Telehealth and Artificial Intelligence Insights into Healthcare during the COVID-19 Pandemic

Tele-medicine controlled hospital at home is associated with better outcomes than hospital stay

BACKGROUND

Hospital-at-home (HAH) is increasingly becoming an alternative for in-hospital stay in selected clinical scenarios. Nevertheless, there is still a question whether HAH could be a viable option for acutely ill patients, otherwise hospitalized in departments of general-internal medicine.

METHODS

This was a retrospective matched study, conducted at a telemedicine controlled HAH department, being part of a tertiary medical center. The objective was to compare clinical outcomes of acutely ill patients (both COVID-19 and non-COVID) admitted to either in-hospital or HAH. Non-COVID patients had one of three acute infectious diseases: urinary tract infections (UTI, either lower or upper), pneumonia, or cellulitis.

RESULTS

The analysis involved 159 HAH patients (64 COVID-19 and 95 non-COVID) who were compared to a matched sample of in-hospital patients (192 COVID-19 and 285 non-COVID). The median length-of-hospital stay (LOS) was 2 days shorter in the HAH for both COVID-19 patients (95% CI: 1-3; p = 0.008) and non-COVID patients (95% CI; 1-3; p < 0.001). The readmission rates within 30 days were not significantly different for both COVID-19 patients (Odds Ratio (OR) = 1; 95% CI: 0.49-2.04; p = 1) and non-COVID patients (OR = 0.7; 95% CI; 0.39-1.28; p = 0.25). The differences remained insignificant within one year. The risk of death within 30 days was significantly lower in the HAH group for COVID-19 patients (OR = 0.34; 95% CI: 0.11-0.86; p = 0.018) and non-COVID patients (OR = 0.38; 95% CI: 0.14-0.9; p = 0.019). For one year survival period, the differences were significant for COVID-19 patients (OR = 0.5; 95% CI: 0.31-0.9; p = 0.044) and insignificant for non-COVID patients (OR = 0.63; 95% CI: 0.4-1; p = 0.052).

CONCLUSIONS

Care for acutely ill patients in the setting of telemedicine-based hospital at home has the potential to reduce hospitalization length without increasing readmission risk and to reduce both 30 days and one-year mortality rates.

Anxiety in young people: Analysis from a machine learning model

The study addresses the detection of anxiety symptoms in young people using artificial intelligence models. Questionnaires such as the Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder 7-item scale (GAD-7) are used to collect data, with a focus on early detection of anxiety. Three machine learning models are employed: Support Vector Machine (SVM), K Nearest Neighbors (KNN), and Random Forest (RF), with cross-validation to assess their effectiveness. Results show that the RF model is the most efficient, with an accuracy of 91 %, surpassing previous studies. Significant predictors of anxiety are identified, such as parental education level, alcohol consumption, and social security affiliation. A relationship is observed between anxiety and personal and family history of mental illness, as well as with characteristics external to the model, such as family and personal history of depression. The analysis of the results highlights the importance of considering not only clinical but also social and family aspects in mental health interventions. It is suggested that the sample size be expanded in future studies to improve the robustness of the model. In summary, the study demonstrates the usefulness of artificial intelligence in the early detection of anxiety in young people and highlights the relevance of addressing multidimensional factors in the assessment and treatment of this condition.

The Use of COVID-19 Mobile Apps in Connecting Patients with Primary Healthcare in 30 Countries: Eurodata Study

BACKGROUND

The COVID-19 pandemic has necessitated changes in European healthcare systems, with a significant proportion of COVID-19 cases being managed on an outpatient basis in primary healthcare (PHC). To alleviate the burden on healthcare facilities, many European countries developed contact-tracing apps and symptom checkers to identify potential cases. As the pandemic evolved, the European Union introduced the Digital COVID-19 Certificate for travel, which relies on vaccination, recent recovery, or negative test results. However, the integration between these apps and PHC has not been thoroughly explored in Europe.

OBJECTIVE

To describe if governmental COVID-19 apps allowed COVID-19 patients to connect with PHC through their apps in Europe and to examine how the Digital COVID-19 Certificate was obtained.

