AI-related Research Research Articles

International publication trends in the application of artificial intelligence in ophthalmology research: an updated bibliometric analysis.

The literature on artificial intelligence (AI)-related topics has been expanding rapidly over the last two decades, showing that AI is a crucial force in advancing ophthalmology. This analysis aims to provide a dynamic and longitudinal bibliometric analysis of AI-related ophthalmic papers. The Web of Science was searched to retrieve papers regarding the application of AI in ophthalmology published in the English language up to May 2022. The variables were analyzed using Microsoft Excel 2019 and GraphPad Prism 9. Data visualization was performed using VOSviewer and CiteSpace. In this study, a total of 1,686 publications were analyzed. Recently, AI-related ophthalmology research has increased exponentially. China was the most productive country in this research field, with 483 articles, but the United States of America (446 publications) contributed most to the sum of citations and the H-index. The League of European Research Universities, Ting DSW, and Daniel SW were the most prolific institution and researchers. This field is primarily concerned with diabetic retinopathy (DR), glaucoma, optical coherence tomography, and the classification and diagnosis of fundus pictures. Current hotspots in AI research include deep learning, diagnosing and predicting systemic disorders by fundus images, incidence and progression of ocular diseases, and outcome prediction. This analysis thoroughly reviews AI-related research in ophthalmology to help academics better comprehend the growth and possible practice consequences of AI. The association between eye and systemic biomarkers, telemedicine, real-world studies, and the development and application of new AI algorithms, such as visual converters, will continue to be research hotspots over the next few years.

Inaugural Editorial

It gives me great pleasure to write an editorial note for the first edition of the new journal of Artificial Intelligence and Applications (AIA). Before I begin, I would like to thank our Managing Editor, Mrs. Yu Zhang of Bon View Publishing, Singapore for approaching me with a great idea of launching a new journal under my leadership in the field of computer science. Therefore, I am honored and fortunate to be part of AIA and take responsibility as Editor-in-Chief. Since it is the first joint venture for us, initially, we struggled and worked hard to find innovative ways to reach potential authors such that AIA can receive quality papers across the globe. As a result, in a short period, AIA has attracted many quality submissions from different countries including developing and developed countries. Since the journal scope is AI related research, we encouraged the authors to emphasize emerging areas of artificial intelligence, such as image processing, security, augmented reality, data mining, machine learning, distributed computing and big data analysis, etc. The first issue includes high quality papers submitted from different countries in the areas mentioned above. However, in AIA, the acceptance rate is only 24%. We are determined to bring you a highly respected journal by setting our standard from the very first issue. We are happy to release the first issue in January 2023. For this achievement, we should thank our team led by Mrs. Yu Zhang, Ms. Phoebe, Ms. Lantian, Ms. Ruifang Zhang for their efforts, time, hard work and significant contributions. Today, we see the success because of their support and effort. To take any journal to a high level/standard in terms of quality papers according to the rules stipulated by the web of science, Scopus, the role of the International Advisors, Associate Editors, Editorial board members, Reviewers and Editorial staff of the journal is vital. In order to maintain the standards of AIA, we introduced desk reject by providing sufficient technical comments, which helped authors to improve the quality of their work. There were a number of challenges, including issues pertaining to reviews, such as delays in receiving review reports, poor reviews, finding relevant experts for reviewing and sufficient number of reviews. However, with the help of the tips provided by the members of the advisory board, our editorial office managed effectively in communicating with the authors, reviewers and Editor-in-Chief without any problems. What is very important for the journal is popularity, so it should reach as many potential readers as possible to achieve a global scientific exposure and impact. To achieve such goals, we need to explore new ways of dissemination and reaching authors, as well as readers. At the same time, our journal should find new ways to receive more quality papers on intra-and-inter-disciplinary areas. We believe if the topic is interesting and has scope for the future, it will show great motivation on submission and a loyal readership. I am fortunate to be part of a highly effective team led by Yu Zhang in the editorial office who is instrumental to achieving our targets. The current group of Associate Editors work incredibly well to assess paper quality and to maintain standards. The papers published by Bon View Publishing focus on special domains and publish our papers in time. In addition, our journal has good support from members of the international advisory board, which comprises pioneers in different areas of artificial intelligence. They serve like ambassadors for our journal. Last but not the least, I should thank all our contributors who chose our journal to publish their work. Furthermore, there were many authors that were disappointed due to rejection, but they should be happy with their achievements and the editorial board comments to improve their work.

