Introduction: There is no recent consensus on the effectiveness of teledentistry versus in-person examination in the diagnosis of dental caries, especially after the COVID-19 pandemic. Objective: To assess the diagnostic accuracy of teledentistry versus in-person examination for dental caries diagnosis (PROSPERO #CRD42023410962). Methods: This systematic review and meta-analysis compared the effectiveness of teledentistry versus in-person examination for dental caries diagnosis. The eligibility criteria were peer-reviewed studies published in English between January 2013 and December 2021 that reported diagnostic parameters (specificity and sensitivity) for caries detection in primary and permanent dentition. Articles were extracted using search strategies from PubMed and CINAHL databases and screened using PRISMA-DTA guidelines, following a review for quality assessment and risk of bias using the QUADAS-2 and JBI Critical Appraisal Checklists. Meta-analysis was conducted in R using the MADA package. A descriptive analysis of the sensitivity, specificity, diagnostic odds ratio, and confidence intervals was performed with respective forest plots. Heterogeneity was assessed using Cochrane and Higgins's 2 tests. Univariate measures of diagnostic accuracy were performed based on the DerSimonian-Laird random effect and reported summary diagnostic odds ratios. Results: Twelve studies met the inclusion criteria and were reviewed and included in the meta-analysis. The diagnostic parameters ranged from 45.6% to 88.3% for sensitivity, 55.2% to 98.3% for specificity, 79% to 92% for positive predictive value, 48% to 97% for negative predictive value, and 70% to 96% for accuracy. The κ scores ranged from 0.46 to 0.89 for teledentistry modalities. Tests for equality of sensitivities and specificities were significant ( < 0.001). The studies were not heterogeneous with Cochran's : 14.502 ( = 0.206) and Higgins's 2 of 24%. The multivariable analysis showed a diagnostic odds ratio based on the DerSimonian-Laird random effect of 35.14, which indicates that the odds of caries detection via teledentistry is 35 times more true positive (i.e., correctly identifying a positive condition) than false positive. Conclusions: Diagnosis of caries via teledentistry is effective and comparable to in-person diagnosis. Remote assessments are consistent in diagnostic accuracy for caries.Knowledge Transfer Statement:This systematic review and meta-analysis added to the evidence about using teledentistry assessment as a diagnostically accurate tool to detect dental caries. Using teledentistry dental practices could promote greater access to dental and oral health care in the absence of in-person assessment.

Ethnopharmacological relevance: Tanreqing injection (TRQI) is an intravenous herbal preparation derived from 5 types of traditional Chinese medicines including Scutellariae Radix, Lonicerae Japonicae Flos, Forsythiae Fructus, bear bile powder and goral horn, incorporating baicalin, chlorogenic acid, ursodeoxycholic acid, and goose deoxycholic acid and other compounds known for anti-inflammatory properties, is widely used in China to treat cough caused by acute trachea-bronchitis disease (ATB). Aim of the study: To investigate the clinical efficacy and safety of Tanreqing injection (TRQI) with and without Western medicine (WM) for cough caused by acute trachea-bronchitis (ATB). Materials and methods: We systematically searched eight databases, including CENTRAL, Embase, PubMed, Science Direct, Wiley, China National Knowledge Infrastructure, Chinese Biomedical Literature Database and WanFang, from inception to August 2023 for randomized clinical trials (RCTs) on TRQI for cough caused by ATB. The critical outcomes of interest were time to symptom disappearance, including time for cough symptom to disappear and time to improve cough and sputum production. Important outcomes included symptom disappearance rate, adverse events (AEs) and lung function. We carried out random-effects meta-analysis using Review Manager 5.4 and assessed the certainty of evidence utilizing the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. Results: A total of 2872 citations were identified by our search, of which 26 eligible RCTs enrolled 2731 participants. Low to moderate certainty evidence showed that when compared with WM, TRQI plus WM treatment was associated with a favorable effect on the time for cough symptom to disappear (MD-2.21 d, 95% CI-2.64 to-1.78), time to improve cough and sputum production (MD-0.68 d, 95% CI-0.83 to-0.53), symptom disappearance rate (RR 1.37, 95% CI 1.20 to 1.55), forced vital capacity, and forced expiratory volume in 1 s (MD 0.38 L, 95% CI 0.26 to 0.50; MD 2.92%, 95% CI 1.29 to 4.56, respectively). In terms of AEs, there was no association between TRQI plus WM and WM (RR 0.55, 95% CI 0.14 to 2.21; low-certainty evidence). Very low certainty evidence showed that TRQI alone was associated with reduced time to improve cough and sputum (MD-0.14 d, 95% CI-0.26 to-0.02) and increased symptom disappearance rate (RR 1.89, 95% CI 1.24 to 2.88; low certainty evidence) compared to WM.

