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Background: The rates of mental health disorders such as anxiety and depression are at an all-time high especially since the onset of COVID-19, and the need for readily available digital health care solutions has never been greater. Wearable devices have increasingly incorporated sensors that were previously reserved for hospital settings. The availability of wearable device features that address anxiety and depression is still in its infancy, but consumers will soon have the potential to self-monitor moods and behaviors using everyday commercially-available devices. Objective: This study aims to explore the features of wearable devices that can be used for monitoring anxiety and depression. Methods: Six bibliographic databases, including MEDLINE, EMBASE, PsycINFO, IEEE Xplore, ACM Digital Library, and Google Scholar were used as search engines for this review. Two independent reviewers performed study selection and data extraction, while two other reviewers justified the cross-checking of extracted data. A narrative approach for synthesizing the data was utilized. Results: From 2408 initial results, 58 studies were assessed and highlighted according to our inclusion criteria. Wrist-worn devices were identified in the bulk of our studies (n = 42 or 71%). For the identification of anxiety and depression, we reported 26 methods for assessing mood, with the State-Trait Anxiety Inventory being the joint most common along with the Diagnostic and Statistical Manual of Mental Disorders (n = 8 or 14%). Finally, n = 26 or 46% of studies highlighted the smartphone as a wearable device host device. Conclusion: The emergence of affordable, consumer-grade biosensors offers the potential for new approaches to support mental health therapies for illnesses such as anxiety and depression. We believe that purposefully-designed wearable devices that combine the expertise of technologists and clinical experts can play a key role in self-care monitoring and diagnosis.

Introduction: Diabetes Mellitus (DM) is characterized by impaired ability to metabolize glucose for use in cells for energy, resulting in high blood sugar (hyperglycemia). DM impacted 463 million individuals worldwide in 2019, with over four million fatalities documented. Blood glucose levels (BGL) are usually measured, as standard protocols, through invasive procedures. Recently, Artificial Intelligence (AI) based techniques have demonstrated the potential to estimate BGL using data collected by non-invasive Wearable Devices (WDs), thereby, facilitating monitoring and management of diabetics. One of the key aspects of WDs with machine learning (ML) algorithms is to find specific data signatures, called Digital biomarkers, that can be used in classification or gaging the extent of the underlying condition. The use of such biomarkers to monitor glycemic events represents a major shift in technology for self-monitoring and developing digital biomarkers using non-invasive WDs. To do this, it is necessary to investigate the correlations between characteristics acquired from non-invasive WDs and indicators of glycemic health; furthermore, much work is needed to validate accuracy. Research Design & Methods: The study aimed to investigate performance of AI models in estimating BGL among diabetic patients using non-invasive wearable devices data An open-source dataset was used which provided BGL readings, diabetic status (Diabetic or non-diabetic), heart rate, Blood oxygen level (SPO2), Diastolic Blood pressure, Systolic Blood Pressure, Body temperature, Sweating, and Shivering for 13 participants by age group taken from WDs. Our experimental design included Data Collection, Feature Engineering, ML model selection/development, and reporting evaluation of metrics. Results: We were able to estimate with high accuracy (RMSE range: 0.099 to 0.197) the relationship between glycemic metrics and features that can be derived from non-invasive WDs when utilizing AI models. Conclusion: We provide further evidence of the feasibility of using commercially available WDs for the purpose of BGL estimation amongst diabetics.

Summary: Several reviews have been conducted regarding artificial intelligence (AI) techniques to improve pregnancy outcomes. But they are not focusing on ultrasound images. This survey aims to explore how AI can assist with fetal growth monitoring via ultrasound image. We reported our findings using the guidelines for PRISMA. We conducted a comprehensive search of eight bibliographic databases. Out of 1269 studies 107 are included. We found that 2D ultrasound images were more popular (88) than 3D and 4D ultrasound images (19). Classification is the most used method (42), followed by segmentation (31), classification integrated with segmentation (16) and other miscellaneous methods such as object-detection, regression, and reinforcement learning (18). The most common areas that gained traction within the pregnancy domain were the fetus head (43), fetus body (31), fetus heart (13), fetus abdomen (10), and the fetus face (10). This survey will promote the development of improved AI models for fetal clinical applications.

