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.

Background:While depression is a growing public health issue, the percentage of individuals with depression receiving treatment is low. Physical and social attributes of the neighborhood may influence the level of depressive symptoms and the prevalence of depression in older adults.Methods:This review systematically examined the literature on neighborhood environmental correlates of depression in older adults. Findings were analyzed according to three depression outcomes: depressive symptoms, possible depression, and clinical depression. Based on their description in the article, environmental variables were assigned to one of 25 categories. The strength of evidence was statistically quantified using a meta-analytical approach with articles weighted for sample size and study quality. Findings were summarized by the number of positive, negative, and statistically non-significant associations by each combination of environmental attribute – depression outcome and by combining all depression outcomes.Results:Seventy-three articles met the selection criteria. For all depression outcomes combined, 12 of the 25 environmental attribute categories were considered to be sufficiently studied. Three of these, neighborhood socio-economic status, collective efficacy, and personal/crime-related safety were negatively associated with all depression outcomes combined. Moderating effects on associations were sparsely investigated, with 52 articles not examining any. Attributes of the physical neighborhood environment have been understudied.Conclusion:This review provides support for the potential influence of some neighborhood attributes on population levels of depression. However, further research is needed to adequately examine physical attributes associated with depression and moderators of both social and physical neighborhood environment attribute – depression outcome associations.

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.

People across the world have been greatly affected by the ongoing coronavirus disease (COVID-19) pandemic. The high infection risk of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in hospitals is particularly problematic for recently delivered mothers and currently pregnant women who require professional antenatal care. Online antenatal care would be a preferable alternative for these women since it can provide pregnancy-related information and remote clinic consultations. In addition, online antenatal care may help to provide relatively economical medical services and diminish health care inequality due to its convenience and cost-effectiveness, especially in developing countries or regions. However, some pregnant women will doubt the reliability of such online information. Therefore, it is important to ensure the quality and safety of online services and establish a stable, mutual trust between the pregnant women, the obstetric care providers and the technology vis-a-vis the online programs. Here, we report how the COVID-19 pandemic brings not only opportunities for the development and popularization of online antenatal care programs but also challenges.

Gestational diabetes mellitus (GDM) is one of the most common complications during pregnancy, and it has both short- and long-term adverse effects on the health of mothers and fetuses. To investigate the effect of exercise during pregnancy on the occurrence of GDM among normal-weight pregnant women. We searched for studies published between January 1994 and June 2017 that appeared in the Web of Science, Scopus, ClinicalTrials.gov or Cochrane library databases. Randomized controlled trials that investigated the preventive effect of exercise on GDM in normal-weight women were included. Interventions including any confounding factors (e.g., dietary) were excluded. We extracted maternal characteristics, the diagnostic criteria of GDM, and basic information for intervention and obstetric outcomes. The primary outcome was the occurrence of GDM, and the secondary outcomes included gestational weight gain, gestational age at birth, birth weight, and the odds of cesarean section. A meta-analysis was conducted based on calculations of pooled estimates using the random-effects model. Eight studies were included in this systematic review and meta-analysis. Exercise during pregnancy was shown to decrease the occurrence of GDM [RR = 0.58, 95% CI (0.37, 0.90), P = 0.01 and RR = 0.60, 95% CI (0.36, 0.98), P = 0.04 based on different diagnosis criteria, respectively] in normal-weight women. Regarding secondary outcomes, exercise during pregnancy can decrease gestational weight gain [MD = − 1.61, 95% CI (− 1.99, − 1.22), P<0.01], and had no significant effects on gestational age at birth [MD = − 0.55, 95% CI (− 1.57, 0.47), P = 0.29], birth weight [MD = − 18.70, 95% CI (− 52.49, 15.08), P = 0.28], and the odds of caesarean section [RR = 0.88, 95% CI (0.72, 1.08), P = 0.21], respectively. Exercise during pregnancy can ostensibly decrease the occurrence of GDM without reducing gestational age at delivery and increasing the odds of cesarean section in normal-weight women.