# eHealth and mHealth
###### Abstract  
eHealth (electronic health) and mHealth (mobile health) have been rapidly evolving in recent years, offering innovative solutions to healthcare challenges. The combination of these two fields can provide many health-related data while keeping track of data exchange, interoperability, and security. The potential large amount of data provided by many wearables and sensors, in turn, provides an excellent basis for data-driven methods employing machine learning algorithms and further artificial intelligence (AI)-based methods-- interested readers are referred to the Special Issue _Challenges and Opportunities in Electronic Medical Record (EMR)_[1]. Accordingly, eHealth and mHealth cover a wide range of topics and applications. Nevertheless, providing and exchanging large amounts of data will also bring about new challenges. Mobile apps used for health tracking often require personal data that could be vulnerable to hacking or third-party misuse; the collection, storage, and sharing of health data raise concerns about data breaches, unauthorized access, and misuse of sensitive personal health information. In terms of interoperability, different healthcare systems, platforms, and devices often fail to communicate with each other efficiently, but, even when this barrier can be overcome, healthcare professionals may be resistant to adopting eHealth or mHealth tools, especially if they disrupt existing workflows. In addition, both eHealth and mHealth solutions heavily rely on technology, which means they are susceptible to technical failures, downtime, and bugs. Therefore, it is critical to ensure that health data are accurate and clinically useful, which require documented evaluations of the proposed methods and explainability for decision systems. For this Special Issue, we selected contributions that reflect the breadth of the field. The publications by [PERSON] et al. [2] and [PERSON] et al. [3] focused on the aforementioned security aspects, while [PERSON] et al. [4] highlighted the data-driven machine learning aspects. In _Deep Learning Framework for Advanced De-Identification of Protected Health Information_, [PERSON] et al. applied _conditional random field_ (CRF) models for the de-identification of medical records. In _Masketer: An Ensemble-Based Pseudonymization Tool with Entity Recognition for German Unstructured Medical Free Text_, [PERSON] et al. developed a tool to mask medical free texts in accordance with the HIPAA Safe Harbor Guidelines. [PERSON] et al. proposed a framework for cost-sensitive EEG classification based on graph kernel-based Koopman embeddings in _Metastable Substructure Embedding and Robust Classification of Multichannel EEG Data Using Spectral Graph Kernels_. The studies by [PERSON] et al. [5], [PERSON] et al. [6], and [PERSON] et al. [7] address specific health-related problems. By applying _transfer entropy_ (TE) analysis, [PERSON] et al. investigated connectivity patterns in patients with schizophrenia, [PERSON] et al. proposed a deep learning framework enabled by XAI _(explainable artificial intelligence)_ to reduce maternal mortality risks while maintaining secure data transmission in _An Explainable DeepLearning-Enhanced IoMT Model for Effective Monitoring and Reduction of Maternal Mortality Risks, and [PERSON] et al. investigated the accuracy and usability of a fall detection system in In-Home Evaluation of the Neo Care Artificial Intelligence Sound-Based Fall Detection System. On the other hand, [PERSON] and [PERSON] considered future economic and commercial aspects using a SWOT (strengths, weaknesses, opportunities, and threats) analysis in _The Future of Healthcare with Industry 5.0: Preliminary Interview-Based Qualitative Analysis_[8]. To reflect the scale of the subject and the pace of development, this Special Issue also comprises three review papers investigating current eHealth and mHealth topics.