From critical technical practice to reflexive data science

In this article, we reconsider elements of Agre’s critical technical practice approach ( Agre, 1997 ) for critical technical practice approach for reflexive artificial intelligence (AI) research and explore ways and expansions to make it productive for an operationalization in contemporary data science. Drawing on Jörg Niewöhner’s co-laboration approach, we show how frictions within interdisciplinary work can be made productive for reflection. We then show how software development environments can be repurposed to infrastructure reflexivities and to make co-laborative engagement with AI-related technology possible and productive. We document our own co-laborative engagement with machine learning and highlight three exemplary critical technical practices that emerged out of the co-laboration: negotiating comparabilities, shifting contextual attention and challenging similarity and difference. We finally wrap up the conceptual and empirical elements and propose Reflexive Data Science (RDS) as a methodology for co-laborative engagement and infrastructured reflexivities in contemporary AI-related research. We come back to Agre’s ways of operationalizing reflexivity and introduce the building blocks of RDS: (1) organizing encounters of social contestation, (2) infrastructuring a network of anchoring devices enabling reflection, (3) negotiating timely matters of concern and (4) designing for reflection. With our research, we aim at contributing to the methodological underpinnings of epistemological and social reflection in contemporary AI research.

Orbital and eyelid diseases: The next breakthrough in artificial intelligence?

Orbital and eyelid disorders affect normal visual functions and facial appearance, and precise oculoplastic and reconstructive surgeries are crucial. Artificial intelligence (AI) network models exhibit a remarkable ability to analyze large sets of medical images to locate lesions. Currently, AI-based technology can automatically diagnose and grade orbital and eyelid diseases, such as thyroid-associated ophthalmopathy (TAO), as well as measure eyelid morphological parameters based on external ocular photographs to assist surgical strategies. The various types of imaging data for orbital and eyelid diseases provide a large amount of training data for network models, which might be the next breakthrough in AI-related research. This paper retrospectively summarizes different imaging data aspects addressed in AI-related research on orbital and eyelid diseases, and discusses the advantages and limitations of this research field.

Current Status and Future Perspectives of Artificial Intelligence in Colonoscopy.

The early endoscopic identification, resection, and treatment of precancerous adenoma and early-stage cancer has been shown to reduce not only the prevalence of colorectal cancer but also its mortality rate. Recent advances in endoscopic devices and imaging technology have dramatically improved our ability to detect colorectal lesions and predict their pathological diagnosis. In addition to this, rapid advances in artificial intelligence (AI) technology mean that AI-related research and development is now progressing in the diagnostic imaging field, particularly colonoscopy, and AIs (i.e., devices that mimic cognitive abilities, such as learning and problem-solving) already approved as medical devices are now being introduced into everyday clinical practice. Today, there is an increasing expectation that sophisticated AIs will be able to provide high-level diagnostic performance irrespective of the level of skill of the endoscopist. In this paper, we review colonoscopy-related AI research and the AIs that have already been approved and discuss the future prospects of this technology.

Meaningless Applications and Misguided Methodologies in Artificial Intelligence–Related Orthopaedic Research Propagates Hype Over Hope