Background: Chatbots have been widely used in many spheres of life from customer services to mental health companions. Despite the breakthroughs in achieving human-like conversations, Arabic language chatbots driven by AI and NLP are relatively scarce due to the complex nature of the Arabic language. Objective: We aim to review published literature on Arabic chatbots to gain insight into the technologies used highlighting the gap in this emerging field. Methods: To identify relevant studies, we searched eight bibliographic databases and conducted backward and forward reference checking. Two reviewers independently performed study selection and data extraction. The extracted data was synthesized using a narrative approach. Results: We included 18 of 1755 retrieved publications. Thirteen unique chatbots were identified from the 18 studies. ArabChat was the most common chatbot in the included studies (n = 5). The type of Arabic language in most chatbots (n = 13) was Modern Standard Arabic. The input and output modalities used in 17 chatbots were only text. Most chatbots (n = 14) were able to have long conversations. The majority of the chatbots (n = 14) were developed to serve a specific purpose (Closed domain). A retrieval-based model was used for developing most chatbots (n = 17). Conclusion: Despite a large number of chatbots worldwide, there is relatively a small number of Arabic language chatbots. Furthermore, the available Arabic language chatbots are less advanced than other language chatbots. Researchers should develop more Arabic language chatbots that are based on more advanced input and output modalities, generative-based models, and natural language processing (NLP).

Background: Big Data offers promise in the field of mental health and plays an important part when it comes to automation, analysis and prediction of mental health disorders.Objective: The purpose of this scoping review is to explore how big data was exploited in mental health. This review specifically addresses the volume, velocity, veracity and variety of collected data as well as how data was attained, stored, managed, and kept private and secure.Methods: Six databases were searched to find relevant articles. PRISMA Extension for Scoping Reviews (PRISMA-ScR) was used as a guideline methodology to develop a comprehensive scoping review. General and Big Data features were extracted from the studies reviewed, and analyzed in the context of data collection, protection, storage and for what concerns data processing, targeted disorder and application purpose.Results: A collection of 23 studies were analyzed, mostly targeting depression (n=13) and anxiety (n=4). For what concerns data sources, mostly social media posts (n=5), tweets (n=7), and medical records (n=6) were used. Various Big Data technologies were used: for data protection, only 7 studies faced the problem, with anonymization schemes for medical records and only surveys (n=4), and safe authentication methods for social media (n=3). For data processing, Machine Learning (ML) models appeared in 22 studies of which Random Forest (RF) was the most widely used (n=5). Logistic Regression (LR) was used in 4 studies, and Support Vector Machine (SVM) was used in 3 studies.Conclusion: In order to utilize Big Data as a way to mitigate mental health disorders and predict their appearance a great effort is still needed. Integration and analysis of Big Data coming from different sources such as social media and health records and information exchange between multiple disciplines is also needed. Doctors and researchers alike can find patterns in otherwise difficult to identify data by making use of Artificial Intelligence (AI) and Machine Learning (ML) techniques. Similarly, AI and ML can be used to automate the analytical process.

Background: Corpora play a vital role when training machine learning (ML) models and building systems that use natural language processing (NLP). It can be challenging for researchers to access corpora in a language other than English, and even more so if the corpora are not available for free of cost. The Arabic language is used by more than 1.5 billion Muslims and is the native language of over 250 million people as the Quran, the core text of Islam, is written in Arabic. Objective: To highlight peer-reviewed literature reporting free and accessible Arabic corpora. We aimed to benefit researchers by providing insights into freely available Arabic and accessible corpora, allowing them to achieve their research goals with ease. Methods: By conducting a scoping review using PRISMA guidelines, we searched the most common information technology (IT) databases and identified free of cost and accessible Arabic corpora. Results: We identified a total of 48 accessible corpora sources available free of cost in the Arabic language, we present our findings according to categories to further help readers understand the corpora with direct links where available. The results were classified by corpora type into five categories based on their primary purpose. Conclusion: Arabic is underrepresented considering freely available corpora as most such corpora are available in English. Although previous studies have performed searches for corpora, ours is the first of its kind as it follows the PRISMA guidelines and includes peer-reviewed articles in the literature, obtained by searching the most common IT databases and source recommendations from language experts.

Despite improvement in detection rates, the prevalence of mental health disorders such as anxiety and depression are on the rise especially since the outbreak of the COVID-19 pandemic. Symptoms of mental health disorders have been noted and observed on social media forums such Facebook. We explored machine learning models used to detect anxiety and depression through social media. Six bibliographic databases were searched for conducting the review following PRISMA-ScR protocol. We included 54 of 2219 retrieved studies. Users suffering from anxiety or depression were identified in the reviewed studies by screening their online presence and their sharing of diagnosis by patterns in their language and online activity. Majority of the studies (70%, 38/54) were conducted at the peak of the COVID-19 pandemic (2019–2020). The studies made use of social media data from a variety of different platforms to develop predictive models for the detection of depression or anxiety. These included Twitter, Facebook, Instagram, Reddit, Sina Weibo, and a combination of different social sites posts. We report the most common Machine Learning models identified. Identification of those suffering from anxiety and depression disorders may be achieved using prediction models to detect user's language on social media and has the potential to complimenting traditional screening. Such analysis could also provide insights into the mental health of the public especially so when access to health professionals can be restricted due to lockdowns and temporary closure of services such as we saw during the peak of the COVID-19 pandemic.

