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AbstractArtificial intelligence (AI) is being increasingly applied in healthcare. The expansion of AI in healthcare necessitates AI-related ethical issues to be studied and addressed. This systematic scoping review was conducted to identify the ethical issues of AI application in healthcare, to highlight gaps, and to propose steps to move towards an evidence-informed approach for addressing them. A systematic search was conducted to retrieve all articles examining the ethical aspects of AI application in healthcare from Medline (PubMed) and Embase (OVID), published between 2010 and July 21, 2020. The search terms were "artificial intelligence" or "machine learning" or "deep learning" in combination with "ethics" or "bioethics". The studies were selected utilizing a PRISMA flowchart and predefined inclusion criteria. Ethical principles of respect for human autonomy, prevention of harm, fairness, explicability, and privacy were charted. The search yielded 2166 articles, of which 18 articles were selected for data charting on the basis of the predefined inclusion criteria. The focus of many articles was a general discussion about ethics and AI. Nevertheless, there was limited examination of ethical principles in terms of consideration for design or deployment of AI in most retrieved studies. In the few instances where ethical principles were considered, fairness, preservation of human autonomy, explicability and privacy were equally discussed. The principle of prevention of harm was the least explored topic. Practical tools for testing and upholding ethical requirements across the lifecycle of AI-based technologies are largely absent from the body of reported evidence. In addition, the perspective of different stakeholders is largely missing.

In December 2019, corona virus disease 2019 (COVID-19) has broken out in China. Understanding the distribution of disease at the national level contributes to the formulation of public health policies. There are several studies that investigating the influencing factors on distribution of COVID-19 in China. However, more influencing factors need to be considered to improve our understanding about the current epidemic. Moreover, in the absence of effective medicine or vaccine, the Chinese government introduced a series of non-pharmaceutical interventions (NPIs). However, assessing and predicting the effectiveness of these interventions requires further study. In this paper, we used statistical techniques, correlation analysis and GIS mapping expression method to analyze the spatial and temporal distribution characteristics and the influencing factors of the COVID-19 in mainland China. The results showed that the spread of outbreaks in China's non-Hubei provinces can be divided into five stages. Stage I is the initial phase of the COVID-19 outbreak; in stage II the new peak of the epidemic was observed; in stage III the outbreak was contained and new cases decreased; there was a rebound in stage IV, and stage V led to level off. Moreover, the cumulative confirmed cases were mainly concentrated in the southeastern part of China, and the epidemic in the cities with large population flows from Wuhan was more serious. In addition, statistically significant correlations were found between the prevalence of the epidemic and the temperature, rainfall and relative humidity. To evaluate the NPIs, we simulated the prevalence of the COVID-19 based on an improved SIR model and under different prevention intensity. It was found that our simulation results were compatible with the observed values and the parameter of the time function in the improved SIR model for China is a = − 0.0058. The findings and methods of this study can be effective for predicting and managing the epidemics and can be used as an aid for decision makers ...