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Smart and Digital Health
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Smart and Digital Health

A special issue of Healthcare (ISSN 2227-9032). This special issue belongs to the section "Artificial Intelligence in Medicine".

Deadline for manuscript submissions: 30 November 2025 | Viewed by 17928

Special Issue Editors

Dr. George E. Dafoulas

E-Mail Website
Guest Editor
Faculty of Medicine, University of Thessaly, Larisa, Greece
Interests: systematic reviews; meta-analysis; evidence-based medicine; statistical analysis; data analysis; clinical medicine; health outcomes; healthcare management; health management; research project management; diabetes mellitus; digital health; real-world data; AI; health IoT
Special Issues, Collections and Topics in MDPI journals
Dr. Ilias Kalamaras

E-Mail Website
Guest Editor
Centre for Research and Technology Hellas, Information Technology Institute, Thermi, 57001 Thessaloniki, Greece
Interests: data visualization; big data analytics; data mining; machine learning; AI; pattern clustering; decision making; decision support systems

Special Issue Information

Dear Colleagues, 

The advances of the 4th Industrial Revolution based on real-world data analytics, the Internet of Medical Things (IoMT), machine learning (ML) and artificial intelligence (AI), applied in the healthcare sector, have the potential to revolutionize the provision of healthcare. The digital transformation of health services (Health 4.0) will be based on digital diagnostics and therapeutics (DTx), smart wearables, clinical decision support systems (CDSS), and computer-aided diagnostics (CAD). On the other hand, for the challenges of clinical evidence and cost effectiveness, user acceptance and satisfaction, privacy and ethical considerations, and new organization models of smart, digital health services need to be addressed. This Special Issue welcomes contributions of studies focused on these advantages and challenges of Smart, digital health on various technological platforms and covering all aspects of care. We are interested in studies and reviews focused on the validation or evaluation of smart, digital health services, rather than reports of technological breakthroughs.

Dr. George E. Dafoulas
Dr. Ilias Kalamaras
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. Healthcare 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 2700 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

  • real-world data analytics
  • machine learning
  • artificial intelligence
  • wearables
  • clinical decision support systems (CDSS)
  • computer-aided diagnostics (CAD)
  • digital diagnostics and therapeutics (DTx)

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Published Papers (6 papers)