Background: As public health strategists and policymakers explore different approaches to lessen the devastating effects of novel coronavirus disease (COVID-19), blockchain technology has emerged as a resource that can be utilized in numerous ways. Many blockchain technologies have been proposed or implemented during the COVID-19 pandemic; however, to the best of our knowledge, no comprehensive reviews have been conducted to uncover and summarise the main feature of these technologies.Objective: This study aims to explore proposed or implemented blockchain technologies used to mitigate the COVID-19 challenges as reported in the literature.Methods: We conducted a scoping review in line with guidelines of PRISMA Extension for Scoping Reviews (PRISMA-ScR). To identify relevant studies, we searched 11 bibliographic databases (e.g., EMBASE and MEDLINE) and conducted backward and forward reference list checking of the included studies and relevant reviews. The study selection and data extraction were conducted by 2 reviewers independently. Data extracted from the included studies was narratively summarised and described.Results: 19 of 225 retrieved studies met eligibility criteria in this review. The included studies reported 10 used cases of blockchain to mitigate COVID-19 challenges; the most prominent use cases were contact tracing and immunity passports. While the blockchain technology was developed in 10 studies, its use was proposed in the remaining 9 studies. The public blockchain technology was the most commonly utilized type in the included studies. All together, 8 different consensus mechanisms were used in the included studies. Out of 10 studies that identified the used platform, 9 studies used Ethereum to run the blockchain. Solidity was the most prominent programming language used in developing blockchain technology in the included studies. The transaction cost was reported in only 4 of the included studies and varied between USD 10−10 and USD 5. The expected latency and expected scalability were not identified in the included studies.Conclusion: Blockchain technologies are expected to play an integral role in the fight against the COVID-19 pandemic. Many possible applications of blockchain were found in this review; however, most of them are not mature enough to reveal their expected impact in the fight against COVID-19.We encourage governments, health authorities, and policymakers to consider all blockchain applications suggested in the current review to combat COVID-19 challenges. There is a pressing need to empirically examine how effective blockchain technologies are in mitigating COVID-19 challenges. Further studies are required to assess the performance of blockchain technologies’ fight against COVID-19 in terms of transaction cost, scalability, and/or latency when using different consensus algorithms, platforms, and access types.

Background: Diabetes mellitus (DM) is highly prevalent in the Middle East and North Africa (MENA) region. Mobile health (m-health) can improve communications between diabetic patients and medical teams, and this, in turn, may enhance engagement and self-management. Droobi is a multi-language mobile application designed to support self-management of patients with DM. Objective: The study aimed to explore experiences of patients and educators about their communication before and after implementation of Droobi, a diabetes management app. Methods: We interviewed a convenience sample consisting of 9 patients and 5 nurse educators. Before interviews, Droobi was downloaded to participants’ mobile phone, and their profile on the application was created. To ensure optimum usage experience, all participants received education on how to use Droobi. Participants used Droobi for 6 to 12 weeks. All interviews were audio recorded, transcribed by professionals, and thematically analyzed. Results: Two main themes were generated from participants’ responses. The first theme relates to experiences of patients and educators about their communication before Droobi and consists of 3 sub-themes: (1) how patients communicated with medical team before Droobi (previous methods), (2) adoption of previous communication methods, and (3) shortcomings of previous communication methods. The second theme relates to experiences of patients and educators about their communication after Droobi and consists of 4 sub-themes: (1) adoption of Droobi, (2) advantages of Droobi, (3) shortcomings of Droobi, and (4) improvements suggested by educators and patients. Conclusions: Our findings suggest that Droobi provided a more efficient and convenient way for communication between health workers and patients, yet multiple shortcomings and several suggestions for improvements were noted. Future work should continue evaluating the Droobi app, they should include a number of different stakeholders when developing the upcoming Droobi version taking into account the limitations and suggestions put forth by the end-users.

