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Future Internet
Special Issues
eHealth and mHealth
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eHealth and mHealth

A special issue of Future Internet (ISSN 1999-5903).

Deadline for manuscript submissions: closed (15 December 2024) | Viewed by 29711

Special Issue Editors

Dr. Sten Hanke

E-Mail Website
Guest Editor
eHealth Institute, FH JOANNEUM University of Applied Sciences, 8020 Graz, Austria
Interests: health informatics; personal health; eHealth; mHealth
Special Issues, Collections and Topics in MDPI journals
Dr. Bernhard Neumayer

E-Mail Website
Guest Editor
eHealth Institute, FH JOANNEUM University of Applied Sciences, 8020 Graz, Austria
Interests: AI; eHealth; mHealth
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Stefan Sauermann

E-Mail Website
Guest Editor
Department of Biomedical Engineering, University of Applied Sciences Technikum Wien, 1200 Vienna, Austria
Interests: medical engineering; eHealth
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

eHealth, or Electronic Health, signifies the utilization of information and communication technologies (ICTs) to fortify and elevate health and healthcare. This comprehensive concept encompasses a diverse array of applications, services, and systems designed to facilitate the electronic management of health information. It fosters seamless communication between healthcare providers and patients, ultimately enhancing the delivery of healthcare services.

Moreover, mHealth, or Mobile Health, is the strategic use of mobile devices, including smartphones, tablets, wearables, and wireless technologies. Its purpose is to bolster healthcare delivery, amplify health outcomes, and provide accessible health-related information and services.

These transformative trends have the potential to disrupt the healthcare system, ushering in a paradigm shift towards predictive, prevention-focused, and personalized service delivery, underpinned by the utilization of digital data. The emergence of new services holds the promise of actively involving participants in their own journey towards maintaining good health and reshaping how they receive healthcare services.

In unison, eHealth and mHealth contribute synergistically to the ongoing digitization and advancement of the healthcare landscape, marking a pivotal era of innovation and enhanced patient-centric care.

Topics include, but are not limited to, the following:

  • Usability and user experience in health apps;
  • Effectiveness of mobile health interventions;
  • Data security and privacy in eHealth;
  • Integration of wearables and ioT in healthcare;
  • Machine learning and predictive analytics in eHealth;
  • Telemedicine and remote patient monitoring;
  • Behavioral interventions through mobile apps;
  • Ethical considerations in eHealth and mHealth;
  • Human-centered design for health technologies.

Dr. Sten Hanke
Dr. Bernhard Neumayer
Prof. Dr. Stefan Sauermann
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. Future Internet is an international peer-reviewed open access monthly 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 1600 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

  • eHealth
  • mHealth
  • wearables
  • telemedicine
  • interoperability
  • mobile apps
  • standards
  • patient empowerment

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Related Special Issue

Published Papers (11 papers)

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Editorial

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2 pages, 133 KiB  
Editorial
eHealth and mHealth
by Bernhard Neumayer, Stefan Sauermann and Sten Hanke
Future Internet 2025, 17(4), 152; https://doi.org/10.3390/fi17040152 - 31 Mar 2025
Viewed by 176
Abstract
eHealth (electronic health) and mHealth (mobile health) have been rapidly evolving in recent years, offering innovative solutions to healthcare challenges [...] Full article
(This article belongs to the Special Issue eHealth and mHealth)

