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Diagnostics
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AI-Powered Solutions for Personalized Healthcare Monitoring with Wearables
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AI-Powered Solutions for Personalized Healthcare Monitoring with Wearables

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 6351

Special Issue Editors

Dr. Kuo-Chung Chu

E-Mail Website
Guest Editor
Department of Information Management, National Taipei University of Nursing and Health Sciences, Taipei 112, Taiwan
Interests: artificial intelligence; health care
Dr. Jakir Hossain Bhuiyan Masud

E-Mail Website
Guest Editor
Department of Biomedical Informatics and Data Science, The University of Alabama at Birmingham, Birmingham, AL, USA
Interests: health informatics; public health; digital health; artificial intelligence
Dr. Ming-Chun Huang

E-Mail Website
Guest Editor
Department of Data and Computational Science, Duke Kunshan University, Suzhou, China
Interests: health

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) and wearable technology have transformed healthcare by making sophisticated personal health monitoring systems possible. Healthcare technology has shifted from conventional approaches to wearables with AI capabilities, marking a significant advancement in personalized care. However, further research and development are necessary to overcome current limitations and fully realize the benefits of such integrated health monitoring systems. These applications gather all parameters, which are then transmitted to the AI model for assessment. The prediction of cardiac illness based on the information gathered by the wearable device and its use is the main focus of this type of system's technique. If heart illness is detected or blood oxygen levels fall below a healthy threshold, the device records the information for medical learning. All people should have access to primary care, and lower-income families and individuals should also be able to afford to use a more complete healthcare system.

Artificial intelligence helps medical practitioners to evaluate monitoring data, provide real-time alarms for possible issues, and enable prompt actions. Analytics driven by AI has enormous potential to transform healthcare decision-making and boost productivity. Better patient outcomes, lower costs, and increased operational efficiency across a range of healthcare business areas can result from the deployment of these technologies. By using sophisticated algorithms for data analysis and predictive modeling, artificial intelligence plays a critical role in interpreting this abundance of information. In addition to empowering individuals to take an active role in their own health management, this combination of wearable technology, big data, and AI also helps medical personnel to identify abnormalities, make well-informed judgments, and intervene promptly. To improve remote desktop patient care, encourage the early diagnosis of health conditions, and eventually improve overall outcomes for patients, this abstract examines the revolutionary effects of wearable technology, as well as the combination of big data and artificial intelligence. A ground-breaking advancement in pediatric cardiac diagnostics, AI holds great promise for improving diagnostic precision and for the early identification of pediatric cardiac disorders. AI-powered solutions can accurately evaluate intricate medical data, spot trends that human physicians would miss, and offer insightful information on heart health in children.

One of the biggest problems facing the healthcare sector is making suggestions for individualized and effective therapy. The rapid expansion of medical data and the development of artificial intelligence technologies present a great opportunity to create novel frameworks that use these data for individualized treatment regimens to improve patient outcomes. To produce individualized treatment suggestions, this Special Issue offers an AI-powered framework that combines a variety of data sources, sophisticated machine learning models, and explainable AI methodologies.

Dr. Kuo-Chung Chu
Dr. Jakir Hossain Bhuiyan Masud
Dr. Ming-Chun Huang
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
  • wearable technology
  • health monitoring
  • remote patient care
  • personalized healthcare

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Published Papers (5 papers)

