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

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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 10663

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Dr. AKM Azad

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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

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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
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Dr. Hussain Mohammed Dipu Kabir

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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-anonymized 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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Published Papers (9 papers)

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25 pages, 15843 KB

Open AccessArticle

Accurate Segmentation of Overlapping Cervical Cells Using an Optimized Deep Learning Framework for Cytology Screening

by Amal A. Alzu’bi, Mohammad Khatatbeh, Wan Azani Mustafa, Norhayati Mohd Zain, Hiam Alquran, Alia Al-Mohtaseb, Khaled Z. Alawneh, Mohammad Fawaeer, Bara’a Fawaeer, Shatha Salameh and Ahmad Alhussain

Diagnostics 2026, 16(14), 2240; https://doi.org/10.3390/diagnostics16142240 - 17 Jul 2026

Viewed by 306

Abstract

Background: Cervical cancer remains one of the leading causes of cancer-related morbidity among women worldwide. The Papanicolaou (Pap) smear is widely used for early detection; however, its manual interpretation is time-consuming, requires substantial expertise, and is often affected by inter-observer variability, particularly in cases with dense and overlapping cells. Methods: This study developed an optimized deep learning framework for cervical cell instance segmentation, specifically targeting the separation of overlapping cells in Pap smear images. The proposed framework was based on Mask R-CNN with a ResNet-50 backbone and Feature Pyramid Network. A public development dataset of 460 cervical smear images was used for model development and internal evaluation, while an independent 210-image dataset collected from King Abdullah University Hospital was reserved as an external held-out clinical assessment set. To improve small-cell detection and mask refinement, the Region Proposal Network was adapted using biologically informed anchor scales (16, 32, 64, 128, 256), followed by soft calibration-based post-processing. Results: The proposed framework achieved an AP50 of 89.90% and a Mask IoU of 90.44%. Small-cell performance, measured using APs under the COCO small-object convention, reached 22.80%, while APm and APl reached 68.45% and 81.42%, respectively. Clinical cell-count evaluation on the independent KAUH held-out set was performed using the mean count of five physicians as the reference standard and achieved an overall clinical detection accuracy of 93.00%. Conclusions: The optimized Mask R-CNN framework improved the detection and separation of overlapping cervical cells in Pap smear images and may serve as a supportive tool for cytopathology workflows. The results suggest that biologically informed anchor optimization and soft calibration can improve cell-level instance segmentation, particularly for small and overlapping cells. Further validation on larger multi-center datasets remains necessary before routine clinical deployment. Full article

(This article belongs to the Special Issue AI and Digital Health for Disease Diagnosis and Monitoring, 2nd Edition)

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32 pages, 25206 KB

Open AccessArticle

TransNet–SAM2: A Transformer–Foundation Model Framework for Prompt-Free Segmentation of White Blood Cells in Microscopic Blood Smear Images

by Julius Bamwenda, Mehmet Siraç Özerdem, Orhan Ayyildiz, Veysi Akpolat and İrem Akpolat

Diagnostics 2026, 16(11), 1737; https://doi.org/10.3390/diagnostics16111737 - 4 Jun 2026

Viewed by 519

Abstract

Background: Accurate segmentation of white blood cells (WBCs) in peripheral blood smear images is a fundamental step in computational hematology, enabling downstream tasks such as classification, morphological assessment, and quantitative analysis. However, reliable segmentation remains challenging due to staining variability, complex cellular morphology, overlapping structures, and limited availability of high-quality annotations. Aim and Methods: The aim of this study is to develop a robust and fully automated segmentation framework for white blood cells (WBCs) in microscopic blood smear images, providing a reliable foundation for subsequent computational analysis. We propose TransNet–SAM2, a hybrid deep learning architecture that integrates hierarchical transformer-based feature extraction with a foundation-model-based decoder for prompt-free segmentation. Specifically, a Swin Transformer backbone is employed to capture multi-scale contextual representations, which are subsequently aligned and fused through a feature adaptation module. The fused features are directly injected into the SAM2 mask decoder, replacing conventional prompt-based conditioning and enabling fully automatic segmentation. In addition, a weakly supervised self-training strategy is incorporated to utilize partially annotated data, improving model generalization while reducing annotation requirements. The proposed framework is evaluated using a clinically curated dataset from Dicle University, the publicly available Raabin-WBC dataset, and an additional external leukemic blast validation dataset (ALL-IDB) to assess robustness under both routine and atypical hematological conditions. Results: TransNet-SAM2 achieved a Dice coefficient of 0.95 ± 0.01 and IoU of 0.90 on internal testing, significantly outperforming U-Net (0.89), Mask R-CNN (0.90), and SAM2 (0.92) (p < 0.05). In cross-dataset evaluation (Dicle training, Raabin-WBC testing), the framework maintained strong performance (Dice: 0.91, IoU: 0.84), demonstrating robustness to domain shifts. Ablation studies confirmed each component’s contribution, with the full model improving Dice by 6% over a CNN baseline. Qualitative analysis showed accurate boundary delineation even with cell overlap and background clutter. Conclusions: These findings indicate that the proposed framework provides a promising and scalable framework for WBC segmentation. While the current study focuses on segmentation, future work will investigate integration with classification and radiomics pipelines, as well as validation on more diverse clinical datasets, including bone marrow and leukemia samples. Full article