Research

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19 pages, 5346 KiB  
Article
Metastable Substructure Embedding and Robust Classification of Multichannel EEG Data Using Spectral Graph Kernels
by Rashmi N. Muralinath, Vishwambhar Pathak and Prabhat K. Mahanti
Future Internet 2025, 17(3), 102; https://doi.org/10.3390/fi17030102 - 23 Feb 2025
Cited by 1 | Viewed by 513
Abstract
Classification of neurocognitive states from Electroencephalography (EEG) data is complex due to inherent challenges such as noise, non-stationarity, non-linearity, and the high-dimensional and sparse nature of connectivity patterns. Graph-theoretical approaches provide a powerful framework for analysing the latent state dynamics using connectivity measures [...] Read more.
Classification of neurocognitive states from Electroencephalography (EEG) data is complex due to inherent challenges such as noise, non-stationarity, non-linearity, and the high-dimensional and sparse nature of connectivity patterns. Graph-theoretical approaches provide a powerful framework for analysing the latent state dynamics using connectivity measures across spatio-temporal-spectral dimensions. This study applies the graph Koopman embedding kernels (GKKE) method to extract latent neuro-markers of seizures from epileptiform EEG activity. EEG-derived graphs were constructed using correlation and mean phase locking value (mPLV), with adjacency matrices generated via threshold-binarised connectivity. Graph kernels, including Random Walk, Weisfeiler–Lehman (WL), and spectral-decomposition (SD) kernels, were evaluated for latent space feature extraction by approximating Koopman spectral decomposition. The potential of graph Koopman embeddings in identifying latent metastable connectivity structures has been demonstrated with empirical analyses. The robustness of these features was evaluated using classifiers such as Decision Trees, Support Vector Machine (SVM), and Random Forest, on Epilepsy-EEG from the Children’s Hospital Boston’s (CHB)-MIT dataset and cognitive-load-EEG datasets from online repositories. The classification workflow combining mPLV connectivity measure, WL graph Koopman kernel, and Decision Tree (DT) outperformed the alternative combinations, particularly considering the accuracy (91.7%) and F1-score (88.9%), The comparative investigation presented in results section convinces that employing cost-sensitive learning improved the F1-score for the mPLV-WL-DT workflow to 91% compared to 88.9% without cost-sensitive learning. This work advances EEG-based neuro-marker estimation, facilitating reliable assistive tools for prognosis and cognitive training protocols. Full article
(This article belongs to the Special Issue eHealth and mHealth)
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24 pages, 654 KiB  
Article
Deep Learning Framework for Advanced De-Identification of Protected Health Information
by Ahmad Aloqaily, Emad E. Abdallah, Rahaf Al-Zyoud, Esraa Abu Elsoud, Malak Al-Hassan and Alaa E. Abdallah
Future Internet 2025, 17(1), 47; https://doi.org/10.3390/fi17010047 - 20 Jan 2025
Cited by 1 | Viewed by 939
Abstract
Electronic health records (EHRs) are widely used in healthcare institutions worldwide, containing vast amounts of unstructured textual data. However, the sensitive nature of Protected Health Information (PHI) embedded within these records presents significant privacy challenges, necessitating robust de-identification techniques. This paper introduces a [...] Read more.
Electronic health records (EHRs) are widely used in healthcare institutions worldwide, containing vast amounts of unstructured textual data. However, the sensitive nature of Protected Health Information (PHI) embedded within these records presents significant privacy challenges, necessitating robust de-identification techniques. This paper introduces a novel approach, leveraging a Bi-LSTM-CRF model to achieve accurate and reliable PHI de-identification, using the i2b2 dataset sourced from Harvard University. Unlike prior studies that often unify Bi-LSTM and CRF layers, our approach focuses on the individual design, optimization, and hyperparameter tuning of both the Bi-LSTM and CRF components, allowing for precise model performance improvements. This rigorous approach to architectural design and hyperparameter tuning, often underexplored in the existing literature, significantly enhances the model’s capacity for accurate PHI tag detection while preserving the essential clinical context. Comprehensive evaluations are conducted across 23 PHI categories, as defined by HIPAA, ensuring thorough security across critical domains. The optimized model achieves exceptional performance metrics, with a precision of 99%, recall of 98%, and F1-score of 98%, underscoring its effectiveness in balancing recall and precision. By enabling the de-identification of medical records, this research strengthens patient confidentiality, promotes compliance with privacy regulations, and facilitates safe data sharing for research and analysis. Full article
(This article belongs to the Special Issue eHealth and mHealth)
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21 pages, 1716 KiB  
Article
AI-Driven Neuro-Monitoring: Advancing Schizophrenia Detection and Management Through Deep Learning and EEG Analysis