There exists great hope and hype in the literature surrounding applications of artificial intelligence (AI) to orthopaedic surgery. Between 2018 and 2021, a total of 178 AI-related articles were published in orthopaedics. However, for every 2 original research papers that apply AI to orthopaedics, a commentary or review is published (30.3%). AI-related research in orthopaedics frequently fails to provide use cases that offer the uninitiated an opportunity to appraise the importance of AI by studying meaningful questions, evaluating unknown hypotheses, or analyzing quality data. The hype perpetuates a feed-forward cycle that relegates AI to a meaningless buzzword by rewarding those with nascent understanding and rudimentary technical knowhow into committing several basic errors: (1) inappropriately conflating vernacular (“AI/machine learning”), (2) repackaging registry data, (3) prematurely releasing internally validated algorithms, (4) overstating the “black box phenomenon” by failing to provide weighted analysis, (5) claiming to evaluate AI rather than the data itself, and (6) withholding full model architecture code. Relevant AI-specific guidelines are forthcoming, but forced application of the original Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis guidelines designed for regression analyses is irrelevant and misleading. To safeguard meaningful use, AI-related research efforts in orthopaedics should be (1) directed toward administrative support over clinical evaluation and management, (2) require the use of the advanced model, and (3) answer a question that was previously unknown, unanswered, or unquantifiable.

유아 인공지능(AI) 교육에 대한 유치원교사 인식 분석

Objectives The purpose of this study is to explore the direction of AI education for infants for nurturing future talents by analyzing the perceptions of kindergarten teachers on AI education for infants.
 Methods A survey was conducted with 275 kindergarten teachers. The questionnaire was composed and revised by researchers based on related prior research, and validity was secured through review by experts in early childhood education and researchers with experience in AI-related research. Descriptive statistics were performed on the collected questionnaires using the SPSS 21 program, and cross-analysis and one-way analysis of variance (ANOVA) were performed to analyze the differences between groups.
 Results As a result of the study, first, it was found that there was a significant difference in the level of interest in artificial intelligence according to the teacher s AI-related training experience and education experience. It was found that the higher the final educational level, the higher the educational experience, the higher the level of interest and belief in artificial intelligence. Also, it was analyzed that the majority of teachers understood AI as an intelligent system that integrates knowledge from various academic fields such as engineering, natural science, and education. Second, as a result of analyzing the perceptions of kindergarten teachers on AI education, it was found that teachers with high final education, teachers with many educational experiences, and teachers with artificial intelligence-related training and education experiences had significantly higher awareness levels. Third, as a result of analyzing kindergarten teachers perceptions on the necessity and possibility of AI education for infants, it was found that teachers with high final education and teachers with AI-related training and education experience had significantly higher awareness levels. In addition, it was found that the lack of experts to support related education is recognized as the biggest problem as a problem with early childhood AI education.
 Conclusions In order to improve kindergarten teachers awareness of early childhood AI education to a high level, efforts are required for policy and institutional support, such as strengthening teacher training related to early childhood AI education, and follow-up research should be actively carried out for this purpose. This study is meaningful in that it provided basic data to explore the early childhood AI education for young children for nurturing future talents by analyzing kindergarten teachers perceptions of early childhood AI education.

The Global Research of Artificial Intelligence on Prostate Cancer: A 22-Year Bibliometric Analysis

BackgroundWith the rapid development of technology, artificial intelligence (AI) has been widely used in the diagnosis and prognosis prediction of a variety of diseases, including prostate cancer. Facts have proved that AI has broad prospects in the accurate diagnosis and treatment of prostate cancer.ObjectiveThis study mainly summarizes the research on the application of artificial intelligence in the field of prostate cancer through bibliometric analysis and explores possible future research hotspots.MethodsThe articles and reviews regarding application of AI in prostate cancer between 1999 and 2020 were selected from Web of Science Core Collection on August 23, 2021. Microsoft Excel 2019 and GraphPad Prism 8 were applied to analyze the targeted variables. VOSviewer (version 1.6.16), Citespace (version 5.8.R2), and a widely used online bibliometric platform were used to conduct co-authorship, co-citation, and co-occurrence analysis of countries, institutions, authors, references, and keywords in this field.ResultsA total of 2,749 articles were selected in this study. AI-related research on prostate cancer increased exponentially in recent years, of which the USA was the most productive country with 1,342 publications, and had close cooperation with many countries. The most productive institution and researcher were the Henry Ford Health System and Tewari. However, the cooperation among most institutions or researchers was not close even if the high research outputs. The result of keyword analysis could divide all studies into three clusters: “Diagnosis and Prediction AI-related study”, “Non-surgery AI-related study”, and “Surgery AI-related study”. Meanwhile, the current research hotspots were “deep learning” and “multiparametric MRI”.ConclusionsArtificial intelligence has broad application prospects in prostate cancer, and a growing number of scholars are devoted to AI-related research on prostate cancer. Meanwhile, the cooperation among various countries and institutions needs to be strengthened in the future. It can be projected that noninvasive diagnosis and accurate minimally invasive treatment through deep learning technology will still be the research focus in the next few years.

