Search

China is facing significant challenges in healthcare owing to a severe aging population, continuously increasing healthcare demands, and uneven distribution of healthcare resources. The application of artificial intelligence (AI) and robotics in the human medical and health field can provide innovative support in theory and diagnosis for surgeons, researchers, and patients, contributing to improved clinical outcomes, reduced medical costs, and the promotion of balanced distribution of healthcare resources. This paper starts from four aspects: surgical robots, rehabilitation and nursing robots, assisted telemedicine, and medical AI, extracts the typical applications of AI and robots in the medical field, assesses the current status of AI and robotics in medicine, and evaluates pertinent policies, laws, and regulatory frameworks. Based on the understanding of the challenges faced in the development of AI and robotics in medicine in China, it delineates phased development goals and identifies future key directions: advancing toward differentiated, miniaturized, and intelligent surgical robotics; emphasizing patient-centered rehabilitation and nursing robotics; achieving multi-tasking and high situational awareness in assisted telemedicine; and promoting medical AI for healthcare innovation. This paper suggests determining promising research directions for surgical robotics, promoting domestic production of core components, exploring commercially viable models that align with national conditions, expanding the coverage of product applications, focusing on the development of specialized talent pools, implementing technology innovation driven by clinical demands, and strengthening regulations to reduce medical accidents and safeguard data privacy. These measures are expected to foster high-quality development of AI and robotics in medicine in China.

Digital textbooks that offer multimedia features, interactive controls, e-annotation and learning process tracking are gaining increasing attention in today's mobile learning era, particularly with the rapid development of mobile learning terminals such as Apple's iPad series and Android-based models. Accordingly, this study explores how elementary school students use digital textbooks via mobile learning terminals such as the iPad by mainly assessing relevant data resources. The participants of this study included 6211 students from 74 elementary schools in China. Several themes, such as demographic characteristics, self-efficacy, technology acceptance model, learning process, behavior attention and attitude with regard to their usage of digital textbooks, were analyzed. The data of this study would aid policy makers and educators in conducting further research on the implementation of the K-12 educational technology reform with digital textbooks via mobile learning terminals in formal school settings.

The virtual teaching and research platform is a cognitive and practical activity for relevant teachers engaged in education and teaching research to improve and improve the quality of education and teaching, relying on the unique functions and resource advantages of the network, using information technology and various network tools to carry out teaching, feedback, etc., and to carry out education and teaching exchange, discussion and other education and teaching research with other administrative organizations or teachers in space regions. It can break the constraints of time, space, organization and other factors. It is an effective form of educational and teaching activities carried out for common goals. It integrates teaching, guidance, discussion, exchange, resource sharing and other functions, and is an important part of the "double training plan". The collaborative innovation ability training mode selection of virtual teaching and research platforms as a multi-attribute group decision making (MAGDM) problem. In this paper, the Evaluation Based on Distance from Average Solution (EDAS) method is extended to the single-valued neutrosophic sets (SVNSs) and single-valued neutrosophic EDAS method (SVN-EDAS) is proposed to solve MAGDM problem. Finally, the collaborative innovation ability training mode selection of virtual teaching and research platforms is given to show the SVN-EDAS model and some comparative analysis is conduct to depict the advantages of SVN-EDAS. [ABSTRACT FROM AUTHOR]

Background Electronic medical records provide large-scale real-world clinical data for use in developing clinical decision systems. However, sophisticated methodology and analytical skills are required to handle the large-scale datasets necessary for the optimisation of prediction accuracy. Myopia is a common cause of vision loss. Current approaches to control myopia progression are effective but have significant side effects. Therefore, identifying those at greatest risk who should undergo targeted therapy is of great clinical importance. The objective of this study was to apply big data and machine learning technology to develop an algorithm that can predict the onset of high myopia, at specific future time points, among Chinese school-aged children. Methods and findings Real-world clinical refraction data were derived from electronic medical record systems in 8 ophthalmic centres from January 1, 2005, to December 30, 2015. The variables of age, spherical equivalent (SE), and annual progression rate were used to develop an algorithm to predict SE and onset of high myopia (SE [less than or equal to] -6.0 dioptres) up to 10 years in the future. Random forest machine learning was used for algorithm training and validation. Electronic medical records from the Zhongshan Ophthalmic Centre (a major tertiary ophthalmic centre in China) were used as the training set. Ten-fold cross-validation and out-of-bag (OOB) methods were applied for internal validation. The remaining 7 independent datasets were used for external validation. Two population-based datasets, which had no participant overlap with the ophthalmic-centre-based datasets, were used for multi-resource validation testing. The main outcomes and measures were the area under the curve (AUC) values for predicting the onset of high myopia over 10 years and the presence of high myopia at 18 years of age. In total, 687,063 multiple visit records ([greater than or equal to]3 records) of 129,242 individuals in the ophthalmic-centre-based electronic medical record databases and 17,113 follow-up records of 3,215 participants in population-based cohorts were included in the analysis. Our algorithm accurately predicted the presence of high myopia in internal validation (the AUC ranged from 0.903 to 0.986 for 3 years, 0.875 to 0.901 for 5 years, and 0.852 to 0.888 for 8 years), external validation (the AUC ranged from 0.874 to 0.976 for 3 years, 0.847 to 0.921 for 5 years, and 0.802 to 0.886 for 8 years), and multi-resource testing (the AUC ranged from 0.752 to 0.869 for 4 years). With respect to the prediction of high myopia development by 18 years of age, as a surrogate of high myopia in adulthood, the algorithm provided clinically acceptable accuracy over 3 years (the AUC ranged from 0.940 to 0.985), 5 years (the AUC ranged from 0.856 to 0.901), and even 8 years (the AUC ranged from 0.801 to 0.837). Meanwhile, our algorithm achieved clinically acceptable prediction of the actual refraction values at future time points, which is supported by the regressive performance and calibration curves. Although the algorithm achieved balanced and robust performance, concerns about the compromised quality of real-world clinical data and over-fitting issues should be cautiously considered. Conclusions To our knowledge, this study, for the first time, used large-scale data collected from electronic health records to demonstrate the contribution of big data and machine learning approaches to improved prediction of myopia prognosis in Chinese school-aged children. This work provides evidence for transforming clinical practice, health policy-making, and precise individualised interventions regarding the practical control of school-aged myopia., Author(s): Haotian Lin 1, Erping Long 1, Xiaohu Ding 1, Hongxing Diao 1, Zicong Chen 2, Runzhong Liu 2, Jialing Huang 2, Jingheng Cai 3, Shuangjuan Xu 3, Xiayin Zhang [...]