by Elena-Anca Paraschiv, Lidia Băjenaru, Cristian Petrache, Ovidiu Bica and Dragoș-Nicolae Nicolau
Future Internet 2024, 16(11), 424; https://doi.org/10.3390/fi16110424 - 16 Nov 2024
Cited by 4 | Viewed by 2329
Abstract
Schizophrenia is a complex neuropsychiatric disorder characterized by disruptions in brain connectivity and cognitive functioning. Continuous monitoring of neural activity is essential, as it allows for the detection of subtle changes in brain connectivity patterns, which could provide early warnings of cognitive decline [...] Read more.
Schizophrenia is a complex neuropsychiatric disorder characterized by disruptions in brain connectivity and cognitive functioning. Continuous monitoring of neural activity is essential, as it allows for the detection of subtle changes in brain connectivity patterns, which could provide early warnings of cognitive decline or symptom exacerbation, ultimately facilitating timely therapeutic interventions. This paper proposes a novel approach for detecting schizophrenia-related abnormalities using deep learning (DL) techniques applied to electroencephalogram (EEG) data. Using an openly available EEG dataset on schizophrenia, the focus is on preprocessed event-related potentials (ERPs) from key electrode sites and applied transfer entropy (TE) analysis to quantify the directional flow of information between brain regions. TE matrices were generated to capture neural connectivity patterns, which were then used as input for a hybrid DL model, combining convolutional neural networks (CNNs) and Bidirectional Long Short-Term Memory (BiLSTM) networks. The model achieved a performant accuracy of 99.94% in classifying schizophrenia-related abnormalities, demonstrating its potential for real-time mental health monitoring. The generated TE matrices revealed significant differences in connectivity between the two groups, particularly in frontal and central brain regions, which are critical for cognitive processing. These findings were further validated by correlating the results with EEG data obtained from the Muse 2 headband, emphasizing the potential for portable, non-invasive monitoring of schizophrenia in real-world settings. The final model, integrated into the NeuroPredict platform, offers a scalable solution for continuous mental health monitoring. By incorporating EEG data, heart rate, sleep patterns, and environmental metrics, NeuroPredict facilitates early detection and personalized interventions for schizophrenia patients. Full article
(This article belongs to the Special Issue eHealth and mHealth)
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23 pages, 1624 KiB  
Article
An Explainable Deep Learning-Enhanced IoMT Model for Effective Monitoring and Reduction of Maternal Mortality Risks
by Sherine Nagy Saleh, Mazen Nabil Elagamy, Yasmine N. M. Saleh and Radwa Ahmed Osman
Future Internet 2024, 16(11), 411; https://doi.org/10.3390/fi16110411 - 8 Nov 2024
Cited by 3 | Viewed by 1670
Abstract
Maternal mortality (MM) is considered one of the major worldwide concerns. Despite the advances of artificial intelligence (AI) in healthcare, the lack of transparency in AI models leads to reluctance to adopt them. Employing explainable artificial intelligence (XAI) thus helps improve the transparency [...] Read more.
Maternal mortality (MM) is considered one of the major worldwide concerns. Despite the advances of artificial intelligence (AI) in healthcare, the lack of transparency in AI models leads to reluctance to adopt them. Employing explainable artificial intelligence (XAI) thus helps improve the transparency and effectiveness of AI-driven healthcare solutions. Accordingly, this article proposes a complete framework integrating an Internet of Medical Things (IoMT) architecture with an XAI-based deep learning model. The IoMT system continuously monitors pregnant women’s vital signs, while the XAI model analyzes the collected data to identify risk factors and generate actionable insights. Additionally, an efficient IoMT transmission model is developed to ensure reliable data transfer with the best-required system quality of service (QoS). Further analytics are performed on the data collected from different regions in a country to address high-risk cities. The experiments demonstrate the effectiveness of the proposed framework by achieving an accuracy of 80% for patients and 92.6% for regional risk prediction and providing interpretable explanations. The XAI-generated insights empower healthcare providers to make informed decisions and implement timely interventions. Furthermore, the IoMT transmission model ensures efficient and secure data transfer. Full article
(This article belongs to the Special Issue eHealth and mHealth)
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16 pages, 948 KiB  
Article
Masketeer: An Ensemble-Based Pseudonymization Tool with Entity Recognition for German Unstructured Medical Free Text
by Martin Baumgartner, Karl Kreiner, Fabian Wiesmüller, Dieter Hayn, Christian Puelacher and Günter Schreier
Future Internet 2024, 16(8), 281; https://doi.org/10.3390/fi16080281 - 6 Aug 2024
Cited by 1 | Viewed by 1433
Abstract
Background: The recent rise of large language models has triggered renewed interest in medical free text data, which holds critical information about patients and diseases. However, medical free text is also highly sensitive. Therefore, de-identification is typically required but is complicated since medical [...] Read more.