METHODOLOGY

Design and setting: Retrospective descriptive study in PHC in 30 European countries. An ad hoc, semi-structured questionnaire was developed to collect country-specific data on primary healthcare activity during the COVID-19 pandemic and the use of information technology tools to support medical care from 15 March 2020 to 31 August 2021. Key informants belong to the WONCA Europe network (World Organization of Family Doctors). The data were collected from relevant and reliable official sources, such as governmental websites and guidelines.

MAIN OUTCOME MEASURES

Patient's first contact with health system, governmental COVID-19 app (name and function), Digital COVID-19 Certification, COVID-19 app connection with PHC.

RESULTS

Primary care was the first point of care for suspected COVID-19 patients in 28 countries, and 24 countries developed apps to complement classical medical care. The most frequently developed app was for tracing COVID-19 cases (24 countries), followed by the Digital COVID-19 Certificate app (17 countries). Bulgaria, Italy, Serbia, North Macedonia, and Romania had interoperability between PHC and COVID-19 apps, and Poland and Romania's apps considered social needs.

CONCLUSIONS

COVID-19 apps were widely created during the first pandemic year. Contact tracing was the most frequent function found in the registered apps. Connection with PHC was scarcely developed. In future pandemics, connections between health system levels should be guaranteed to develop and implement effective strategies for managing diseases.

Implementing Systematic Patient-Reported Measures for Chronic Conditions Through the Naveta Value-Based Telemedicine Initiative: Observational Retrospective Multicenter Study

BACKGROUND

Patient-reported outcome and experience measures can play a critical role in providing patient-centered and value-based health care to a growing population of patients who are chronically ill. Value-based telemedicine platforms such as the Naveta initiative may facilitate the effective integration of these tools into health care systems.

OBJECTIVE

This study aims to evaluate the response rate to electronic patient-reported outcome measures (ePROMs) and electronic patient-reported experience measures (ePREMs) among patients participating in the Naveta telemedicine initiative and its correlations with sociodemographic and clinical characteristics, as well as the evolution of the response rates over time.

METHODS

Between January 1, 2021, and June 30, 2023, a total of 53,364 ePREMs and ePROMs for 20 chronic conditions were administered through the Naveta-Phemium platform. Descriptive statistics were used to summarize continuous and categorical variables. Differences in response rates within each sociodemographic variable were analyzed using logistic regression models, with significance assessed via chi-square and post hoc Tukey tests. Two-way ANOVA was used to examine the interaction between time interval and disease type on response rate evolution.

RESULTS

A total of 3372 patients with severe chronic diseases from 64 public hospitals in Spain participated in the Naveta health questionnaire project. The overall response rate to ePROMs and ePREMs during the first 2.5 years of the Naveta initiative was 46.12% (24,704/53,364), with a baseline rate of 53.33% (7198/13,496). Several sociodemographic factors correlated with lower response rates, including male gender, older age, lower education level, frequent alcohol use, being a student, and not being physically active. There were also significant variations in response rates among different types of chronic conditions (P<.001), with the highest rates being for respiratory (433/606, 71.5%), oncologic (200/319, 62.7%), digestive (2247/3601, 62.4%), and rheumatic diseases (7506/12,982, 57.82%) and the lowest being for HIV infection (7473/22,695, 32.93%). During the first 6 months of follow-up, the response rates decreased in all disease types, except in the case of the group of patients with oncologic disease, among whom the response rate increased up to 100% (6/6). Subsequently, the overall response rate approached baseline levels.

CONCLUSIONS

Recognizing the influence of sociodemographic factors on response rates is critical to identifying barriers to participation in telemonitoring programs and ensuring inclusiveness in patient-centered health care practices. The observed decline in response rates at follow-up may be due to survey fatigue, highlighting the need for strategies to mitigate this effect. In addition, the variation in response rates across chronic conditions emphasizes the importance of tailoring telemonitoring approaches to specific patient populations.

A Compass for North Carolina Health Care Workers Navigating the Adoption of Artificial Intelligence

This article underscores the economic benefits of AI, the importance of collaborative innovation, and the need for workforce development to prepare health care professionals for an AI-enhanced future. We include guidance for strategic and ethical AI adoption while advocating for a unified approach to leveraging technology to improve patient outcomes.