Numerous factors contribute to the low adherence to lung cancer screening (LCS) programs. A theory-informed approach to identifying the obstacles and facilitators to LCS uptake is required. This study aimed to identify, assess, and synthesize the available literature at the individual and healthcare provider (HCP) levels based on a social-ecological model and identify gaps to improve practice and policy decision-making. Systematic searches were conducted in nine electronic databases from inception to December 31, 2020. We also searched Google Scholar and manually examined the reference lists of systematic reviews to include relevant articles. Primary studies were scored for quality assessment. Among 3938 potentially relevant articles, 36 studies, including 25 quantitative and 11 qualitative studies, were identified for inclusion in the review. Fifteen common factors were extracted from 34 studies, including nine barriers and six facilitators. The barriers included individual factors (n = 5), health system factors (n = 3), and social/environmental factors (n = 1). The facilitators included only individual factors (n = 6). However, two factors, age and screening harm, remain mixed. This systematic review identified and combined barriers and facilitators to LCS uptake at the individual and HCP levels. The interaction mechanisms among these factors should be further explored, which will allow the construction of tailored LCS recommendations or interventions for the Chinese context.

Background: Chatbot is a timely topic applied in various fields, including medicine and health care, for human-like knowledge transfer and communication. Machine learning, a subset of artificial intelligence, has been proven particularly applicable in health care, with the ability for complex dialog management and conversational flexibility. Objective: This review article aims to report on the recent advances and current trends in chatbot technology in medicine. A brief historical overview, along with the developmental progress and design characteristics, is first introduced. The focus will be on cancer therapy, with in-depth discussions and examples of diagnosis, treatment, monitoring, patient support, workflow efficiency, and health promotion. In addition, this paper will explore the limitations and areas of concern, highlighting ethical, moral, security, technical, and regulatory standards and evaluation issues to explain the hesitancy in implementation. Methods: A search of the literature published in the past 20 years was conducted using the IEEE Xplore, PubMed, Web of Science, Scopus, and OVID databases. The screening of chatbots was guided by the open-access Botlist directory for health care components and further divided according to the following criteria: diagnosis, treatment, monitoring, support, workflow, and health promotion. Results: Even after addressing these issues and establishing the safety or efficacy of chatbots, human elements in health care will not be replaceable. Therefore, chatbots have the potential to be integrated into clinical practice by working alongside health practitioners to reduce costs, refine workflow efficiencies, and improve patient outcomes. Other applications in pandemic support, global health, and education are yet to be fully explored. Conclusions: Further research and interdisciplinary collaboration could advance this technology to dramatically improve the quality of care for patients, rebalance the workload for clinicians, and revolutionize the practice of medicine

Backgrounds: Pulmonary tuberculosis (PTB) is a major health and economic burden. Accurate PTB detection is an important step to eliminating TB globally. Interferon gamma-induced protein 10 (IP-10) has been reported as a potential diagnostic marker for PTB since 2007. In this study, a meta-analysis approach was used to assess diagnostic value of IP-10 for PTB. Methods: Web of Science, PubMed, the Cochrane Library, and Embase databases were searched for studies published in English up to February 2019. The pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), the area under the curve (AUC) and hierarchical summary receiver operating characteristic (HSROC) curve were estimated by the HSROC model and random effect model. Results: Eighteen studies including 2836 total participants met our inclusion criteria. The pooled sensitivity, specificity, PLR, and NLR of IP-10 for PTB detection were 86, 88%, 7.00, and 0.16, respectively. The pooled DOR was 43.01, indicating a very powerful discriminatory ability of IP-10. The AUC was 0.93 (95% CI: 0.91-0.95), showed the accuracy of IP-10 was good. Meta-regression showed that there was no heterogeneity with respect to TB burden, study design type, age, IP-10 assay method, IP-10 condition and HIV-infection status. Conclusions: Our results showed that IP-10 is a promising marker for differentiating PTB from non-TB.

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.

Objective: The aim of this study was to determine the diagnostic accuracy of different computer-aided diagnostic (CAD) systems for thyroid nodules classification. Methods: A systematic search of the literature was conducted from inception until March, 2019 using the PubMed, EMBASE, Web of science, and Cochrane library. Literature selection and data extraction were conducted by 2 independent reviewers. Numerical values for sensitivity and specificity were obtained from false negative (FN), false positive (FP), true negative (TN), and true positive (TP) rates, presented alongside graphical representations with boxes marking the values and horizontal lines showing the confidence intervals (CIs). Summary receiver operating characteristic (SROC) curves were applied to assess the performance of diagnostic tests. Data were processed using Review Manager 5.3 and Stata 15. The methodological quality of included studies was assessed using Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Trial registration number: PROSPERO CRD42019132540