Background: The recent rise of large language models has triggered renewed interest in medical free text data, which holds critical information about patients and diseases. However, medical free text is also highly sensitive. Therefore, de-identification is typically required but is complicated since medical free text is mostly unstructured. With the Masketeer algorithm, we present an effective tool to de-identify German medical text. Methods: We used an ensemble of different masking classes to remove references to identifiable data from over 35,000 clinical notes in accordance with the HIPAA Safe Harbor Guidelines. To retain additional context for readers, we implemented an entity recognition scheme and corpus-wide pseudonymization. Results: The algorithm performed with a sensitivity of 0.943 and specificity of 0.933. Further performance analyses showed linear runtime complexity (O(n)) with both increasing text length and corpus size. Conclusions: In the future, large language models will likely be able to de-identify medical free text more effectively and thoroughly than handcrafted rules. However, such gold-standard de-identification tools based on large language models are yet to emerge. In the current absence of such, we hope to provide best practices for a robust rule-based algorithm designed with expert domain knowledge. Full article
(This article belongs to the Special Issue eHealth and mHealth)
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18 pages, 1364 KiB  
Article
In-Home Evaluation of the Neo Care Artificial Intelligence Sound-Based Fall Detection System
by Carol Maher, Kylie A. Dankiw, Ben Singh, Svetlana Bogomolova and Rachel G. Curtis
Future Internet 2024, 16(6), 197; https://doi.org/10.3390/fi16060197 - 2 Jun 2024
Cited by 2 | Viewed by 1694
Abstract
The Neo Care home monitoring system aims to detect falls and other events using artificial intelligence. This study evaluated Neo Care’s accuracy and explored user perceptions through a 12-week in-home trial with 18 households of adults aged 65+ years old at risk of [...] Read more.
The Neo Care home monitoring system aims to detect falls and other events using artificial intelligence. This study evaluated Neo Care’s accuracy and explored user perceptions through a 12-week in-home trial with 18 households of adults aged 65+ years old at risk of falls (mean age: 75.3 years old; 67% female). Participants logged events that were cross-referenced with Neo Care logs to calculate sensitivity and specificity for fall detection and response. Qualitative interviews gathered in-depth user feedback. During the trial, 28 falls/events were documented, with 12 eligible for analysis as others occurred outside the home or when devices were offline. Neo Care was activated 4939 times—4930 by everyday household sounds and 9 by actual falls. Fall detection sensitivity was 75.00% and specificity 6.80%. For responding to falls, sensitivity was 62.50% and specificity 17.28%. Users felt more secure with Neo Care but identified needs for further calibration to improve accuracy. Advantages included avoiding wearables, while key challenges were misinterpreting noises and occasional technical issues like going offline. Suggested improvements were visual indicators, trigger words, and outdoor capability. The study demonstrated Neo Care’s potential with modifications. Users found it beneficial, but highlighted areas for improvement. Real-world evaluations and user-centered design are crucial for healthcare technology development. Full article
(This article belongs to the Special Issue eHealth and mHealth)
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26 pages, 1791 KiB  
Article
The Future of Healthcare with Industry 5.0: Preliminary Interview-Based Qualitative Analysis
by Juliana Basulo-Ribeiro and Leonor Teixeira
Future Internet 2024, 16(3), 68; https://doi.org/10.3390/fi16030068 - 22 Feb 2024
Cited by 15 | Viewed by 7378
Abstract
With the advent of Industry 5.0 (I5.0), healthcare is undergoing a profound transformation, integrating human capabilities with advanced technologies to promote a patient-centered, efficient, and empathetic healthcare ecosystem. This study aims to examine the effects of Industry 5.0 on healthcare, emphasizing the synergy [...] Read more.
With the advent of Industry 5.0 (I5.0), healthcare is undergoing a profound transformation, integrating human capabilities with advanced technologies to promote a patient-centered, efficient, and empathetic healthcare ecosystem. This study aims to examine the effects of Industry 5.0 on healthcare, emphasizing the synergy between human experience and technology. To this end, 6 specific objectives were found, which were answered in the results through an empirical study based on interviews with 11 healthcare professionals. This article thus outlines strategic and policy guidelines for the integration of I5.0 in healthcare, advocating policy-driven change, and contributes to the literature by offering a solid theoretical basis on I5.0 and its impact on the healthcare sector. Full article
(This article belongs to the Special Issue eHealth and mHealth)
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Review

