Background: Discrete choice experiment (DCE) as a tool that can measure medical stakeholders' preferences especially patients recently has been increasingly applied in health care.Objective: The aim of this study was to examine the hotspots and trends of the application of DCE in health care and to provide reference and direction for further development of DCE in the future.Method: A bibliometric method was implemented using the Web of Science (WoS) Core Collection for the period from the database established to December 8, 2020. The data files are imported into CiteSpace and Excel to analyze and visualize the annual volume of productive, authors, countries, cited journals, cited articles, and keywords.Results: A total of 1,811 articles were retrieved, then we read the abstract of each paper one by one, and 1,562 articles were included after screening, with an exponential increase in publication volume. John F. P. Bridges contributed to 40 publications and ranked first, followed by F. Reed Johnson (n = 37), Julie Ratcliffe (n = 36). The majority of the papers were conducted in the United States (n = 513) and the United Kingdom (n = 433). The top three cited journals were “Health Economics” (n = 981), “Value in Health” (n = 893), and “Pharmaceutical Economics” (n = 774), and the top three articles were “Constructing experimental designs for discrete-choice experiments: report of the ISPOR Conjoint Analysis Experimental Design Good Research Practices Task Force,” “Conjoint analysis applications in health-a checklist: a report of the ISPOR Good Research Practices for Conjoint Analysis Task Force,” and “Discrete choice experiments in health economics: a review of the literature.” The research hotspots and trends included “health technology assessment,” “survival,” “preference based measure,” and “health state valuation.”Conclusion: The size of the literature about DCE studies in health care showed a noticeable increase in the past decade. The application of DCE in health care remains in an early growth phase, and “health technology assessment,” “survival,” “preference based measure,” and “health state valuation” reflected the latest research hotpots and future trends.

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PurposeThis study is a systematic literature review of the application of artificial intelligence (AI) in safety-critical systems. The authors aim to present the current application status according to different AI techniques and propose some research directions and insights to promote its wider application.Design/methodology/approachA total of 92 articles were selected for this review through a systematic literature review along with a thematic analysis.FindingsThe literature is divided into three themes: interpretable method, explain model behavior and reinforcement of safe learning. Among AI techniques, the most widely used are Bayesian networks (BNs) and deep neural networks. In addition, given the huge potential in this field, four future research directions were also proposed.Practical implicationsThis study is of vital interest to industry practitioners and regulators in safety-critical domain, as it provided a clear picture of the current status and pointed out that some AI techniques have great application potential. For those that are inherently appropriate for use in safety-critical systems, regulators can conduct in-depth studies to validate and encourage their use in the industry.Originality/valueThis is the first review of the application of AI in safety-critical systems in the literature. It marks the first step toward advancing AI in safety-critical domain. The paper has potential values to promote the use of the term “safety-critical” and to improve the phenomenon of literature fragmentation.

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Recently, there have increasingly been debates on whether there exists a surface resonance state (SRS) in black phosphorus (BP), as suggested by recent angle-resolved photoemission spectroscopy results. To resolve this issue, we have performed temperature- and angle-dependent magnetoconductivity measurements on exfoliated, high-quality BP single crystals. A pronounced weak-antilocalization (WAL) effect was observed within a narrow temperature range of 8-16 K, with the electrical current flowing parallel to the cleaved ac-plane (along the a- or c-axis) and the magnetic field along the b-axis. The angle-dependent magnetoconductivity and the Hikami-Larkin-Nagaoka model-fitted results have revealed that the observed WAL effect shows surface-bulk coherent features, which supports the existence of SRS in BP.