Objectives Medical artificial intelligence (AI) has been used widely applied in clinical field due to its convenience and innovation. However, several policy and regulatory issues such as credibility, sharing of responsibility and ethics have raised concerns in the use of AI. It is therefore necessary to understand the general public’s views on medical AI. Here, a meta-synthesis was conducted to analyse and summarise the public’s understanding of the application of AI in the healthcare field, to provide recommendations for future use and management of AI in medical practice. Design This was a meta-synthesis of qualitative studies. Method A search was performed on the following databases to identify studies published in English and Chinese: MEDLINE, CINAHL, Web of science, Cochrane library, Embase, PsycINFO, CNKI, Wanfang and VIP. The search was conducted from database inception to 25 December 2021. The meta-aggregation approach of JBI was used to summarise findings from qualitative studies, focusing on the public’s perception of the application of AI in healthcare. Results Of the 5128 studies screened, 12 met the inclusion criteria, hence were incorporated into analysis. Three synthesised findings were used as the basis of our conclusions, including advantages of medical AI from the public’s perspective, ethical and legal concerns about medical AI from the public’s perspective, and public suggestions on the application of AI in medical field. Conclusion Results showed that the public acknowledges the unique advantages and convenience of medical AI. Meanwhile, several concerns about the application of medical AI were observed, most of which involve ethical and legal issues. The standard application and reasonable supervision of medical AI is key to ensuring its effective utilisation. Based on the public’s perspective, this analysis provides insights and suggestions for health managers on how to implement and apply medical AI smoothly, while ensuring safety in healthcare practice. PROSPERO registration number CRD42022315033.

The number of risk prediction models for deep venous thrombosis (DVT) in patients with acute stroke is increasing, while the quality and applicability of these models in clinical practice and future research remain unknown. To systematically review published studies on risk prediction models for DVT in patients with acute stroke. Systematic review and meta-analysis of observational studies. China National Knowledge Infrastructure (CNKI), Wanfang Database, China Science and Technology Journal Database (VIP), SinoMed, PubMed, Web of Science, The Cochrane Library, Cumulative Index to Nursing and Allied Health Literature (CINAHL) and Embase were searched from inception to November 7, 2022. Data from selected studies were extracted, including study design, data source, outcome definition, sample size, predictors, model development and performance. The Prediction Model Risk of Bias Assessment Tool (PROBAST) checklist was used to assess the risk of bias and applicability. A total of 940 studies were retrieved, and after the selection process, nine prediction models from nine studies were included in this review. All studies utilized logistic regression to establish DVT risk prediction models. The incidence of DVT in patients with acute stroke ranged from 0.4 % to 28 %. The most frequently used predictors were D-dimer and age. The reported area under the curve (AUC) ranged from 0.70 to 0.912. All studies were found to have a high risk of bias, primarily due to inappropriate data sources and poor reporting of the analysis domain. The pooled AUC value of the five validated models was 0.76 (95 % confidence interval: 0.70-0.81), indicating a fair level of discrimination. Although the included studies reported a certain level of discrimination in the prediction models of DVT in patients with acute stroke, all of them were found to have a high risk of bias according to the PROBAST checklist. Future studies should focus on developing new models with larger samples, rigorous study designs, and multicenter external validation. The protocol for this study is registered with PROSPERO (registration number: CRD42022370287).

To synthesize nursing students' knowledge and attitudes about end-of-life care and to identify strategic directions for optimizing end-of-life care education. A meta-analysis of observational studies. PubMed, Web of Science, EMBASE, CINAHL, PsycINFO, MEDLINE, CNKI, and WANFANG 8 electronic databases in English and Chinese were systematically searched from inception until 10 April 2022. Two reviewers independently screened literature and extracted data using structured tables. The Agency for Healthcare Research and Quality (AHRQ) was used to appraise the methodological quality of included studies. The study outcomes were synthesized using a meta-analysis. 26 cross-sectional studies of medium or high quality from 13 countries met the eligibility criteria, involving 9749 nursing students. In our review, nursing students demonstrated insufficient knowledge about end-of-life care, with a pooled mean score of 7.50 (95 % CI: 6.55-8.45); of these, knowledge about philosophy and principles, psychosocial and spiritual care, and pain and symptom management were all deficient, with pooled mean scores of 1.49 (95 % CI: 0.78-2.21), 1.00 (95 % CI: 0.35-1.65), and 3.44 (95 % CI: 2.25-4.63), respectively. Conversely, nursing students showed positive attitudes toward end-of-life care, with a pooled mean score of 102.97 (95 % CI: 99.43-106.51). The subgroup analysis revealed that male nursing students had lower pooled mean scores for end-of-life care knowledge and attitudes. There is a mismatch between nursing students' knowledge and attitudes about end-of-life care, they have a positive attitude but lack the necessary knowledge. Male nursing students seem to have a greater deficit of knowledge and a relatively conservative attitude toward end-of-life care. These findings may provide a significant reference for nursing educators to adjust educational strategies promptly.