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32 pages, 2728 KiB  
Review
Trends, Challenges, and Applications of Large Language Models in Healthcare: A Bibliometric and Scoping Review
by Vincenza Carchiolo and Michele Malgeri
Future Internet 2025, 17(2), 76; https://doi.org/10.3390/fi17020076 - 8 Feb 2025
Cited by 1 | Viewed by 1440
Abstract
The application of Large Language Models (LLMs) in medicine represents an area of growing interest in scientific research. This study presents a quantitative review of the scientific literature aiming at analyzing emerging trends in the use of LLMs in the [...] Read more.
The application of Large Language Models (LLMs) in medicine represents an area of growing interest in scientific research. This study presents a quantitative review of the scientific literature aiming at analyzing emerging trends in the use of LLMs in the medical field. Through a systematic analysis of works extracted from Scopus, the study examines the temporal evolution, geographical distribution, and scientific collaborations between research institutions and nations. Furthermore, the main topics addressed in the most cited papers are identified, and the most recent and relevant reviews are explored in depth. The quantitative approach enables mapping the development of research, highlighting both opportunities and open challenges. This study presents a comprehensive analysis of research articles and review-type articles across several years, focusing on temporal, geographical, and thematic trends. The temporal analysis reveals significant shifts in research activity, including periods of increased or decreased publication output and the emergence of new areas of interest. Geographically, the results identify regions and countries with higher concentrations of publications, as well as regions experiencing growing or stagnant international collaboration. The thematic analysis highlights the key research areas addressed in the reviewed papers, tracking evolving topics and changes in research focus over time. Additionally, the collaborative analysis sheds light on key networks of international collaboration, revealing changes in the distribution of affiliations across subperiods and publication types. Finally, an investigation of the most cited papers highlights the works that have had the greatest impact on the scientific community, identifying enduring themes and methodologies that continue to shape the field of study. The results provide a clear overview of current trends and future perspectives for the application of LLMs in medicine, offering a valuable reference for researchers and professionals in the field. Full article
(This article belongs to the Special Issue eHealth and mHealth)
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33 pages, 1131 KiB  
Review
Artificial Intelligence to Reshape the Healthcare Ecosystem
by Gianluca Reali and Mauro Femminella
Future Internet 2024, 16(9), 343; https://doi.org/10.3390/fi16090343 - 20 Sep 2024
Cited by 2 | Viewed by 1318
Abstract
This paper intends to provide the reader with an overview of the main processes that are introducing artificial intelligence (AI) into healthcare services. The first part is organized according to an evolutionary perspective. We first describe the role that digital technologies have had [...] Read more.
This paper intends to provide the reader with an overview of the main processes that are introducing artificial intelligence (AI) into healthcare services. The first part is organized according to an evolutionary perspective. We first describe the role that digital technologies have had in shaping the current healthcare methodologies and the relevant foundations for new evolutionary scenarios. Subsequently, the various evolutionary paths are illustrated with reference to AI techniques and their research activities, specifying their degree of readiness for actual clinical use. The organization of this paper is based on the interplay three pillars, namely, algorithms, enabling technologies and regulations, and healthcare methodologies. Through this organization we introduce the reader to the main evolutionary aspects of the healthcare ecosystem, to associate clinical needs with appropriate methodologies. We also explore the different aspects related to the Internet of the future that are not typically presented in papers that focus on AI, but that are equally crucial to determine the success of current research and development activities in healthcare. Full article
(This article belongs to the Special Issue eHealth and mHealth)
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32 pages, 1667 KiB  
Review
Artificial Intelligence Applications in Smart Healthcare: A Survey
by Xian Gao, Peixiong He, Yi Zhou and Xiao Qin
Future Internet 2024, 16(9), 308; https://doi.org/10.3390/fi16090308 - 27 Aug 2024
Cited by 2 | Viewed by 9766
Abstract
The rapid development of AI technology in recent years has led to its widespread use in daily life, where it plays an increasingly important role. In healthcare, AI has been integrated into the field to develop the new domain of smart healthcare. In [...] Read more.
The rapid development of AI technology in recent years has led to its widespread use in daily life, where it plays an increasingly important role. In healthcare, AI has been integrated into the field to develop the new domain of smart healthcare. In smart healthcare, opportunities and challenges coexist. This article provides a comprehensive overview of past developments and recent progress in this area. First, we summarize the definition and characteristics of smart healthcare. Second, we explore the opportunities that AI technology brings to the smart healthcare field from a macro perspective. Third, we categorize specific AI applications in smart healthcare into ten domains and discuss their technological foundations individually. Finally, we identify ten key challenges these applications face and discuss the existing solutions for each. Full article
(This article belongs to the Special Issue eHealth and mHealth)
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