Background: Anxiety and depression rates are at an all-time high. Smartphone-based mental health chatbots can aid psychiatrists replacing some of the costly human based interaction providing a unique opportunity to expand the availability and quality of mental health intervention whilst providing an alternative approach to fill the much-needed self-care gap. Objective: Assess the quality and characteristics of chatbots for anxiety and depression available on Android and iOS systems. Methods: A search was performed in the App Store and Google Play Store following the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) protocol to identify existing chatbot apps for anxiety and depression. Eligibility was assessed by two individuals based on predefined eligibility criteria. Meta-data of the included chatbots and their characteristics were extracted from their description and post-installation by two reviewers. Information on quality was assessed by following the mHONcode principles. Results: Only a handful (n=11) of chatbots were included from an initial search of 1000 that provide a substitute for human-human based interaction and clearly had a therapeutic human substitute goal in mind. The majority of reviewed apps had a high number of downloads indicating their popularity. The apps were also of a general high quality based on our assessment according to the mHONcode principles. Conclusion: The general popularity of apps reviewed, and results of our quality assessment indicate chatbots have a promising future within the realm of anxiety and depression. Anxiety and depression chatbot apps have the potential to increase the capacity of mental health self-care providing much needed low-cost assistance to professionals.

Background: Since the onset of the COVID-19 pandemic, the world witnessed disruption on an unprecedented scale affecting our daily lives including but not limited to healthcare, business, education, and transportation. Deep Learning (DL) is a branch of Artificial intelligence (AI) applications, the recent growth of DL includes features that could be helpful in fighting the COVID-19 pandemic. Utilizing such features could support public health efforts. Objective: Investigate the literature available in the use of DL technology to support dealing with the COVID-19 crisis. We summarize the literature that uses DL features to analyze datasets for the purpose of a quick COVID-19 detection. Methods: This review follows PRISMA Extension for Scoping Reviews (PRISMA-ScR). We have scanned the most two commonly used databases (IEEE, ACM). Search terms were identified based on the target intervention (DL) and the target population (COVID-19). Two authors independently handled study selection and one author assigned for data extraction. A narrative approach is used to synthesize the extracted data. Results: We retrieved 53 studies and after passing through PRISMA excluding criteria, only 17 studies are considered in this review. All studies used deep learning for detection of COVID-19 cases in early stage based on different diagnostic modalities. Convolutional Neural Network (CNN) and Transfer Learning (TL) were the most commonly used techniques. Conclusion: The included studies showed that DL techniques has significant impact on early detection of COVID-19 with high accuracy rate. However, most of the proposed methods are still in development and not tested in a clinical setting. Further investigation and collaboration are required from the research community and healthcare professionals in order to develop and standardize guidelines for use of DL in the healthcare domain.

Background: Over the past decade, there has been a steady increase of the number of mobile and web-based applications that enable people with mental disorders to self-regulate their mental health needs, to feel autonomous, and to take responsibility for their own care. To the best of our knowledge, no current reviews explore the features of self-care applications used for mental disorders. Objective: The purpose of this review is to explore the characteristics of novel mobile applications utilized for mental health self-care. Methods: A scoping review of literature was conducted. The search sources consisted of searching 6 electronic databases (e.g., Medline, PsychINFO) and conducting backward and forward reference list checking. Rayyan software was used for this study selection process. One reviewer checked the eligibility of the retrieved studies and extracted data from the included studies. A narrative approach was used to synthesize the extracted data. Results: 2744 citations were identified by searching the electronic databases. Of those, 15 studies were included in this review. Twelve studies used mobile applications while the remaining three studies used web-based applications. Applications in most of the included studies targeted depression (9/15, 60%). Only two types of study designs were identified from the included studies: Randomized control trials (10/15, 67%) and Usability and feasibility studies (5/15, 33%). Six studies had a sample size of less than 50 while six studies had a sample size of more than 200. Conclusion: While many mobile apps have been developed to promote self-care, there is a gap in the current literature discussing the effectiveness of such mental health resources. Detailed systematic reviews in this field are needed. Through this scoping review we were only able to identify 15 such studies from our initial search returning 2744 studies. As such, this review will ideally encourage developers to produce future, empirically reviewed apps that rise to the challenge of both being user-friendly as well as scientifically valid.

This scoping review explores the potential of artificial intelligence (AI) in enhancing the screening, diagnosis, and monitoring of disorders related to body iron levels. A systematic search was performed to identify studies that utilize machine learning in iron-related disorders. The search revealed a wide range of machine learning algorithms used by different studies. Notably, most studies used a single data type. The studies varied in terms of sample sizes, participant ages, and geographical locations. AI's role in quantifying iron concentration is still in its early stages, yet its potential is significant. The question is whether AI-based diagnostic biomarkers can offer innovative approaches for screening, diagnosing, and monitoring of iron overload and anemia., Competing Interests: Declaration of Competing Interest The authors have no conflicts of interest to declare., (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)