The increasing demand for new therapies and other clinical interventions has made researchers conduct many clinical trials. The high level of evidence generated by clinical trials makes them the main approach to evaluating new clinical interventions. The increasing amounts of data to be considered in the planning and conducting of clinical trials has led to higher costs and increased timelines of clinical trials, with low productivity. Advanced technologies including artificial intelligence, machine learning, deep learning, and the internet of things offer an opportunity to improve the efficiency and productivity of clinical trials at various stages. Although researchers have done some tangible work regarding the application of advanced technologies in clinical trials, the studies are yet to be mapped to give a general picture of the current state of research. This systematic mapping study was conducted to identify and analyze studies published on the role of advanced technologies in clinical trials. A search restricted to the period between 2010 and 2020 yielded a total of 443 articles. The analysis revealed a trend of increasing research interests in the area over the years. Recruitment and eligibility aspects were the main focus of the studies. The main research types were validation and evaluation studies. Most studies contributed methods and theories, hence there exists a gap for architecture, process, and metric contributions. In the future, more empirical studies are expected given the increasing interest to implement the AI, ML, DL, and IoT in clinical trials.

To explore the complexity and uncertainty of R&D cooperation in supply and demand network (SDN) enterprises, this study used the evolutionary game theory and the duplicate dynamic equation to construct the R&D cooperation strategy selection model. Based on the perspective of knowledge spillover, the model investigated the influence of knowledge spillover, government reward, government penalty, cooperative cost, and R&D cooperation risk in the cooperation of SDN enterprises. Next, this study analyzed the stability and the system evolution path of the model. Finally, it used numerical simulation to vary the rationality of the model. Based on this research, there are some conclusions can be obtained: Firstly, the evolution of R&D cooperation strategy for SDN enterprises may eventually stabilize at cooperation or selfishness strategy under different situations. As for which strategy is ultimately stable, it is closely related to the construction of the initial payment matrix and the selection of initial parameters. Secondly, there are two stable states in the game system: cooperation and selfishness. If one party chooses cooperation and the other party chooses selfishness, the system will not be stable. Thirdly, knowledge spillover degree, government reward, government penalty, government penalty, cooperation cost, and cooperation risk are important factors that can influence the cooperation evolution of SDN enerprises. Fourth, under certain situations, if knowledge spillover degree, government reward, and government penalty increase, cooperation cost and cooperation risk decrease, the R&D cooperation in SDN enterprises will be improved. Related implications and suggestions are finally proposed, which can offer some valuable guidance for the development of SDN enterprises.