# A Review of Converging Technologies in eHealth Pertaining to Artificial Intelligence
###### Abstract  
Over the last couple of years, in the context of the COVID-19 pandemic, many healthcare issues have been exacerbated, highlighting the paramount need to provide both reliable and affordable health services to remote locations by using the latest technologies such as video conferencing, data management, the secure transfer of patient information, and efficient data analysis tools such as machine learning algorithms. In the constant struggle to offer healthcare to everyone, many modern technologies find applicability in eHealth, mHealth, telehealth or telemedicine. Through this paper, we attempt to render an overview of what different technologies are used in certain healthcare applications, ranging from remote patient monitoring in the field of cardio-oncology to analyzing EEG signals through machine learning for the prediction of seizures, focusing on the role of artificial intelligence in eHealth.  
eHealth; mHealth; telehealth; telemedicine; remote patient monitoring; Internet of Things; brain-computer interface; artificial intelligence; machine learning; deep learning +
Footnote †: journal: International Journal of and Public Health  
The primary benefit of AI algorithms is that they are implemented in a way that mimics certain aspects of human thinking, and they can handle various structured or unstructured types of input data when aiming to solve a specific problem. There are multiple subsets of AI, one of them being machine learning (ML). ML is a method used to train a model from datasets without explicitly programming it. The model can then be used to replicate the learnt behavior on new data. There are several approaches to ML, some of them being:  
* Supervised learning: learning starts with a set of example inputs and their correct outputs.
* Unsupervised learning: only input data are provided and the algorithm groups or clusters the data, giving it a structure.
* Semi-supervised learning: some of the data are missing training labels.
* Reinforcement learning: focused on learning through trial and error.  
Deep learning (DL) is a subset of ML that employs multiple layers to extract features from raw input data.  
AI is one of the key technologies responsible for the evolution of eHealth, bringing multiple advantages to the field of healthcare, such as the following:  
* Improvements in diagnosis accuracy, e.g., detecting heart failure [2].
* Risk prediction, e.g., predicting antibiotic resistance through machine learning [3], cardiovascular disease prediction from AI-based models [4], and using deep learning to predict cardiac indices [5].
* Clinical applications, e.g., nutrition assessment [6].
* Healthcare process optimization, e.g., the complexity and the potential of integrating AI into healthcare processes [7].
* Patient flow, e.g., enhancing patient flow for mental health [8; 9].
* Precision medicine, e.g., detecting sleep apnea through deep learning [10].
* Automate detection, e.g., automated acute myocardial infarction detection using ECGs from smartwatches [11].
* Improved quality of care, e.g., using AI to improve chronic disease care for type 2 diabetes mellitus patients [12].
* Reduced healthcare costs, e.g., predicting health and population well-being [13].
* Discovering adverse effects of medication, e.g., machine learning models discovering adverse drug effects on the gut microbiome [14].