Understanding and Creating Art with AI: Review and Outlook

Technologies related to artificial intelligence (AI) have a strong impact on the changes of research and creative practices in visual arts. The growing number of research initiatives and creative applications that emerge in the intersection of AI and art motivates us to examine and discuss the creative and explorative potentials of AI technologies in the context of art. This article provides an integrated review of two facets of AI and art: (1) AI is used for art analysis and employed on digitized artwork collections, or (2) AI is used for creative purposes and generating novel artworks. In the context of AI-related research for art understanding, we present a comprehensive overview of artwork datasets and recent works that address a variety of tasks such as classification, object detection, similarity retrieval, multimodal representations, and computational aesthetics, among others. In relation to the role of AI in creating art, we address various practical and theoretical aspects of AI Art and consolidate related works that deal with those topics in detail. Finally, we provide a concise outlook on the future progression and potential impact of AI technologies on our understanding and creation of art.

Translatability Analysis of National Institutes of Health–Funded Biomedical Research That Applies Artificial Intelligence

Despite the rapid growth of interest and diversity in applications of artificial intelligence (AI) to biomedical research, there are limited objective ways to characterize the potential for use of AI in clinical practice. To examine what types of medical AI have the greatest estimated translational impact (ie, ability to lead to development that has measurable value for human health) potential. In this cohort study, research grants related to AI awarded between January 1, 1985, and December 31, 2020, were identified from a National Institutes of Health (NIH) award database. The text content for each award was entered into a Natural Language Processing (NLP) clustering algorithm. An NIH database was also used to extract citation data, including the number of citations and approximate potential to translate (APT) score for published articles associated with the granted awards to create proxies for translatability. Unsupervised assignment of AI-related research awards to application topics using NLP. Annualized citations per $1 million funding (ACOF) and average APT score for award-associated articles, grouped by application topic. The APT score is a machine-learning based metric created by the NIH Office of Portfolio Analysis that quantifies the likelihood of future citation by a clinical article. A total of 16 629 NIH awards related to AI were included in the analysis, and 75 applications of AI were identified. Total annual funding for AI grew from $17.4 million in 1985 to $1.43 billion in 2020. By average APT, interpersonal communication technologies (0.488; 95% CI, 0.472-0.504) and population genetics (0.463; 95% CI, 0.453-0.472) had the highest translatability; environmental health (ACOF, 1038) and applications focused on the electronic health record (ACOF, 489) also had high translatability. The category of applications related to biochemical analysis was found to have low translatability by both metrics (average APT, 0.393; 95% CI, 0.388-0.398; ACOF, 246). Based on this study's findings, data on grants from the NIH can apparently be used to identify and characterize medical applications of AI to understand changes in academic productivity, funding support, and potential for translational impact. This method may be extended to characterize other research domains.

A Systematic Literature Review of Influencing Factors and Strategies of Artificial Intelligence Adoption in the Construction Industry

The construction industry has seen an increase in Artificial Intelligence(AI) in recent years, a paradigm shift in many industries. It puts under pressure for technological advancement. Therefore, AI is under great attention in the construction industry as a new strategic paver. This paper adopts a systematic literature review (SLR) approach and followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to observe and understand the influencing factors and strategies for AI adoption. The SLR for AI-related research carried out between 2010 and 2020. Data was collected from ASCE Journals, Emerald Insight, Elsevier ScienceDirect, Engineering Village, Google Scholar, ICE virtual library, IOPscience, IEEE Xplore, ScienceDirect, Scopus, SpringerLink and Taylor & Francis. The paper identifies and classifies the new developments in AI research, making its implementation and adoption a reality in the construction industry. This review has the potential for construction industry stakeholders, especially those in developing countries, to utilise the accumulated evidence from selected systematic reviews to enable the usage of AI for infrastructure development

Artificial intelligence-based computer vision in surgery: Recent advances and future perspectives.