Development and Validation of a Deep Learning Model for Histopathological Slide Analysis in Lung Cancer Diagnosis

Lung cancer is the leading cause of cancer-related deaths worldwide. Two of the crucial factors contributing to these fatalities are delayed diagnosis and suboptimal prognosis. The rapid advancement of deep learning (DL) approaches provides a significant opportunity for medical imaging techniques to play a pivotal role in the early detection of lung tumors and subsequent monitoring during treatment. This study presents a DL-based model for efficient lung cancer detection using whole-slide images. Our methodology combines convolutional neural networks (CNNs) and separable CNNs with residual blocks, thereby improving classification performance. Our model improves accuracy (96% to 98%) and robustness in distinguishing between cancerous and non-cancerous lung cell images in less than 10 s. Moreover, the model's overall performance surpassed that of active pathologists, with an accuracy of 100% vs. 79%. There was a significant linear correlation between pathologists' accuracy and years of experience (r Pearson = 0.71, 95% CI 0.14 to 0.93, p = 0.022). We conclude that this model enhances the accuracy of cancer detection and can be used to train junior pathologists.

Greenway of Digital Health Technology During COVID-19 Crisis: Bibliometric Analysis, Challenges, and Future Perspective

Digital health has transformed the healthcare landscape by leveraging technology to improve patient outcomes and access to medical services. The COVID-19 pandemic has highlighted the urgent need for digital healthcare solutions that can mitigate the impact of the outbreak while ensuring patient safety. In this chapter, we delve into how digital health technologies such as telemedicine, mobile apps, and wearable devices can provide personalized care, reduce healthcare provider burden, and lower healthcare costs. We also explore the creation of a greenway of digital healthcare that safeguards patient confidentiality, enables efficient communication, and ensures cost-effective payment systems. This chapter showcases the potential of digital health to revolutionize healthcare delivery while ensuring patient well-being and medical staff satisfaction.

Factors Influencing the Acceptance and Adoption of Mobile Health Apps by Physicians During the COVID-19 Pandemic: Systematic Review

BACKGROUND

During the COVID-19 pandemic, the provision of and access to health care have been uniquely challenging, particularly during lockdowns or when dealing with COVID-19 cases. Health care professionals have had to provide patients with the necessary health care. However, delivering health care services while reducing face-to-face interaction puts an immense strain on health systems that are already overburdened. Against this backdrop, it is now more critical than ever to ensure the accessibility of health care services. Such access has been made increasingly available through mobile health (mHealth) apps. These apps have the potential to significantly improve health care outcomes and expectations and address some of the challenges confronting health care systems worldwide. Despite the advantages of mHealth, its acceptance and adoption remain low. Hence, health care organizations must consider the perceptions and opinions of physicians if the technology is to be successfully implemented.

OBJECTIVE

The objective of this systematic review was to explore and synthesize the scientific literature on the factors influencing the acceptance and adoption of mHealth among physicians during the COVID-19 pandemic.

METHODS

A systematic review of the studies published between March 2020 and December 2022 was conducted using the MEDLINE, Scopus, Embase, and ProQuest databases. The database search yielded an initial sample of 455 potential publications for analysis, of which 9 (2%) met the inclusion criteria. The methodology of this review was based on PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses).

RESULTS

The factors influencing mHealth acceptance and adoption by physicians were divided into perceived barriers and perceived facilitators, which were further grouped into the following 3 major thematic categories: technological, individual, and organizational barriers and facilitators, respectively. The technological barriers were accessibility, technical issues, usefulness, and data management; individual barriers were perceived patient barriers, time and workload pressure, technical literacy, knowledge of mHealth, and peer support; and organizational barriers were financial factors, management support and engagement, data security, telemonitoring policy, and collaboration. The technological facilitators of uptake were technical factors, clinical usefulness, and data management; individual facilitators were patient-related care, intrinsic motivation, collaboration, and data sharing (individual); and organizational facilitators were workflow-related determinants, organizational financial support, recommendation of mHealth services, and evidence-based guidelines.

CONCLUSIONS

This review summarized the evidence on the factors influencing mHealth acceptance and adoption by physicians during the COVID-19 pandemic. The main findings highlighted the importance of addressing organizational readiness to support physicians with adequate resources, shifting the focus from technological to patient-centered factors, and the seamless integration of mHealth into routine practice during and beyond the pandemic.

TRIAL REGISTRATION

PROSPERO CRD42022356125; https://tinyurl.com/2mmhn5yu.