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17 pages, 1641 KB  
Article
Large-Scale Validation of a Dual Cross-Attention Network for Automated Sleep Staging Using Wearable Photoplethysmography Signals
by Ruochen Li, Yutao He, Yanan Bie, Jiawei Guo, Lichao Wang, Yao Zhao, Jun Zhong and Wei Zhu
Diagnostics 2026, 16(5), 802; https://doi.org/10.3390/diagnostics16050802 - 8 Mar 2026
Viewed by 355
Abstract
Background: Sleep staging is vital for diagnosing sleep disorders, but the clinical gold standard, polysomnography, is too intrusive for routine home monitoring. While photoplethysmography (PPG) offers a wearable alternative, achieving high diagnostic accuracy remains challenging due to signal noise and individual variability. Methods: [...] Read more.
Background: Sleep staging is vital for diagnosing sleep disorders, but the clinical gold standard, polysomnography, is too intrusive for routine home monitoring. While photoplethysmography (PPG) offers a wearable alternative, achieving high diagnostic accuracy remains challenging due to signal noise and individual variability. Methods: We developed DCA-Sleep, a deep learning framework using a Dual Cross-Attention (DCA) mechanism to capture long-range temporal dependencies from raw single-channel PPG. To overcome data scarcity, a cross-modality transfer learning strategy was implemented, pre-training the model on six electrocardiogram (ECG) datasets before extensive validation on a combined cohort of 9738 subjects across nine public datasets (including MESA and CFS). Results: DCA-Sleep demonstrated superior robustness, achieving an average F1-score of 0.731 and a Cohen’s Kappa of 0.652 on the MESA dataset, significantly outperforming state-of-the-art baselines. The model showed high sensitivity in detecting Wake and Deep Sleep stages, which are critical for clinical assessment. Conclusions: This study provides a large-scale validation of a PPG-based staging tool, confirming its reliability as a non-invasive, scalable solution for long-term sleep monitoring and clinical screening. Full article
(This article belongs to the Special Issue AI-Powered Solutions for Personalized Healthcare Monitoring with Wearables)
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18 pages, 6756 KB  
Article
Neurosense: Bridging Neural Dynamics and Mental Health Through Deep Learning for Brain Health Assessment via Reaction Time and p-Factor Prediction
by Haipeng Wang, Shanruo Xu, Runkun Guo, Jiang Han and Ming-Chun Huang
Diagnostics 2026, 16(2), 293; https://doi.org/10.3390/diagnostics16020293 - 16 Jan 2026
Viewed by 545
Abstract
Background/Objectives: Cognitive decline and compromised attention control serve as early indicators of neurodysfunction that manifest as broader psychopathological symptoms, yet conventional mental health assessment relies predominantly on subjective self-report measures lacking objectivity and temporal granularity. We propose Neurosense, an AI-driven brain health [...] Read more.
Background/Objectives: Cognitive decline and compromised attention control serve as early indicators of neurodysfunction that manifest as broader psychopathological symptoms, yet conventional mental health assessment relies predominantly on subjective self-report measures lacking objectivity and temporal granularity. We propose Neurosense, an AI-driven brain health assessment framework using electroencephalography (EEG) to non-invasively capture neural dynamics. Methods: Our Dual-path Spatio-Temporal Adaptive Gated Encoder (D-STAGE) architecture processes temporal and spatial EEG features in parallel through Transformer-based and graph convolutional pathways, integrating them via adaptive gating mechanisms. We introduce a two-stage paradigm: first training on cognitive task EEG for reaction time prediction to acquire cognitive performance-related representations, then featuring parameter-efficient adapter-based transfer learning to estimate p-factor—a transdiagnostic psychopathology dimension. The adapter-based transfer achieves competitive performance using only 1.7% of parameters required for full fine-tuning. Results: The model achieves effective reaction time prediction from EEG signals. Transfer learning from cognitive tasks to mental health assessment demonstrates that cognitive efficiency representations can be adapted for p-factor prediction, outperforming direct training approaches while maintaining parameter efficiency. Conclusions: The Neurosense framework reveals hierarchical relationships between neural dynamics, cognitive efficiency, and mental health dimensions, establishing foundations for a promising computational framework for mental health assessment applications. Full article
(This article belongs to the Special Issue AI-Powered Solutions for Personalized Healthcare Monitoring with Wearables)
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22 pages, 2159 KB  
Article
Association of Mobile-Enhanced Remote Patient Monitoring with Blood Pressure Control in Hypertensive Patients with Comorbidities: A Multicenter Pre–Post Evaluation
by Ashfaq Ullah, Irfan Ahmad and Wei Deng
Diagnostics 2026, 16(2), 244; https://doi.org/10.3390/diagnostics16020244 - 12 Jan 2026