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Research

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18 pages, 2688 KiB  
Article
Deep Learning and IoT-Based Ankle–Foot Orthosis for Enhanced Gait Optimization
by Ferdous Rahman Shefa, Fahim Hossain Sifat, Jia Uddin, Zahoor Ahmad, Jong-Myon Kim and Muhammad Golam Kibria
Healthcare 2024, 12(22), 2273; https://doi.org/10.3390/healthcare12222273 - 14 Nov 2024
Cited by 1 | Viewed by 1894
Abstract
Background/Objectives: This paper proposes a method for managing gait imbalances by integrating the Internet of Things (IoT) and machine learning technologies. Ankle–foot orthosis (AFO) devices are crucial medical braces that align the lower leg, ankle, and foot, offering essential support for individuals with [...] Read more.
Background/Objectives: This paper proposes a method for managing gait imbalances by integrating the Internet of Things (IoT) and machine learning technologies. Ankle–foot orthosis (AFO) devices are crucial medical braces that align the lower leg, ankle, and foot, offering essential support for individuals with gait imbalances by assisting weak or paralyzed muscles. This research aims to revolutionize medical orthotics through IoT and machine learning, providing a sophisticated solution for managing gait issues and enhancing patient care with personalized, data-driven insights. Methods: The smart ankle–foot orthosis (AFO) is equipped with a surface electromyography (sEMG) sensor to measure muscle activity and an Inertial Measurement Unit (IMU) sensor to monitor gait movements. Data from these sensors are transmitted to the cloud via fog computing for analysis, aiming to identify distinct walking phases, whether normal or aberrant. This involves preprocessing the data and analyzing it using various machine learning methods, such as Random Forest, Decision Tree, Support Vector Machine (SVM), Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), and Transformer models. Results: The Transformer model demonstrates exceptional performance in classifying walking phases based on sensor data, achieving an accuracy of 98.97%. With this preprocessed data, the model can accurately predict and measure improvements in patients’ walking patterns, highlighting its effectiveness in distinguishing between normal and aberrant phases during gait analysis. Conclusions: These predictive capabilities enable tailored recommendations regarding the duration and intensity of ankle–foot orthosis (AFO) usage based on individual recovery needs. The analysis results are sent to the physician’s device for validation and regular monitoring. Upon approval, the comprehensive report is made accessible to the patient, ensuring continuous progress tracking and timely adjustments to the treatment plan. Full article
(This article belongs to the Special Issue Smart and Digital Health)
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14 pages, 634 KiB  
Article
Development of an AI-Based Predictive Algorithm for Early Diagnosis of High-Risk Dementia Groups among the Elderly: Utilizing Health Lifelog Data
by Ji-Yong Lee and So Yoon Lee
Healthcare 2024, 12(18), 1872; https://doi.org/10.3390/healthcare12181872 - 18 Sep 2024
Cited by 1 | Viewed by 1614
Abstract
Background/Objectives: This study aimed to develop a predictive algorithm for the early diagnosis of dementia in the high-risk group of older adults using artificial intelligence technologies. The objective is to create an accessible diagnostic method that does not rely on traditional medical equipment, [...] Read more.
Background/Objectives: This study aimed to develop a predictive algorithm for the early diagnosis of dementia in the high-risk group of older adults using artificial intelligence technologies. The objective is to create an accessible diagnostic method that does not rely on traditional medical equipment, thereby improving the early detection and management of dementia. Methods: Lifelog data from wearable devices targeting this high-risk group were collected from the AI Hub platform. Various indicators from these data were analyzed to develop a dementia diagnostic model. Machine learning techniques such as Logistic Regression, Random Forest, LightGBM, and Support Vector Machine were employed. Data augmentation techniques were applied to address data imbalance, thereby enhancing the model performance. Results: Data augmentation significantly improved the model’s accuracy in classifying dementia cases. Specifically, in gait data, the SVM model performed with an accuracy of 0.879. In sleep data, a Logistic Regression was performed, yielding an accuracy of 0.818. This indicates that the lifelog data can effectively contribute to the early diagnosis of dementia, providing a practical solution that can be easily integrated into healthcare systems. Conclusions: This study demonstrates that lifelog data, which are easily collected in daily life, can significantly enhance the accessibility and efficiency of dementia diagnosis, aiding in the effective use of medical resources and potentially delaying disease progression. Full article
(This article belongs to the Special Issue Smart and Digital Health)
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32 pages, 4322 KiB  
Article
Beyond the Bedside: Machine Learning-Guided Length of Stay (LOS) Prediction for Cardiac Patients in Tertiary Care
by Sarab AlMuhaideb, Alanoud bin Shawyah, Mohammed F. Alhamid, Arwa Alabbad, Maram Alabbad, Hani Alsergani and Osama Alswailem
Healthcare 2024, 12(11), 1110; https://doi.org/10.3390/healthcare12111110 - 29 May 2024
Cited by 1 | Viewed by 1601
Abstract
Efficient management of hospital resources is essential for providing high-quality healthcare while ensuring sustainability. Length of stay (LOS), measuring the duration from admission to discharge, directly impacts patient outcomes and resource utilization. Accurate LOS prediction offers numerous benefits, including reducing re-admissions, ensuring appropriate [...] Read more.
Efficient management of hospital resources is essential for providing high-quality healthcare while ensuring sustainability. Length of stay (LOS), measuring the duration from admission to discharge, directly impacts patient outcomes and resource utilization. Accurate LOS prediction offers numerous benefits, including reducing re-admissions, ensuring appropriate staffing, and facilitating informed discharge planning. While conventional methods rely on statistical models and clinical expertise, recent advances in machine learning (ML) present promising avenues for enhancing LOS prediction. This research focuses on developing an ML-based LOS prediction model trained on a comprehensive real-world dataset and discussing the important factors towards practical deployment of trained ML models in clinical settings. This research involves the development of a comprehensive adult cardiac patient dataset (SaudiCardioStay (SCS)) from the King Faisal Specialist Hospital & Research Centre (KFSH&RC) hospital in Saudi Arabia, comprising 4930 patient encounters for 3611 unique patients collected from 2019 to 2022 (excluding 2020). A diverse range of classical ML models (i.e., Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LGBM), artificial neural networks (ANNs), Average Voting Regression (AvgVotReg)) are implemented for the SCS dataset to explore the potential of existing ML models in LOS prediction. In addition, this study introduces a novel approach for LOS prediction by incorporating a dedicated LOS classifier within a sophisticated ensemble methodology (i.e., Two-Level Sequential Cascade Generalization (2LSCG), Three-Level Sequential Cascade Generalization (3LSCG), Parallel Cascade Generalization (PCG)), aiming to enhance prediction accuracy and capture nuanced patterns in healthcare data. The experimental results indicate the best mean absolute error (MAE) of 0.1700 for the 3LSCG model. Relatively comparable performance was observed for the AvgVotReg model, with a MAE of 0.1703. In the end, a detailed analysis of the practical implications, limitations, and recommendations concerning the deployment of ML approaches in actual clinical settings is presented. Full article