Application possibilities of artificial intelligence in facial vascularized composite allotransplantation-a narrative review

Facial vascularized composite allotransplantation (FVCA) is an emerging field of reconstructive surgery that represents a dogmatic shift in the surgical treatment of patients with severe facial disfigurements. While conventional reconstructive strategies were previously considered the goldstandard for patients with devastating facial trauma, FVCA has demonstrated promising short- and long-term outcomes. Yet, there remain several obstacles that complicate the integration of FVCA procedures into the standard workflow for facial trauma patients. Artificial intelligence (AI) has been shown to provide targeted and resource-effective solutions for persisting clinical challenges in various specialties. However, there is a paucity of studies elucidating the combination of FVCA and AI to overcome such hurdles. Here, we delineate the application possibilities of AI in the field of FVCA and discuss the use of AI technology for FVCA outcome simulation, diagnosis and prediction of rejection episodes, and malignancy screening. This line of research may serve as a fundament for future studies linking these two revolutionary biotechnologies.

Empowering Patients and Transforming Healthcare in the Post-COVID-19 Era: The Role of Digital and Wearable Technologies

The COVID-19 pandemic has dramatically impacted the global healthcare system, revealing critical gaps in our capacity to provide efficient and effective care to patients, particularly those with chronic diseases [...].

mHealth and COVID-19: A Bibliometric Study

mHealth, i.e., using mobile computing and communication technologies in health care, has played an increasingly important role in the provision of medical care and undertaking self-health monitoring and management in the past two decades. Specifically, it becomes critically important for health care delivery when governments have been forced to impose quarantines and lockdowns during the spikes in COVID-19 cases. Therefore, this research focuses on academic publications including journal articles, reviews, and conference papers on the use of mHealth during the COVID-19 pandemic. Using a keyword search on "mHealth" (or "mobile health") and "COVID-19" on 7 January 2023 in Scopus, it was found that 1125 documents were officially published between 2020 and 2022. Among these 1125 documents, 1042 documents were journal articles, reviews, and conference papers. Researchers in the US produced 335 articles, followed by UK researchers with 119 articles, and Chinese researchers with 79 articles. Researchers affiliated with Harvard Medical School published the largest number of articles (31), followed by researchers of University College London with 21 articles and Massachusetts General Hospital with 20 articles. Co-occurrence of keywords analysis revealed four clusters, namely "COVID-19, mHealth, mobile applications, and public health", "adult, adolescent, mental health, and major clinical study", "human, pandemic, and epidemiology", and "telemedicine, telehealth, and health care delivery". Implications of this study are given.

Patient's and Practionner's Experiences of a First Face-to-Face vs. Remote Orthodontic Consultation: A Randomized Controlled Trial

(1) Aim: The purpose of this study was to assess patients' and practitioners' reported experience measures (PREMs) following a first standard orthodontic consultation (face-to-face consultation) versus a first orthodontic teleconsultation (video-assisted remote orthodontic consultation).; (2) Materials and Methods: This study was designed as a randomized controlled trial in which 60 patients were randomly allocated to two groups. In the control group, patients received a first face-to-face consultation (n = 30) whereas, in the test group, patients received a first orthodontic teleconsultation (n = 30). Patients as well as the orthodontic practitioners completed questionnaires after the experience. (3) Results: From the patients' point of view, overall satisfaction was comparable between the control group and the test group (p = 0.23). Quality of communication with the clinician, understanding of the explanations provided and a sense of privacy were also comparable between the two groups. However, from the practitioners' perspective, overall satisfaction after the face-to-face consultation was significantly higher than after the first remote consultation (p < 0.01). (4) Conclusions: In the context of a first orthodontic consultation, teleorthodontics appears to be an interesting and complementary approach to a classical face-to-face appointment, but which should by no means become systematic.

Are Health Information Systems Ready for the Digital Transformation in Portugal? Challenges and Future Perspectives

PURPOSE

This study aimed to reflect on the challenges of Health Information Systems in Portugal at a time when technologies enable the creation of new approaches and models for care provision, as well as to identify scenarios that may characterize this practice in the future.

DESIGN/METHODOLOGY/APPROACH

A guiding research model was created based on an empirical study that was conducted using a qualitative method that integrated content analysis of strategic documents and semi-structured interviews with a sample of fourteen key actors in the health sector.

FINDINGS

Results pointed to the existence of emerging technologies that may promote the development of Health Information Systems oriented to "health and well-being" in a preventive model logic and reinforce the social and management implications.