Technology has advanced surgery, especially minimally invasive surgery (MIS), including laparoscopic surgery and robotic surgery. It has led to an increase in the number of technologies in the operating room. They can provide further information about a surgical procedure, e.g. instrument usage and trajectories. Among these surgery‐related technologies, the amount of information extracted from a surgical video captured by an endoscope is especially great. Therefore, the automation of data analysis is essential in surgery to reduce the complexity of the data while maximizing its utility to enable new opportunities for research and development. Computer vision (CV) is the field of study that deals with how computers can understand digital images or videos and seeks to automate tasks that can be performed by the human visual system. Because this field deals with all the processes of real‐world information acquisition by computers, the terminology “CV” is extensive, and ranges from hardware for image sensing to AI‐based image recognition. AI‐based image recognition for simple tasks, such as recognizing snapshots, has advanced and is comparable to humans in recent years. Although surgical video recognition is a more complex and challenging task, if we can effectively apply it to MIS, it leads to future surgical advancements, such as intraoperative decision‐making support and image navigation surgery. Ultimately, automated surgery might be realized. In this article, we summarize the recent advances and future perspectives of AI‐related research and development in the field of surgery.

Study on artificial intelligence: The state of the art and future prospects

In the world, the technological and industrial revolution is accelerating by the widespread application of new generation information and communication technologies, such as AI, IoT (the Internet of Things), and blockchain technology. Artificial intelligence has attracted much attention from government, industry, and academia. In this study, popular articles published in recent years that relate to artificial intelligence are selected and explored. This study aims to provide a review of artificial intelligence based on industry information integration. It presents an overview of the scope of artificial intelligence using background, drivers, technologies, and applications, as well as logical opinions regarding the development of artificial intelligence. This paper may play a role in AI-related research and should provide important insights for practitioners in the real world.The main contribution of this study is that it clarifies the state of the art of AI for future study.

Current and Future CONSIDERATIONS for the Use of Artificial Intelligence by the United States’ Department of Defense

Purpose: The “Narrow” AI technologies and its application to different sectors in the U.S. have brought tre-mendous advantages to the country and its citizens. This paper attempts to examine the opportunities and risks of AI’s potential application to the military health care sector. Yet, some human rights concerns remain. This paper further argues that the new Biden administration should pursue policies that reinforce a human-centric AI to be more supportive of human rights. Method: This paper employs the politics of responsibility theory to examine the present and future of AI re-sponsible governance in the Biden era. Based on resources from various AI related research by the U.S. gov-ernment, scholars, and experts, this paper further attempts to examine the opportunities and challenges in the ethical area that will likely be generated by applying AI to the American system of military health care. Results: This paper finds that the application of AI in military health care would likely provide immense op-portunities, especially in times of COVID-19. However, there are risks posed by the application of AI in various sectors as well as in military health care. Meanwhile, the new Biden administration’s future policymaking stage should emphasize ways in which AI may remain human-centric to be more supportive of human rights. Conclusion: In incorporating AI into virtually every economic sector and into daily life, the U.S. government, together with all sectors of society, should seek ways to maximize opportunities and minimize the risk of over-dependence or unregulated exposure to an intelligence far beyond the human level. The new Biden administra-tion should be at the forefront in promoting responsible AI policymaking to be more supportive of human rights.

