Results: The reviewed literature suggests progressive architectural refinements associated with reported improvements in diagnostic performance. YOLOv5 and YOLOv8 are the most frequently used architectures in diagnostic settings, reflecting a favorable trade-off between accuracy and computational complexity. YOLO-based methods have demonstrated strong performance across radiological, pathological, ophthalmological, and endoscopic applications. Conclusions: YOLO models have matured into robust and optimized solutions for medical image analysis; however, challenges remain in interpretability, cross-institution generalization, and deployment on edge devices. Future work on explainable YOLO-based diagnostics and energy-efficient model design will be particularly valuable. Full article
(This article belongs to the Special Issue AI and Digital Health for Disease Diagnosis and Monitoring, 2nd Edition)
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27 pages, 1331 KB  
Study Protocol
Application of Telemedicine and Artificial Intelligence in Outpatient Cardiology Care: TeleAI-CVD Study (Design)
by Stefan Toth, Marianna Barbierik Vachalcova, Kamil Barbierik, Adriana Jarolimkova, Pavol Fulop, Mariana Dvoroznakova, Dominik Pella and Tibor Poruban
Diagnostics 2026, 16(1), 145; https://doi.org/10.3390/diagnostics16010145 - 1 Jan 2026
Viewed by 910
Abstract
Background/Objectives: Cardiovascular (CV) diseases remain the leading cause of morbidity and mortality across Europe. Despite substantial progress in prevention, diagnostics, and therapeutics, outpatient cardiology care continues to face systemic challenges, including limited consultation time, workforce constraints, and incomplete clinical information at the point [...] Read more.
Background/Objectives: Cardiovascular (CV) diseases remain the leading cause of morbidity and mortality across Europe. Despite substantial progress in prevention, diagnostics, and therapeutics, outpatient cardiology care continues to face systemic challenges, including limited consultation time, workforce constraints, and incomplete clinical information at the point of care. The primary objective of this study is threefold. First, to evaluate whether AI-enhanced telemedicine improves clinical control of hypertension, dyslipidemia, and heart failure compared to standard ambulatory care. Second, to assess the impact on physician workflow efficiency and documentation burden through AI-assisted clinical documentation. Third, to determine patient satisfaction and safety profiles of integrated telemedicine–AI systems. Clinical control will be measured by a composite endpoint of disease-specific targets assessed at the 12-month follow-up visit. Methods: The TeleAI-CVD Concept Study aims to evaluate the integration of telemedicine and artificial intelligence (AI) to enhance the efficiency, quality, and individualization of cardiovascular disease management in the ambulatory setting. Within this framework, AI-driven tools will be employed to collect structured clinical histories and current symptomatology from patients prior to outpatient visits using digital questionnaires and conversational interfaces. Results: Obtained data, combined with telemonitoring metrics, laboratory parameters, and existing clinical records, will be synthesized to support clinical decision-making. Conclusions: This approach is expected to streamline consultations, increase diagnostic accuracy, and enable personalized, data-driven care through continuous evaluation of patient trajectories. The anticipated outcomes of the TeleAI-CVD study include the development of optimized, AI-assisted management protocols for cardiology patients, a reduction in unnecessary in-person visits through effective telemedicine-based follow-up, and accelerated attainment of therapeutic targets. Ultimately, this concept seeks to redefine the paradigm of outpatient cardiovascular care by embedding advanced digital technologies within routine clinical workflows. Full article
(This article belongs to the Special Issue AI and Digital Health for Disease Diagnosis and Monitoring, 2nd Edition)
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