ORIGINALITY/VALUE

The originality of this work resided in the empirical study carried out, which allowed us to analyze how the various actors look at the present and the future of Health Information Systems. There is also a lack of studies addressing this subject.

RESEARCH LIMITATIONS/IMPLICATIONS

The main limitations resulted from a low, although representative, number of interviews and the fact that the interviews took place before the pandemic, so the digital transformation that was promoted was not reflected. Managerial implications and social implications: The study highlighted the need for greater commitment from decision makers, managers, healthcare providers, and citizens toward achieving improved digital literacy and health. Decision makers and managers must also agree on strategies to accelerate existing strategic plans and avoid their implementation at different paces.

A study on next-generation digital tool for health data management: the e-Pulse portal

Purpose

This study aims to investigate the working way of the e-Pulse portal in Türkiye as a sample of a next-generation digital tool for health data management. Accordingly, this study focuses on explaining the structure and key services of the e-Pulse portal in the context of health data management.

Design/methodology/approach

This study is a technical paper that will explain how the e-Pulse portal works in Türkiye. Accordingly, the data are based on secondary sources and mostly the official website of the e-Pulse portal. As a sample case, this study investigates the e-Pulse portal from Türkiye. The data are categorized by tables, and some key factors are classified based on review results.

Findings

As a result of the review of the e-Pulse portal's sample account, it is seen that the e-Pulse portal provides comprehensive data for personal health data for both individuals and healthcare professionals. By permitting healthcare professionals, users or patients can share their personal health data on specific dates and numbers whenever they need and want. When sharing recorded personal health data, citizens or patients can get more efficient healthcare service on the time.

Research limitations/implications

By giving descriptive evidence and review through the e-Pulse portal, countries with high-populated can see the key e-services and elements to manage health data through digital tools. On the other side, this study has some limitations. This study investigated the e-Pulse portal and its e-services for Türkiye and gave some findings mostly based on subjective deduction. Another digital portal can give different findings for the literature.

Practical implications

Based on the e-Pulse portal case, it is determined that by creating a digital portal with recorded personal up-to-date health data, healthcare services can be ensured more efficiently among high-populated countries in the long term. While population growth and pandemic possibilities such as COVID-19 increase throughout the world, serving more patients with these portals will increase efficiency and service quality, provided that patient information is well protected.

Originality/value

This study reveals key e-services and segments to provide personal health data management by a next-generation digital tool based on the e-Pulse portal. The main contribution of this study is expected to guide other countries when adapting next-generation technology or systems to manage health data in the future.

Impact of the development of information society on healthcare efficiency: Empirical evidence from 31 Chinese provinces

The development of information society has deeply changed the style of the healthcare service delivery and medical information access. This study aims to investigate how the information society development affects the efficiency of healthcare system in China, and explore provincial disparities in the impact. Based on the two-stage Data Envelopment Analysis (DEA) framework, this paper uses a panel data from 31 Chinese provinces from 2014 to 2017 to estimate efficiency of healthcare service and analyze the influence of the development of information society on efficiency. With the information society index (ISI) increased from 0.423 to 0.488 during the sample period, the healthcare efficiency experienced a slight decrease from 0.892 to 0.869. Moreover, the pure technical efficiency (PTE) is much lower than the scale efficiency (SE), and thus is the key to enhance the overall technical efficiency (TE). The time-fixed Tobit regression analysis suggests that information society development leads to a significant increase in PTE, but results in a decrease in SE, and therefore has little effect on TE. Further analysis reveals that the impact differs sharply between low-efficiency and high-efficiency provinces. For the low-efficiency provinces, the TE increases significantly with the development of the information society, mainly due to a considerable increase in PTE. In contrast, the TE decreases for the high-efficiency provinces, mainly caused by a decrease in SE. This paper highlights the importance of information infrastructure investment in healthcare system and the application of emerging information technologies to breakout the time and space boundaries of healthcare services in improving overall efficiency. In inefficient provinces, it is also necessary to properly control the growth of healthcare inputs.

