Background: Artificial intelligence (AI) has been extensively used in a range of medical fields to promote therapeutic development. The development of diverse AI techniques has also contributed to early detections, disease diagnoses, and referral management. However, concerns about the value of advanced AI in disease diagnosis have been raised by health care professionals, medical service providers, and health policy decision makers. Objective: This review aimed to systematically examine the literature, in particular, focusing on the performance comparison between advanced AI and human clinicians to provide an up-to-date summary regarding the extent of the application of AI to disease diagnoses. By doing so, this review discussed the relationship between the current advanced AI development and clinicians with respect to disease diagnosis and thus therapeutic development in the long run. Methods: We systematically searched articles published between January 2000 and March 2019 following the Preferred Reporting Items for Systematic reviews and Meta-Analysis in the following databases: Scopus, PubMed, CINAHL, Web of Science, and the Cochrane Library. According to the preset inclusion and exclusion criteria, only articles comparing the medical performance between advanced AI and human experts were considered. Results: A total of 9 articles were identified. A convolutional neural network was the commonly applied advanced AI technology. Owing to the variation in medical fields, there is a distinction between individual studies in terms of classification, labeling, training process, dataset size, and algorithm validation of AI. Performance indices reported in articles included diagnostic accuracy, weighted errors, false-positive rate, sensitivity, specificity, and the area under the receiver operating characteristic curve. The results showed that the performance of AI was at par with that of clinicians and exceeded that of clinicians with less experience. Conclusions: Current AI development has a diagnostic performance that is comparable with medical experts, especially in image recognition-related fields. Further studies can be extended to other types of medical imaging such as magnetic resonance imaging and other medical practices unrelated to images. With the continued development of AI-assisted technologies, the clinical implications underpinned by clinicians’ experience and guided by patient-centered health care principle should be constantly considered in future AI-related and other technology-based medical research.

Background: In December 2019, a few coronavirus disease (COVID-19) cases were first reported in Wuhan, Hubei, China. Soon after, increasing numbers of cases were detected in other parts of China, eventually leading to a disease outbreak in China. As this dreadful disease spreads rapidly, the mass media has been active in community education on COVID-19 by delivering health information about this novel coronavirus, such as its pathogenesis, spread, prevention, and containment. Objective: The aim of this study was to collect media reports on COVID-19 and investigate the patterns of media-directed health communications as well as the role of the media in this ongoing COVID-19 crisis in China. Methods: We adopted the WiseSearch database to extract related news articles about the coronavirus from major press media between January 1, 2020, and February 20, 2020. We then sorted and analyzed the data using Python software and Python package Jieba. We sought a suitable topic number with evidence of the coherence number. We operated latent Dirichlet allocation topic modeling with a suitable topic number and generated corresponding keywords and topic names. We then divided these topics into different themes by plotting them into a 2D plane via multidimensional scaling. Results: After removing duplications and irrelevant reports, our search identified 7791 relevant news reports. We listed the number of articles published per day. According to the coherence value, we chose 20 as the number of topics and generated the topics’ themes and keywords. These topics were categorized into nine main primary themes based on the topic visualization figure. The top three most popular themes were prevention and control procedures, medical treatment and research, and global or local social and economic influences, accounting for 32.57% (n=2538), 16.08% (n=1258), and 11.79% (n=919) of the collected reports, respectively. Conclusions: Topic modeling of news articles can produce useful information about the significance of mass media for early health communication. Comparing the number of articles for each day and the outbreak development, we noted that mass media news reports in China lagged behind the development of COVID-19. The major themes accounted for around half the content and tended to focus on the larger society rather than on individuals. The COVID-19 crisis has become a worldwide issue, and society has become concerned about donations and support as well as mental health among others. We recommend that future work addresses the mass media’s actual impact on readers during the COVID-19 crisis through sentiment analysis of news data.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Healthcare industry has entered the era of digital health. With the advancement in information technologies and mobile Internet, real-time monitoring and interventions are provided for management of high burden health problems such as cardiovascular diseases (CVDs). The impact of digital health interventions (DHIs) on cost-effective management of CVDs have been examined in literature using the decision analytic model-based health technology assessment (HTA) approach. Objective: The aim of this study was to perform a systematical review on decision analytic model-based studies evaluating the cost-effectiveness of DHIs on the management of CVDs. Methods: A literature research was conducted in Medline, Embase, CINAHL Complete, PsycINFO, Scopus, Web of Science, Center for Review and Dissemination, and Institute for IEEE Xplore, for paper published between 2001 and 2018. Studies were included if the following of criteria were met: (1) English articles, (2) DHIs that promoted clinical practitioners’ intervention and had impact on patients’ cardiovascular conditions, (3) studies that were modelling works with health economic outcomes of DHIs for CVDs, (4) studies that have a comparative group for assessment, and (5) full economic evaluations including cost-effectiveness analysis, cost-utility analysis, cost-benefit analysis, and cost-consequence analysis. Results: Fifteen studies met the defined criteria and were included in this systematic review. Heart failure and stroke were two frequent CVDs managed with DHIs. DHIs included telemedicine (telephone/mobile phone support, videoconferencing system and telestroke network) and wearable devices. The DHIs gained higher quality-adjusted life-years (QALYs) or improved clinical outcomes when compared with usual care in CVD management in all 15 studies. The DHIs were cost-saving in 5 studies, and incurred additional total cost in 10 studies. The cost-effectiveness of DHIs with higher total cost was subject to the willingness-to-pay threshold. Conclusions: This is the first systematic review of decision model-based cost-effectiveness analyses of DHIs on the management of CVDs. Findings from this review showed that there are few cost-effectiveness analyses of DHIs for CVDs in the literature. The DHIs improved QALYs gain or clinical outcomes of CVDs, and most of the DHIs incurred additional total cost when compared with usual care. Clinical Trial: N.A.

