## 2 Literature Review  
In the area of literature review, there are two major types (i.e., systematic review [45] and bibliometric analysis). Compared to a systematic review, bibliometric analysis is more suitable for evaluating larger-scale literature data and has thus been widely adopted in various research areas [42; 44; 46; 47]. For example, based on 62 articles collected from the WoS, by using content analysis, [PERSON] et al. [48] presented the research trends and bibliometric results of the literature regarding the adoption of augmented reality in science education. Based on the obtained results, they identified several frequently used keywords, including \"science education,\" \"science learning,\" and \"e-learning,\" particularly mobile learning, which has been a great concern for more recent literature. Besides, it was found that \"education,\" \"knowledge,\" \"science education,\" \"experiment,\" and \"effectiveness\" were the most frequently used terms in the abstracts. In addition, interests in students' knowledge and achievement were also found to be significant among authors. With the use of the publications of Computers & Industrial Engineering, one of the top academic journals in the field of the supply chain, [PERSON] et al. [49] identified predominant trends in the field by analyzing the most important publications, keywords, authors, institutions, as well as countries. STM has also been applied to investigate research topics within a particular research domain. For example, based on the abstract information of research articles on community forestry published during the period of 1990-2017, [PERSON] and [PERSON] [50] used bibliometric analysis and an STM method to explore trends of geographic foci, as well as research topics. Four main research issues and 20 topics were identified. The distributions of the annual topic proportions indicated that the research focus has evolved from policy analysis to regional carbon sequestration. Based on a collection of open-ended comments from a nationwide representative sample of 16,464 drivers collected in 2016 and 2017, [PERSON] and [PERSON] [51] adopted STM to identify 13 research themes, for example, \"Tested for a long time\" and \"Hacking & glitches.\" With the use of STM, [PERSON] et al. [52] analyzed 177 responses of the mentally disabled regarding their willingness to using autonomous vehicles. In this way, they identified potential obstacles for the adoption of autonomous vehicles perceived by the mentally disabled. Their study provided suggestions to the government and manufacturers in planning public information campaigns to promote the acceptance of autonomous vehicles by the mentally disabled. Focusing on NLP and clinical trials research, a few bibliometric studies are available. From the NLP perspective, a bibliometric analysis of NLP [53], NLP-empowered mobile computing research [54], NLP-enhanced medical research [47], and computational linguistics [55] can be found. Another aspect is research related to clinical trials or clinical. Reprehensive works include bibliometric analyses of, e.g.,the influence measurement of clinical trials citations [56], clinical studies on Tai Chi [57], controlled clinical research concerning traditional Chinese medicine (TCM) in cancer care [58], clinical research articles on sepsis [59], and radiologic randomized controlled trials (RCTs) [60]. For example, with a large volume of clinical studies concerning the safety and advantages of Tai Chi mind-body exercise, [PERSON] et al. [57] systematically synthesized the results or findings of clinical studies concerning Tai Chi for healthcare. Based on the controlled clinical scientific articles of TCM treatments for cancers collected from four major Chinese electronic databases, [PERSON] et al. [58] bibliometrically analyzed these articles to assess the reporting quality and summarize the evidence presented in the studied articles.