Application of big data and artificial intelligence in epidemic surveillance and containment

Faced with the current time-sensitive COVID-19 pandemic, the overburdened healthcare systems have resulted in a strong demand to develop newer methods to control the spread of the pandemic. Big data and artificial intelligence (AI) have been leveraged amid the COVID-19 pandemic; however, little is known about their use for supporting public health efforts. In epidemic surveillance and containment, efforts are needed to treat critical patients, track and manage the health status of residents, isolate suspected cases, and develop vaccines and antiviral drugs. The applications of emerging practices of artificial intelligence and big data have become powerful "weapons" to fight against the pandemic and provide strong support in pandemic prevention and control, such as early warning, analysis and judgment, interruption and intervention of epidemic, to achieve goals of early detection, early report, early diagnosis, early isolation and early treatment. These are the decisive factors to control the spread of the epidemic and reduce the mortality. This paper systematically summarized the application of big data and AI in epidemic, and describes practical cases and challenges with emphasis on epidemic prevention and control. The included studies showed that big data and AI have the potential strength to fight against COVID-19. However, many of the proposed methods are not yet widely accepted. Thus, the most rewarding research would be on methods that promise value beyond COVID-19. More efforts are needed for developing standardized reporting protocols or guidelines for practice.

Telerehabilitation in the Transitional Care of Patients with Sequelae Associated with COVID-19: Perception of Portuguese Nurses

The COVID-19 pandemic brought many changes and challenges to health professionals, due to a lack of knowledge when dealing with the disease, fear of contagion, and the sequelae that characterize long COVID. To deal with this situation, respiratory rehabilitation programs are recommended in face-to-face and/or telerehabilitation modalities. (1) Background: This study had as its primary aim identifying the aspects/components to be considered in the planning and implementation of telerehabilitation interventions that guarantee transitional care for people with long COVID-19 after hospitalization and as a secondary aim identifying the positive aspects of telerehabilitation. (2) Methods: The method used to answer the research question was a focus group, carried out online with eight nurses specialized in rehabilitation nursing. The answers to the semi-structured interview were subjected to content analysis, and qualitative data analysis software (WebQDA^®) was used to organize and analyze the findings. (3) Results: Four categories emerged from the content analysis: coordination between care levels; transitional care telerehabilitation intervention; advantages of telerehabilitation; and opportunities. (4) Conclusions: These findings make an important contribution to the reorganization of transitional care, allowing the identification of central aspects to be considered in the planning and implementation of telerehabilitation programs for people with long COVID.

Scoping Review on the Multimodal Classification of Depression and Experimental Study on Existing Multimodal Models

Depression is a prevalent comorbidity in patients with severe physical disorders, such as cancer, stroke, and coronary diseases. Although it can significantly impact the course of the primary disease, the signs of depression are often underestimated and overlooked. The aim of this paper was to review algorithms for the automatic, uniform, and multimodal classification of signs of depression from human conversations and to evaluate their accuracy. For the scoping review, the PRISMA guidelines for scoping reviews were followed. In the scoping review, the search yielded 1095 papers, out of which 20 papers (8.26%) included more than two modalities, and 3 of those papers provided codes. Within the scope of this review, supported vector machine (SVM), random forest (RF), and long short-term memory network (LSTM; with gated and non-gated recurrent units) models, as well as different combinations of features, were identified as the most widely researched techniques. We tested the models using the DAIC-WOZ dataset (original training dataset) and using the SymptomMedia dataset to further assess their reliability and dependency on the nature of the training datasets. The best performance was obtained by the LSTM with gated recurrent units (F1-score of 0.64 for the DAIC-WOZ dataset). However, with a drop to an F1-score of 0.56 for the SymptomMedia dataset, the method also appears to be the most data-dependent.

Deep Learning Approaches in Histopathology

The revolution of artificial intelligence and its impacts on our daily life has led to tremendous interest in the field and its related subtypes: machine learning and deep learning. Scientists and developers have designed machine learning- and deep learning-based algorithms to perform various tasks related to tumor pathologies, such as tumor detection, classification, grading with variant stages, diagnostic forecasting, recognition of pathological attributes, pathogenesis, and genomic mutations. Pathologists are interested in artificial intelligence to improve the diagnosis precision impartiality and to minimize the workload combined with the time consumed, which affects the accuracy of the decision taken. Regrettably, there are already certain obstacles to overcome connected to artificial intelligence deployments, such as the applicability and validation of algorithms and computational technologies, in addition to the ability to train pathologists and doctors to use these machines and their willingness to accept the results. This review paper provides a survey of how machine learning and deep learning methods could be implemented into health care providers' routine tasks and the obstacles and opportunities for artificial intelligence application in tumor morphology.

