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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 November 2025 | Viewed by 650

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 100 words) can be sent to the Editorial Office for announcement on this website.

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 (2 papers)

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21 pages, 2657 KiB  
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
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 KiB  
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
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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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