Viewed by 676
Abstract
Background and Objectives: Hypertension affects more than 27% of adults in China, and despite ongoing public health efforts, substantial gaps remain in awareness, treatment, and blood pressure control, particularly among older adults and patients with multiple comorbidities. Conventional clinic-based care often provides limited [...] Read more.
Background and Objectives: Hypertension affects more than 27% of adults in China, and despite ongoing public health efforts, substantial gaps remain in awareness, treatment, and blood pressure control, particularly among older adults and patients with multiple comorbidities. Conventional clinic-based care often provides limited opportunity for frequent monitoring and timely treatment adjustment, which may contribute to persistent poor control in routine practice. The objective of this study was to evaluate changes in blood pressure control and related clinical indicators during implementation of a mobile-enhanced remote patient monitoring (RPM)–supported care model among hypertensive patients with comorbidities, including patterns of medication adjustment, adherence, and selected cardiometabolic parameters. Methods: We conducted a multicenter, pre–post evaluation of a mobile-enhanced remote patient monitoring (RPM) program among 6874 adults with hypertension managed at six hospitals in Chongqing, China. Participants received usual care during the pre-RPM phase (April–September 2024; clinic blood pressure measured using an Omron HEM-7136 device), followed by an RPM-supported phase (October 2024–March 2025; home blood pressure measured twice daily using connected A666G monitors with automated transmission via WeChat, medication reminders, and clinician follow-up). Given the use of different devices and measurement settings, blood pressure comparisons may be influenced by device- and setting-related measurement differences. Monthly blood pressure averages were calculated from all available readings. Subgroup analyses explored patterns by sex, age, baseline BP category, and comorbidity status. Results: The cohort was 48.9% male with a mean age of 66.9 ± 13.7 years. During the RPM-supported care period, the proportion meeting the study’s blood pressure control threshold increased from 62.4% (pre-RPM) to 90.1%. Mean systolic blood pressure decreased from 140 mmHg at baseline to 116–118 mmHg at 6 months during the more frequent monitoring and active treatment adjustment period supported by RPM (p < 0.001), alongside modest reductions in fasting blood glucose and total cholesterol. These achieved SBP levels are below commonly recommended office targets for many older adults (typically <140 mmHg for ages 65–79, with individualized lower targets only if well tolerated; and less stringent targets for adults ≥80 years) and therefore warrant cautious interpretation and safety contextualization. Medication adherence improved, and antihypertensive regimen intensity increased during follow-up, suggesting that more frequent monitoring and active treatment adjustment contributed to the early blood pressure decline. Subgroup patterns were broadly similar across age and baseline BP categories; observed differences by sex and comorbidity groups were exploratory. Conclusions: In this large multicenter pre–post study, implementation of an RPM-supported hypertension care model was associated with substantial improvements in blood pressure control and concurrent intensification of guideline-concordant therapy. Given the absence of a concurrent control group, clinic-to-home measurement differences, and concurrent medication changes, findings should be interpreted as associations observed during an intensified monitoring and treatment period rather than definitive causal effects of RPM technology alone. Pragmatic randomized evaluations with standardized measurement protocols, longer follow-up, and cost-effectiveness analyses are warranted. Full article
(This article belongs to the Special Issue AI-Powered Solutions for Personalized Healthcare Monitoring with Wearables)
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21 pages, 2657 KB  
Article
AI-Powered Adaptive Disability Prediction and Healthcare Analytics Using Smart Technologies
by Malak Alamri, Mamoona Humayun, Khalid Haseeb, Naveed Abbas and Naeem Ramzan
Diagnostics 2025, 15(16), 2104; https://doi.org/10.3390/diagnostics15162104 - 21 Aug 2025
Cited by 2 | Viewed by 1511
Abstract
Background: By leveraging advanced wireless technologies, Healthcare Industry 5.0 promotes the continuous monitoring of real-time medical acquisition from the physical environment. These systems help identify early diseases by collecting health records from patients’ bodies promptly using biosensors. The dynamic nature of medical [...] Read more.