Clinical presentation, management, screening and surveillance for colorectal cancer during the COVID-19 pandemic

Management of colorectal cancer (CRC) was severely affected by the changes implemented during the pandemic, and this resulted in delayed elective presentation, increased emergency presentation, reduced screening and delayed definitive therapy. This review was conducted to analyze the impact of the coronavirus disease 2019 (COVID-19) pandemic on management of CRC and to identify the changes made in order to adapt to the pandemic. We performed a literature search in PubMed, Medline, Index Medicus, EMBASE, SCOPUS, Reference Citation Analysis (https://www.referencecitationanalysis.com/) and Google Scholar using the following keywords in various combinations: Colorectal cancer, elective surgery, emergency surgery, stage upgrading, screening, surveillance and the COVID-19 pandemic. Only studies published in English were included. To curtail the spread of COVID-19 infection, there were modifications made in the management of CRC. Screening was limited to high risk individuals, and the screening tests of choice during the pandemic were fecal occult blood test, fecal immunochemical test and stool DNA testing. The use of capsule colonoscopy and open access colonoscopy was also encouraged. Blood-based tests like serum methylated septin 9 were also encouraged for screening of CRC during the pandemic. The presentation of CRC was also affected by the pandemic with more patients presenting with emergencies like obstruction and perforation. Stage migration was also observed during the pandemic with more patients presenting with more advanced tumors. The operative therapy of CRC was altered by the pandemic as more emergencies surgeries were done, which may require exteriorization by stoma. This was to reduce the morbidity associated with anastomosis and encourage early discharge from the hospital. There was also an initial reduction in laparoscopic surgical procedures due to the fear of aerosols and COVID-19 infection. As we gradually come out of the pandemic, we should remember the lessons learned and continue to apply them even after the pandemic passes.

The role transition of radiotherapy for the treatment of liver cancer in the COVID-19 era

The uncontrollable COVID-19 crises in the SARS-CoV-2 high-prevalence areas have greatly disrupted the routine treatment of liver cancer and triggered a role transformation of radiotherapy for liver cancer. The weight of radiotherapy in the treatment algorithm for liver cancer has been enlarged by the COVID-19 pandemic, which is helpful for the optimal risk-benefit profile.

A Review of Converging Technologies in eHealth Pertaining to Artificial Intelligence

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.

Attitudes toward Receiving COVID-19 Booster Dose in the Middle East and North Africa (MENA) Region: A Cross-Sectional Study of 3041 Fully Vaccinated Participants

COVID-19 vaccines are crucial to control the pandemic and avoid COVID-19 severe infections. The rapid evolution of COVID-19 variants such as B.1.1.529 is alarming, especially with the gradual decrease in serum antibody levels in vaccinated individuals. Middle Eastern countries were less likely to accept the initial doses of vaccines. This study was directed to determine COVID-19 vaccine booster acceptance and its associated factors in the general population in the MENA region to attain public herd immunity. We conducted an online survey in five countries (Egypt, Iraq, Palestine, Saudi Arabia, and Sudan) in November and December 2021. The questionnaire included self-reported information about the vaccine type, side effects, fear level, and several demographic factors. Kruskal−Wallis ANOVA was used to associate the fear level with the type of COVID-19 vaccine. Logistic regression was performed to confirm the results and reported as odds ratios (ORs) and 95% confidence intervals. The final analysis included 3041 fully vaccinated participants. Overall, 60.2% of the respondents reported willingness to receive the COVID-19 booster dose, while 20.4% were hesitant. Safety uncertainties and opinions that the booster dose is not necessary were the primary reasons for refusing the booster dose. The willingness to receive the booster dose was in a triangular relationship with the side effects of first and second doses and the fear (p < 0.0001). Females, individuals with normal body mass index, history of COVID-19 infection, and influenza-unvaccinated individuals were significantly associated with declining the booster dose. Higher fear levels were observed in females, rural citizens, and chronic and immunosuppressed patients. Our results suggest that vaccine hesitancy and fear in several highlighted groups continue to be challenges for healthcare providers, necessitating public health intervention, prioritizing the need for targeted awareness campaigns, and facilitating the spread of evidence-based scientific communication.