AI applications in robotics, diagnostic image analysis and precision medicine: Current limitations, future trends, guidelines on CAD systems for medicine

AI in medicine has been recognized by both academia and industry in revolutionizing how healthcare services will be offered by providers and perceived by all stakeholders. We aim to review recent tendencies in building AI applications for medicine and foster its further development by outlining obstacles. Sub-objectives: (1) to highlight AI techniques that we have identified as key areas of AI-related research in healthcare; (2) to offer guidelines on building reliable AI-based CAD-systems for medicine; and (3) to reveal open research questions, challenges, and directions for future research. To address the tasks, we performed a systematic review of the references on the main branches of AI applications for medical purposes. We focused primarily on limitations of the reviewed studies. This study provides a summary of AI-related research in healthcare, it discusses the challenges and proposes open research questions for further research. Robotics has taken huge leaps in improving the healthcare services in a variety of medical sectors, including oncology and surgical interventions. In addition, robots are now replacing human assistants as they learn to become more sociable and reliable. However, there are challenges that must still be addressed to enable the use of medical robots in diagnostics and interventions. AI for medical imaging eliminates subjectivity in a visual diagnostic procedure and allows for the combining of medical imaging with clinical data, lifestyle risks and demographics. Disadvantages of AI solutions for radiology include both a lack of transparency and dedication to narrowed diagnostic questions. Designing an optimal automatic classifier should incorporate both expert knowledge on a disease and state-of-the-art computer vision techniques. AI in precision medicine and oncology allows for risk stratification due to genomics aberrations discovered on molecular testing. To summarize, AI cannot substitute a medical doctor. However, medicine may benefit from robotics, a CAD, and AI-based personalized approach.

Assessing perspectives on artificial intelligence applications to gastroenterology

Assessing perspectives on artificial intelligence applications to gastroenterology

Policy Approaches to Artificial Intelligence Based Technologies in China, European Union and the United States

This paper provides a summary survey of the policy approaches to artificial intelligence-based technologies in China, the European Union, and the United States. China has the most aggressive approach, launching major initiatives since 2015 such as ‘Made in China 2025’, the Internet Plus Plan, the New Generation Artificial Intelligence Development Plan, and the Artificial Intelligence Standardization White Paper. In 2018, the EU finalized both the European AI Strategy and ‘Made in Europe’ or the Coordinated Plan on the Development and Use of Artificial Intelligence. Despite a traditional reticence to adopt national strategies and perhaps pushed by growing concerns about China, the US Government announced the American AI Initiative and a National AI R&D Strategic Plan in 2019. The AI approaches in these three economies reflect their relative strengths—state control in China, citizen voice in Europe, and business practices in America. Unencumbered by privacy concerns, China’s strategy is geared to exploit the abundance of domestic data and to develop AI talent through central schemes and massive injections of money. The European Union’s regulations and spending priorities are guided by the objective of building citizen trust in AI-based technologies by safeguarding privacy and ameliorating disruptions in national labor markets. The mainstay of the US approach is to strengthen the linkages between business and AI-related research, and find ways to fund basic R&D. Despite efforts to indigenize AI innovations, all three economies face challenges. China’s AI strategy continues to rely disproportionately on just three tech giants: Baidu, Tencent and Alibaba, which have investments in more than a 100 AI-involved companies. Europe’s AI resources are unbalanced geographically—a quarter of Europe’s AI talent is in the UK and another quarter in Germany and France—and Brexit poses a serious risk. More than half of the AI talent in the US is foreign born, so immigration policies will inevitably be a central component of a national AI strategy.

Artificial Intelligence in Health Care: Bibliometric Analysis.

BackgroundAs a critical driving power to promote health care, the health care–related artificial intelligence (AI) literature is growing rapidly.ObjectiveThe purpose of this analysis is to provide a dynamic and longitudinal bibliometric analysis of health care–related AI publications.MethodsThe Web of Science (Clarivate PLC) was searched to retrieve all existing and highly cited AI-related health care research papers published in English up to December 2019. Based on bibliometric indicators, a search strategy was developed to screen the title for eligibility, using the abstract and full text where needed. The growth rate of publications, characteristics of research activities, publication patterns, and research hotspot tendencies were computed using the HistCite software.ResultsThe search identified 5235 hits, of which 1473 publications were included in the analyses. Publication output increased an average of 17.02% per year since 1995, but the growth rate of research papers significantly increased to 45.15% from 2014 to 2019. The major health problems studied in AI research are cancer, depression, Alzheimer disease, heart failure, and diabetes. Artificial neural networks, support vector machines, and convolutional neural networks have the highest impact on health care. Nucleosides, convolutional neural networks, and tumor markers have remained research hotspots through 2019.ConclusionsThis analysis provides a comprehensive overview of the AI-related research conducted in the field of health care, which helps researchers, policy makers, and practitioners better understand the development of health care–related AI research and possible practice implications. Future AI research should be dedicated to filling in the gaps between AI health care research and clinical applications.

