Background: Recently, Coronavirus Disease 2019 (COVID-19), caused by severe acute respiratory syndrome virus 2 (SARS-CoV-2), has affected more than 200 countries and lead to enormous losses. This study systematically reviews the application of Artificial Intelligence (AI) techniques in COVID-19, especially for diagnosis, estimation of epidemic trends, prognosis, and exploration of effective and safe drugs and vaccines; and discusses the potential limitations.Methods: We report this systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched PubMed, Embase and the Cochrane Library from inception to 19 September 2020 for published studies of AI applications in COVID-19. We used PROBAST (prediction model risk of bias assessment tool) to assess the quality of literature related to the diagnosis and prognosis of COVID-19. We registered the protocol (PROSPERO CRD42020211555).Results: We included 78 studies: 46 articles discussed AI-assisted diagnosis for COVID-19 with total accuracy of 70.00 to 99.92%, sensitivity of 73.00 to 100.00%, specificity of 25 to 100.00%, and area under the curve of 0.732 to 1.000. Fourteen articles evaluated prognosis based on clinical characteristics at hospital admission, such as clinical, laboratory and radiological characteristics, reaching accuracy of 74.4 to 95.20%, sensitivity of 72.8 to 98.00%, specificity of 55 to 96.87% and AUC of 0.66 to 0.997 in predicting critical COVID-19. Nine articles used AI models to predict the epidemic of the COVID-19, such as epidemic peak, infection rate, number of infected cases, transmission laws, and development trend. Eight articles used AI to explore potential effective drugs, primarily through drug repurposing and drug development. Finally, 1 article predicted vaccine targets that have the potential to develop COVID-19 vaccines.Conclusions: In this review, we have shown that AI achieved high performance in diagnosis, prognosis evaluation, epidemic prediction and drug discovery for COVID-19. AI has the potential to enhance significantly existing medical and healthcare system efficiency during the COVID-19 pandemic.

Objective: This study aimed to review the research status and to demonstrate the hot spots and frontiers of epilepsy and circadian rhythm via CiteSpace. Method: We searched Web of Science (WoS) for studies related to epilepsy and circadian rhythm from inception to 2020. CiteSpace was used to generate network maps about the collaborations between authors, countries, and institutions and reveal hot spots and frontiers of epilepsy and circadian rhythm. Results: A total of 704 studies related to epilepsy and circadian rhythm from the WoS were retrieved. Sanchez-Vazquez FJ was the most prolific author (17 articles). The USA and University of Murcia were the leading country and institution in this field with 219 and 22 publications, respectively. There were active collaborations among the authors, countries, and institutions. Hot topics focused on the interaction between epilepsy and circadian rhythm, as well as possible novel treatments. Conclusions: Based on the results of CiteSpace, the current study suggested active cooperation between authors, countries, and institutions. Major ongoing research trends include the circadian rhythm of epilepsy based on different epileptic focus and the interaction between epilepsy and circadian rhythm, especially through melatonin, sleep–wake cycles, and clock genes, which may implicate possible treatments (such as chronotherapy, neural stem cells transplantation) for epilepsy in the future.

Background: The modified Dietary Approaches to Stop Hypertension (DASH) diet was a potentially effective treatment for pre-hypertensive and hypertensive patients. The evidence for the effect of the modified DASH diet on blood pressure reduction was inconsistent. The study was designed to assess the effects of the modified DASH diet on blood pressure (BP) in hypertensive and pre-hypertensive adults.Methods: We searched Medline, Embase, CENTRAL, CNKI, VIP, Wanfang Data, SINOMED, Google Scholar, the World Health Organization's International Clinical Trials Registry Platform, and Clinicaltrials.gov from inception to July 1st, 2021. Randomized controlled trials (RCTs) assessing the effects of the modified DASH diet on systolic and diastolic BP, cardiovascular risk factors (body weight, body mass index, waist circumference, fasting glucose, blood lipids), cardiovascular events, and all-cause mortality were included. Statistical analysis was performed using Stata software. Risk of bias was assessed with the Cochrane tool and quality of evidence with GRADE.Results: A total of 10 RCTs were included. Compared with control diet, the modified DASH diet could reduce mean systolic (−3.26 mmHg; 95% confidence interval −5.58, −0.94 mmHg; P = 0.006) and diastolic (−2.07 mmHg; 95% confidence interval −3.68, −0.46 mmHg; P = 0.01) BP. Compared with the controlling diet, the modified DASH diet could reduce systolic BP to a greater extent in trials with a mean baseline BP ≥ 140/90 mmHg compared with <140/90 mmHg. Diastolic BP reduction was greater when the mean body mass index was ≥30 kg/m2 than 8 weeks compared with ≤8 weeks. The modified DASH diet could affect mean waist circumference (difference: 1.57 cm; 95% confidence interval −2.98, −0.15) and triglyceride concentration (difference: 1.04 mol/L; 95% confidence interval −1.47, −0.60).Conclusions: The modified DASH diet can reduce BP, waist circumference, and triglyceride concentration in hypertension patients. A higher baseline BP is associated with more marked systolic and diastolic BP reduction.Systematic Review Registration: PROSPERO registration number: CRD42020190860.

Background: The application of artificial intelligence (AI) in the delivery of health care is a promising area, and guidelines, consensus statements, and standards on AI regarding various topics have been developed. Objective: We performed this study to assess the quality of guidelines, consensus statements, and standards in the field of AI for medicine and to provide a foundation for recommendations about the future development of AI guidelines. Methods: We searched 7 electronic databases from database establishment to April 6, 2022, and screened articles involving AI guidelines, consensus statements, and standards for eligibility. The AGREE II (Appraisal of Guidelines for Research & Evaluation II) and RIGHT (Reporting Items for Practice Guidelines in Healthcare) tools were used to assess the methodological and reporting quality of the included articles. Results: This systematic review included 19 guideline articles, 14 consensus statement articles, and 3 standard articles published between 2019 and 2022. Their content involved disease screening, diagnosis, and treatment; AI intervention trial reporting; AI imaging development and collaboration; AI data application; and AI ethics governance and applications. Our quality assessment revealed that the average overall AGREE II score was 4.0 (range 2.2-5.5; 7-point Likert scale) and the mean overall reporting rate of the RIGHT tool was 49.4% (range 25.7%-77.1%). Conclusions: The results indicated important differences in the quality of different AI guidelines, consensus statements, and standards. We made recommendations for improving their methodological and reporting quality. Trial Registration: PROSPERO International Prospective Register of Systematic Reviews (CRD42022321360); https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=321360