Analysis of mHealth research: mapping the relationship between mobile apps technology and healthcare during COVID-19 outbreak

BACKGROUND

Mobile health applications (mHealth apps) offer enormous promise for illness monitoring and treatment to improve the provided medical care and promote health and wellbeing.

OBJECTIVE

We applied bibliometric quantitative analysis and network visualization to highlight research trends and areas of particular interest. We expect by summarizing the trends in mHealth app research, our work will serve as a roadmap for future investigations.

METHODS

Relevant English publications were extracted from the Scopus database. VOSviewer (version 1.6.17) was used to build coauthorship networks of authors, countries, and the co-occurrence networks of author keywords.

RESULTS

We analyzed 550 published articles on mHealth apps from 2020 to February 1, 2021. The yearly publications increased from 130 to 390 in 2021. JMIR mHealth and uHealth (33/550, 6.0%), J. Med. Internet Res. (27/550, 4.9%), JMIR Res. Protoc. (22/550, 4.0%) were the widest journals for these publications. The United States has the largest number of publications (143/550, 26.0%), and England ranks second (96/550, 17.5%). The top three productive authors were: Giansanti D., Samuel G., Lucivero F., and Zhang L. Frequent authors' keywords have formed major 4 clusters representing the hot topics in the field: (1) artificial intelligence and telehealthcare; (2) digital contact tracing apps, privacy and security concerns; (3) mHealth apps and mental health; (4) mHealth apps in public health and health promotion.

CONCLUSIONS

mHealth apps undergo current developments, and they remain hot topics in COVID-19. These findings might be useful in determining future perspectives to improve infectious disease control and present innovative solutions for healthcare.

6G to Take the Digital Divide by Storm: Key Technologies and Trends to Bridge the Gap

The pandemic caused by COVID-19 has shed light on the urgency of bridging the digital divide to guarantee equity in the fruition of different services by all citizens. The inability to access the digital world may be due to a lack of network infrastructure, which we refer to as service-delivery divide, or to the physical conditions, handicaps, age, or digital illiteracy of the citizens, that is mentioned as service-fruition divide. In this paper, we discuss the way how future sixth-generation (6G) systems can remedy actual limitations in the realization of a truly digital world. Hence, we introduce the key technologies for bridging the digital gap and show how they can work in two use cases of particular importance, namely eHealth and education, where digital inequalities have been dramatically augmented by the pandemic. Finally, considerations about the socio-economical impacts of future 6G solutions are drawn.

Singular Spectrum Analysis of Tremorograms for Human Neuromotor Reaction Estimation

Singular spectrum analysis (SSA) is a method of time series analysis and is used in various fields, including medicine. A tremorogram is a biological signal that allows evaluation of a person’s neuromotor reactions in order to infer the state of the motor parts of the central nervous system (CNS). A tremorogram has a complex structure, and its analysis requires the use of advanced methods of signal processing and intelligent analysis. The paper’s novelty lies in the application of the SSA method to extract diagnostically significant features from tremorograms with subsequent evaluation of the state of the motor parts of the CNS. The article presents the application of a method of singular spectrum decomposition, comparison of known variants of classification, and grouping of principal components for determining the components of the tremorogram corresponding to the trend, periodic components, and noise. After analyzing the results of the SSA of tremorograms, we proposed a new algorithm of grouping based on the analysis of singular values of the trajectory matrix. An example of applying the SSA method to the analysis of tremorograms is shown. Comparison of known clustering methods and the proposed algorithm showed that there is a reasonable correspondence between the proposed algorithm and the traditional methods of classification and pairing in the set of periodic components.

The Form in Formal Thought Disorder: A Model of Dyssyntax in Semantic Networking

Formal thought disorder (FTD) is a clinical mental condition that is typically diagnosable by the speech productions of patients. However, this has been a vexing condition for the clinical community, as it is not at all easy to determine what “formal” means in the plethora of symptoms exhibited. We present a logic-based model for the syntax–semantics interface in semantic networking that can not only explain, but also diagnose, FTD. Our model is based on description logic (DL), which is well known for its adequacy to model terminological knowledge. More specifically, we show how faulty logical form as defined in DL-based Conception Language (CL) impacts the semantic content of linguistic productions that are characteristic of FTD. We accordingly call this the dyssyntax model.