Understanding hierarchical structural evolution in a scientific discipline: A case study of artificial intelligence

Detecting what type of knowledge constitutes a discipline, tracking how the knowledge changes, and understanding why the changes are triggered are the key issues in analyzing scientific development from a macro perspective, which is usually analyzed by the topic of evolution. However, traditional methods assume that the disciplinary structure is flat with only one-layer topics, rather than a tree-like structure with hierarchical topics, which leads to the inability of existing methods to effectively examine the details of the evolution, such as the interactions between different research directions. In this paper, we take artificial intelligence (AI) as a case in which we study its hierarchical structural evolution, more precisely inspecting disciplinary development, by analyzing 65,887 AI-related research papers published during a 10-year period from 2009 to 2018. From a hierarchical topic model that can construct a topic-tree with multi-layer organizations, we design a visual analysis model for the topic-tree to systematically and visually investigate how knowledge transfers along the topic-tree and how the topic-tree changes over time. Moreover, some assistant indicators are employed to help in the exploration of the complicated structural evolution. Then, we discover the latent relationship between the sub-structures within a topic as well as the triggering reason for the knowledge migration. Based on these results, we conclude that different topics have different development patterns and that the recent artificial intelligence revolution stems from the interactions among the different topics.

P071: Artificial intelligence in emergency medicine: A scoping review

Introduction: The study of artificial intelligence (AI) in medicine has become increasingly popular over the last decade. The emergency department (ED) is uniquely situated to benefit from AI due to its power of diagnostic prediction, and its ability to continuously improve with time. However, there is a lack of understanding of the breadth and scope of AI applications in emergency medicine, and evidence supporting its use. Methods: Our scoping review was completed according to PRISMA-ScR guidelines and was published a priori on Open Science Forum. We systematically searched databases (Medline-OVID, EMBASE, CINAHL, and IEEE) for AI interventions relevant to the ED. Study selection and data extraction was performed independently by two investigators. We categorized studies based on type of AI model used, location of intervention, clinical focus, intervention sub-type, and type of comparator. Results: Of the 1483 original database citations, a total of 181 studies were included in the scoping review. Inter-rater reliability for study screening for titles and abstracts was 89.1%, and for full-text review was 77.8%. Overall, we found that 44 (24.3%) studies utilized supervised learning, 63 (34.8%) studies evaluated unsupervised learning, and 13 (7.2%) studies utilized natural language processing. 17 (9.4%) studies were conducted in the pre-hospital environment, with the remainder occurring either in the ED or the trauma bay. The majority of interventions centered around prediction (n = 73, 40.3%). 48 studies (25.5%) analyzed AI interventions for diagnosis. 23 (12.7%) interventions focused on diagnostic imaging. 89 (49.2%) studies did not have a comparator to their AI intervention. 63 (34.8%) studies used statistical models as a comparator, 19 (10.5%) of which were clinical decision making tools. 15 (8.3%) studies used humans as comparators, with 12 of the 15 (80%) studies showing superiority in favour of the AI intervention when compared to a human. Conclusion: AI-related research is rapidly increasing in emergency medicine. AI interventions are heterogeneous in both purpose and design, but primarily focus on predictive modeling. Most studies do not involve a human comparator and lack information on patient-oriented outcomes. While some studies show promising results for AI-based interventions, there remains uncertainty regarding their superiority over standard practice, and further research is needed prior to clinical implementation.