(This article belongs to the Special Issue AI and Digital Health for Disease Diagnosis and Monitoring, 2nd Edition)

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17 pages, 682 KB

Open AccessArticle

Implementation of an AI-Based Clinical Decision Support System Predicting In-Hospital Cardiac Arrest in General Wards: A Multicenter Staggered-Implementation Study in Secondary Hospitals in Korea

by Minjeong Kim, Dongjoon Yoo, Eunbi Noh, Yongwook Jeong, Minsoo Kim, Kyung-Jae Cho, Mincheol Kim, You Dong Sohn and Gyu Chong Cho

Diagnostics 2026, 16(11), 1682; https://doi.org/10.3390/diagnostics16111682 - 29 May 2026

Viewed by 664

Abstract

Background/Objectives: In-hospital cardiac arrest (IHCA) remains a devastating event associated with high morbidity and mortality among general ward patients. While Rapid Response Systems (RRS) can help identify deteriorating patients, maintaining these systems in secondary hospitals is frequently hindered by severe fiscal and personnel constraints. Consequently, evidence regarding the real-world clinical effectiveness of artificial intelligence software as a medical device (AI-SaMD) for predicting deterioration in such resource-constrained settings remains limited. Methods: We conducted a retrospective analysis on a multicenter, staggered-implementation study evaluating 164,761 eligible adult general ward admissions across three secondary hospitals in South Korea. The intervention involved deploying an AI-SaMD (DeepCARS), which utilizes four routine vital signs to predict ward IHCA within 24 h. The primary outcome was ward IHCA. Secondary outcomes included in-hospital mortality and length of stay (LOS). Exploratory analyses investigated the mechanisms of clinical outcomes by evaluating lead-times to interventions, outcomes in sepsis subgroups, changes in care directives, and post-arrest neurological outcomes. Results: AI-SaMD implementation was associated with a 21% reduction in ward IHCA incidence (adjusted rate ratio 0.79; 95% CI, 0.65–0.96; p = 0.016) and a 15% reduction in in-hospital mortality (aRR 0.85; 95% CI, 0.79–0.90; p < 0.001), alongside significantly shorter hospital and intensive care unit LOS. These associations were also observed in patients with sepsis (IHCA aRR 0.71; 95% CI, 0.54–0.93; p = 0.013). Lead-times to critical care intervention and to antibiotic escalation were numerically shorter in the AI-SaMD group by 16.3 h (p = 0.066) and 2.6 h (p = 0.523); poor neurological outcome at discharge among ward IHCA cases was 85/108 (78.7%) in the AI-SaMD group versus 63/102 (61.8%) in the standard-care group (aRR 1.13; 95% CI, 0.99–1.33; p = 0.058); and the full-code death rate did not differ between groups (aRR 0.94; 95% CI, 0.76–1.15)—none of these additional analyses reached statistical significance. Conclusions: In secondary hospitals unable to operate an RRS due to fiscal limitations, implementation of an AI-SaMD as an additional informational layer was associated with lower ward IHCA and in-hospital mortality. The AI-SaMD may serve as an actionable and scalable additional safety layer for general-ward patients in resource-constrained environments where RRS infrastructure is not feasible. Although this was a multicenter, large-scale study, the present analysis was retrospective and quasi-experimental in design; rigorous randomized studies are needed to confirm these associations. Full article

(This article belongs to the Special Issue AI and Digital Health for Disease Diagnosis and Monitoring, 2nd Edition)

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16 pages, 3543 KB

Open AccessArticle

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 988

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 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

Open AccessArticle

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

Cited by 5 | Viewed by 1039

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 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

Open AccessArticle

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 918

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 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

Open AccessEditor’s ChoiceArticle

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

Cited by 1 | Viewed by 2150

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 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

Open AccessSystematic 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

Cited by 6 | Viewed by 1415

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 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

Open AccessStudy 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

Cited by 1 | Viewed by 1777

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 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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