Mobile and tablet apps, ubiquitous and pervasive computing, wearable computing and domotics for health.

A novel coronavirus disease (COVID-19) outbreak due to the severe respiratory syndrome coronavirus (SARS-CoV-2) infection occurred in China in late December 2019. Facemask wearing with proper hand hygiene is considered an effective measure to prevent SARS-CoV-2 transmission, but facemask wearing has become a social concern due to the global facemask shortage. China is the major facemask producer in the world, contributing to 50% of global production. However, a universal facemask wearing policy would put an enormous burden on the facemask supply. We performed a policy review concerning facemasks using government websites and mathematical modelling shortage analyses based on data obtained from the National Health Commission (NHC), the Ministry of Industry and Information Technology (MIIT), the Centre for Disease Control and Prevention (CDC), and General Administration of Customs (GAC) of the People's Republic of China. Three scenarios with respect to wearing facemasks were considered: (1) a universal facemask wearing policy implementation in all regions of mainland China; (2) a universal facemask wearing policy implementation only in the epicentre (Hubei province, China); and (3) no implementation of a universal facemask wearing policy. Regardless of different universal facemask wearing policy scenarios, facemask shortage would occur but eventually end during our prediction period (from 20 Jan 2020 to 30 Jun 2020). The duration of the facemask shortage described in the scenarios of a country-wide universal facemask wearing policy, a universal facemask wearing policy in the epicentre, and no universal facemask wearing policy were 132, seven, and four days, respectively. During the prediction period, the largest daily facemask shortages were predicted to be 589·5, 49·3, and 37·5 million in each of the three scenarios, respectively. In any scenario, an N95 mask shortage was predicted to occur on 24 January 2020 with a daily facemask shortage of 2·2 million. Implementing a universal facemask wearing policy in the whole of China could lead to severe facemask shortage. Without effective public communication, a universal facemask wearing policy could result in societal panic and subsequently, increase the nationwide and worldwide demand for facemasks. These increased demands could cause a facemask shortage for healthcare workers and reduce the effectiveness of outbreak control in the affected regions, eventually leading to a pandemic. To fight novel infectious disease outbreaks, such as COVID-19, governments should monitor domestic facemask supplies and give priority to healthcare workers. The risk of asymptomatic transmission and facemask shortages should be carefully evaluated before introducing a universal facemask wearing policy in high-risk regions. Public health measures aimed at improving hand hygiene and effective public communication should be considered along with the facemask policy.

Background: Evidence for the relationship between maternal and perinatal factors and the success of vaginal birth after cesarean section (VBAC) is conflicting. We aimed to systematically analyze published data on maternal and fetal factors for successful VBAC. Methods: A comprehensive search of Medline, Embase, and the Cumulative Index to Nursing and Allied Health Literature, from each database’s inception to March 16, 2018. Observational studies, identifying women with a trial of labor after one previous low-transverse cesarean section were included. Two reviewers independently abstracted the data. Meta-analysis was performed using the random-effects model. Risk of bias was assessed by the Newcastle-Ottawa Scale. Results: We included 94 eligible observational studies (239,006 pregnant women with 163,502 VBAC). Factors were associated with successful VBAC with the following odds ratios (OR;95%CI): age (0.92;0.86–0.98), obesity (0.50;0.39–0.64), diabetes (0.50;0.42–0.60), hypertensive disorders complicating pregnancy (HDCP) (0.54;0.44–0.67), Bishop score (3.77;2.17–6.53), labor induction (0.58;0.50–0.67), macrosomia (0.56;0.50–0.64), white race (1.39;1.26–1.54), previous vaginal birth before cesarean section (3.14;2.62–3.77), previous VBAC (4.71;4.33–5.12), the indications for the previous cesarean section (cephalopelvic disproportion (0.54;0.36–0.80), dystocia or failure to progress (0.54;0.41–0.70), failed induction (0.56;0.37–0.85), and fetal malpresentation (1.66;1.38–2.01)). Adjusted ORs were similar. Conclusions: Diabetes, HDCP, Bishop score, labor induction, macrosomia, age, obesity, previous vaginal birth, and the indications for the previous CS should be considered as the factors affecting the success of VBAC.