(This article belongs to the Special Issue Smart and Digital Health)
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18 pages, 906 KiB  
Article
A Sustainable Model for Healthcare Systems: The Innovative Approach of ESG and Digital Transformation
by Anastasios Sepetis, Fotios Rizos, George Pierrakos, Haralampos Karanikas and Daniel Schallmo
Healthcare 2024, 12(2), 156; https://doi.org/10.3390/healthcare12020156 - 9 Jan 2024
Cited by 11 | Viewed by 6185
Abstract
In recent years, the globe has faced a series of topics of growing concern, such as the COVID-19 pandemic, the international financial crisis, rising socio-economic inequalities, the negative outcomes of greenhouse gas emissions, which resulted in climate change, and many others. Organizations worldwide [...] Read more.
In recent years, the globe has faced a series of topics of growing concern, such as the COVID-19 pandemic, the international financial crisis, rising socio-economic inequalities, the negative outcomes of greenhouse gas emissions, which resulted in climate change, and many others. Organizations worldwide have confronted these new challenges of sustainable finance by incorporating environmental, social, and corporate governance (ESG) factors and digital transformation (DT) in their innovation business strategies. The healthcare sector represents a large share of the global economy (about 10% of global economic output), employs a large number of workers, and needs to rely more on an open innovation model where interested parties, especially patients, are going to have a say in their own well-being. Thus, it is imperative that healthcare providers be efficient, effective, resilient, and sustainable in the face of significant challenges and risks. At the same time, they must offer sustainable development goals and digital transformation to healthcare users through limited governmental resources. This study investigates the role, importance, and correlation of ESG factors and digital transformation to the sustainable finance of healthcare systems through an innovative model. The main purpose of the paper is to present the already implemented ESG and DT factors in the healthcare sector and to propose a mutual and combined implementation strategy based on common evaluation tools, methods, and actions. A set of proposed actions and strategies are presented for the sustainability and resilience of the healthcare sector. Full article
(This article belongs to the Special Issue Smart and Digital Health)
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13 pages, 479 KiB  
Article
Exploring E-Health Literacy and Technology-Use Anxiety among Older Adults in Korea
by Jiyoun Kim, Sang-Wan Jeon, Hyun Byun and Eunsurk Yi
Healthcare 2023, 11(11), 1556; https://doi.org/10.3390/healthcare11111556 - 25 May 2023
Cited by 9 | Viewed by 2948
Abstract
The COVID-19 pandemic has increased the importance of health literacy in disseminating information on health in a non-contact society. This study focused on examining the acceptance capacity by older adults of smart devices in Korea and investigating the potential differences between men and [...] Read more.
The COVID-19 pandemic has increased the importance of health literacy in disseminating information on health in a non-contact society. This study focused on examining the acceptance capacity by older adults of smart devices in Korea and investigating the potential differences between men and women in terms of e-health literacy and technology-use anxiety. The study included 1369 respondents who were adults over 50 years of age and used welfare centers, public health centers, senior citizen centers, and exercise centers in Seoul and Incheon. An online survey was conducted from 1 June 2021 to 24 June 2021. The study found that the older adults’ low levels of digital literacy could limit their access to health information and negatively impact their health. The difference between men and women in terms of technology-use anxiety was statistically significant, with the latent mean for men being higher than that for women. The effect sizes of the potential mean differences were found to be at a medium level for e-health literacy and a significant level for technology-use anxiety. With Korea’s aging population and the need for the continuous management of chronic diseases among older adults, it is essential to discuss internet-based health information for disease maintenance and treatment. Full article
(This article belongs to the Special Issue Smart and Digital Health)
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37 pages, 5810 KiB  
Systematic Review
Modern Smart Gadgets and Wearables for Diagnosis and Management of Stress, Wellness, and Anxiety: A Comprehensive Review
by Aman Jolly, Vikas Pandey, Manoj Sahni, Ernesto Leon-Castro and Luis A. Perez-Arellano
Healthcare 2025, 13(4), 411; https://doi.org/10.3390/healthcare13040411 - 14 Feb 2025
Cited by 1 | Viewed by 1506
Abstract
The increasing development of gadgets to evaluate stress, wellness, and anxiety has garnered significant attention in recent years. These technological advancements aim to expedite the identification and subsequent treatment of these prevalent conditions. This study endeavors to critically examine the latest smart gadgets [...] Read more.
The increasing development of gadgets to evaluate stress, wellness, and anxiety has garnered significant attention in recent years. These technological advancements aim to expedite the identification and subsequent treatment of these prevalent conditions. This study endeavors to critically examine the latest smart gadgets and portable techniques utilized for diagnosing depression, stress, and emotional trauma while also exploring the underlying biochemical processes associated with their identification. Integrating various detectors within smartphones and smart bands enables continuous monitoring and recording of user activities. Given their widespread use, smartphones, smartwatches, and smart wristbands have become indispensable in our daily lives, prompting the exploration of their potential in stress detection and prevention. When individuals experience stress, their nervous system responds by releasing stress hormones, which can be easily identified and quantified by smartphones and smart bands. The study in this paper focused on the examination of anxiety and stress and consistently employed “heart rate variability” (HRV) characteristics for diagnostic purposes, with superior outcomes observed when HRV was combined with “electroencephalogram” (EEG) analysis. Recent research indicates that electrodermal activity (EDA) demonstrates remarkable precision in identifying anxiety. Comparisons with HRV, EDA, and breathing rate reveal that the mean heart rate employed by several commercial wearable products is less accurate in identifying anxiety and stress. This comprehensive review article provides an evidence-based evaluation of intelligent gadgets and wearable sensors, highlighting their potential to accurately assess stress, wellness, and anxiety. It also identifies areas for further research and development. Full article
(This article belongs to the Special Issue Smart and Digital Health)
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