Brain-computer interfaces (BCIs) have become one of the cutting-edge technologies in the world, and have been mainly applicated in medicine. In this article, we sorted out the development history and important scenarios of BCIs in medical application, analyzed the research progress, technology development, clinical transformation and product market through qualitative and quantitative analysis, and looked forward to the future trends. The results showed that the research hotspots included the processing and interpretation of electroencephalogram (EEG) signals, the development and application of machine learning algorithms, and the detection and treatment of neurological diseases. The technological key points included hardware development such as new electrodes, software development such as algorithms for EEG signal processing, and various medical applications such as rehabilitation and training in stroke patients. Currently, several invasive and non-invasive BCIs are in research. The R&D level of BCIs in China and the United State is leading the world, and have approved a number of non-invasive BCIs. In the future, BCIs will be applied to a wider range of medical fields. Related products will develop shift from a single mode to a combined mode. EEG signal acquisition devices will be miniaturized and wireless. The information flow and interaction between brain and machine will give birth to brain-machine fusion intelligence. Last but not least, the safety and ethical issues of BCIs will be taken seriously, and the relevant regulations and standards will be further improved.
The covariate-adjusted response-adaptive randomisation (CARA) design combines the advantages of response-adaptive randomisation and covariate-adaptive randomisation, and improves the efficiency and reliability of clinical trials by combining analytical results and covariates and dynamically adjusting the allocation of subsequent patients. This paper describes in detail several methods of CARA design and their example applications of various methods, including the dominant confidence method, the urn model, the generalized linear model, and the Atkinson model, and provides the corresponding R codes in anticipation of a wider application of the provided R codes in clinical trials.
ObjectiveTo review the current status of case studies on drug comprehensive clinical evaluation published in China in the past decade, providing references for the standardized implementation of evaluation. MethodsA scoping review was conducted on the published cases of drug comprehensive clinical evaluation in China. The analysis was carried out from themes, dimensions, methods, and quality control. Descriptive analysis was used to analyze the evaluation themes, dimension, method, and quality control of the included studies. Meanwhile, VOSviewer 1.6.20 software was used to conduct co-occurrence network analysis of author's affiliation and evaluation dimensions. ResultsThe research on comprehensive drug clinical evaluation has developed the fastest in Beijing. Medical institutions, universities, and scientific institutions are the main research institutions. Universities and their affiliated hospitals have stronger collaboration, while some local hospitals show research isolation. The evaluation themes focus on chronic diseases and cancer, with some repetitions. The evaluation methods mainly rely on descriptive analysis. In nearly half of the cases, evidence-based medicine methods are inadequately applied. Only about 5% of the cases have conducted real-world research. For cases with the same theme, results may conflict due to differences in methods or data sources. The value integration mainly relies on quantitative scoring, which is lacking in 37.05% cases. Only 14.03% of the cases report quality control. In 31.87% of cases, the data provided by pharmaceutical companies were used, with a lack of conflict-of-interest disclosures. ConclusionAlthough the comprehensive clinical evaluation of drugs in China has developed rapidly, there are problems such as non-standardized methods and differences in data sources. It is necessary to improve the scientificity and decision-making value of evaluation results by coordinating theme selection, strengthening quality control, and developing reporting guideline.