Background: By leveraging advanced wireless technologies, Healthcare Industry 5.0 promotes the continuous monitoring of real-time medical acquisition from the physical environment. These systems help identify early diseases by collecting health records from patients’ bodies promptly using biosensors. The dynamic nature of medical devices not only enhances the data analysis in medical services and the prediction of chronic diseases, but also improves remote diagnostics with the latency-aware healthcare system. However, due to scalability and reliability limitations in data processing, most existing healthcare systems pose research challenges in the timely detection of personalized diseases, leading to inconsistent diagnoses, particularly when continuous monitoring is crucial. Methods: This work propose an adaptive and secure framework for disability identification using the Internet of Medical Things (IoMT), integrating edge computing and artificial intelligence. To achieve the shortest response time for medical decisions, the proposed framework explores lightweight edge computing processes that collect physiological and behavioral data using biosensors. Furthermore, it offers a trusted mechanism using decentralized strategies to protect big data analytics from malicious activities and increase authentic access to sensitive medical data. Lastly, it provides personalized healthcare interventions while monitoring healthcare applications using realistic health records, thereby enhancing the system’s ability to identify diseases associated with chronic conditions. Results: The proposed framework is tested using simulations, and the results indicate the high accuracy of the healthcare system in detecting disabilities at the edges, while enhancing the prompt response of the cloud server and guaranteeing the security of medical data through lightweight encryption methods and federated learning techniques. Conclusions: The proposed framework offers a secure and efficient solution for identifying disabilities in healthcare systems by leveraging IoMT, edge computing, and AI. It addresses critical challenges in real-time disease monitoring, enhancing diagnostic accuracy and ensuring the protection of sensitive medical data. Full article
(This article belongs to the Special Issue AI-Powered Solutions for Personalized Healthcare Monitoring with Wearables)
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18 pages, 879 KB  
Systematic Review
Machine Learning in Myasthenia Gravis: A Systematic Review of Prognostic Models and AI-Assisted Clinical Assessments
by Chen-Chih Chung, I-Chieh Wu, Oluwaseun Adebayo Bamodu, Chien-Tai Hong and Hou-Chang Chiu
Diagnostics 2025, 15(16), 2044; https://doi.org/10.3390/diagnostics15162044 - 14 Aug 2025
Cited by 2 | Viewed by 2466
Abstract
Background: Myasthenia gravis (MG), a chronic autoimmune disorder with variable disease trajectories, presents considerable challenges for clinical stratification and acute care management. This systematic review evaluated machine learning models developed for prognostic assessment in patients with MG. Methods: Following PRISMA guidelines, [...] Read more.
Background: Myasthenia gravis (MG), a chronic autoimmune disorder with variable disease trajectories, presents considerable challenges for clinical stratification and acute care management. This systematic review evaluated machine learning models developed for prognostic assessment in patients with MG. Methods: Following PRISMA guidelines, we systematically searched PubMed, Embase, and Scopus for relevant articles published from January 2010 to May 2025. Studies using machine learning techniques to predict MG-related outcomes based on structured or semi-structured clinical variables were included. We extracted data on model targets, algorithmic strategies, input features, validation design, performance metrics, and interpretability methods. The risk of bias was assessed using the Prediction Model Risk of Bias Assessment Tool. Results: Eleven studies were included, targeting ICU admission (n = 2), myasthenic crisis (n = 1), treatment response (n = 2), prolonged mechanical ventilation (n = 1), hospitalization duration (n = 1), symptom subtype clustering (n = 1), and artificial intelligence (AI)-assisted examination scoring (n = 3). Commonly used algorithms included extreme gradient boosting, random forests, decision trees, multivariate adaptive regression splines, and logistic regression. Reported AUC values ranged from 0.765 to 0.944. Only two studies employed external validation using independent cohorts; others relied on internal cross-validation or repeated holdout. Of the seven prognostic modeling studies, four were rated as having high risk of bias, primarily due to participant selection, predictor handling, and analytical design issues. The remaining four studies focused on unsupervised symptom clustering or AI-assisted examination scoring without predictive modeling components. Conclusions: Despite promising performance metrics, constraints in generalizability, validation rigor, and measurement consistency limited their clinical application. Future research should prioritize prospective multicenter studies, dynamic data sharing strategies, standardized outcome definitions, and real-time clinical workflow integration to advance machine learning-based prognostic tools for MG and support improved patient care in acute settings. Full article
(This article belongs to the Special Issue AI-Powered Solutions for Personalized Healthcare Monitoring with Wearables)
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