Introduction: Despite common, serious, costly, and often fatal conditions affecting up to 50 % of older patients, delirium is often unrecognized and overlooked. We examine the accuracy of the 4AT for detecting older patients with delirium.Methods: We performed a systematic search of PubMed, Web of Science, PsycINFO, and EMBASE databases from inception to April 2020 and updated to January 2022. Four independently reviewers extracted study data and assessed the methodological quality using the revised quality assessment of diagnostic accuracy studies tool (QUADAS-2). Pooled estimates of sensitivity and specificity were generated using a bivariate random effects model. All statistical analyses were performed with STATA version 15.1 and Meta-DiSc version 1.4 software. Results: Eleven studies with 2789 participants were included. The pooled sensitivity and specificity were 0.87 (95 % CI: 0.81-0.91) and 0.87 (95 % CI: 0.79-0.92), respectively, and the positive and negative likelihood ratios were 6.66 (95 % CI: 4.12-10.74) and 0.15 (95 % CI: 0.10-0.23), respectively. Deeks' test indicated no significant publication bias (t = 0.83, P = 0.43). Univariable meta-regression showed that patient selection and flow and timing significantly influenced the pooled sensitivity (P < 0.05), settings significantly influenced the pooled specificity (P < 0.05).Conclusion: Our meta-analysis demonstrates that 4AT is a sensitive and specific screening tool for delirium in older patients. Its brevity and simplicity support its use in routine clinical practice, particularly in time-poor settings. Clinicians should come to a conclusion based largely on the 4AT findings in conjunction with clinical judgment.

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: 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.