Topics In Artificial Intelligence Research Articles

Artificial Intelligence in Neuroradiology: A Review of Current Topics and Competition Challenges.

There is an expanding body of literature that describes the application of deep learning and other machine learning and artificial intelligence methods with potential relevance to neuroradiology practice. In this article, we performed a literature review to identify recent developments on the topics of artificial intelligence in neuroradiology, with particular emphasis on large datasets and large-scale algorithm assessments, such as those used in imaging AI competition challenges. Numerous applications relevant to ischemic stroke, intracranial hemorrhage, brain tumors, demyelinating disease, and neurodegenerative/neurocognitive disorders were discussed. The potential applications of these methods to spinal fractures, scoliosis grading, head and neck oncology, and vascular imaging were also reviewed. The AI applications examined perform a variety of tasks, including localization, segmentation, longitudinal monitoring, diagnostic classification, and prognostication. While research on this topic is ongoing, several applications have been cleared for clinical use and have the potential to augment the accuracy or efficiency of neuroradiologists.

ARDA‐UNIT recurrent dense self‐attention block with adaptive feature fusion for unpaired (unsupervised) image‐to‐image translation

AbstractOne of the most challenging topics in artificial intelligence is image‐to‐image translation, the purpose of which is generating images close to those in the target domain while preserving the important features of the images in the source domain. In this direction, various types of generative adversarial networks have been developed. ARDA‐UNIT, presented in this paper, seeks to meet the main challenges of these networks, that is, producing a high‐quality image in a reasonable amount of time, and transferring content between two images with different structures. The proposed recurrent dense self‐attention block, applied in ARDA‐UNIT's generator latent space, simultaneously increases its generating capability and decreases the training parameters. ARDA‐UNIT has a feature extraction module which feeds both the generator and the discriminator. This module uses a new adaptive feature fusion method which combines multi‐scale features in such a way that the characteristics of each scale are preserved. The module also uses a pre‐trained CNN that reduces the training parameters. Moreover, a feature similarity loss is introduced that guides the model to change the structure of the source domain in accordance with that in the target domain. Experiments performed on different datasets using FID, KID and IS evaluation criteria have shown that the model reduces computational loads, transfers structures well, and achieves better qualities.

Curriculum frameworks and educational programs in artificial intelligence for medical students, residents, and practicing physicians: a scoping review protocol

Objective:The aim of this scoping review is to synthesize knowledge from the literature on curriculum frameworks and current educational programs that focus on the teaching and learning of artificial intelligence (AI) for medical students, residents, and practicing physicians.Introduction:To advance the implementation of AI in clinical practice, physicians need to have a better understanding of AI and how to use it within clinical practice. Consequently, medical education must introduce AI topics and concepts into the curriculum. Curriculum frameworks are educational road maps to teaching and learning. Therefore, any existing AI curriculum frameworks must be reviewed and, if none exist, such a framework must be developed.Inclusion criteria:This review will include articles that describe curriculum frameworks for teaching and learning AI in medicine, irrespective of country. All types of articles and study designs will be included, except conference abstracts and protocols.Methods:This review will follow the JBI methodology for scoping reviews. Keywords will first be identified from relevant articles. Another search will then be conducted using the identified keywords and index terms. The following databases will be searched: MEDLINE (Ovid), Embase (Ovid), Cochrane Central Register of Controlled Trials (CENTRAL), CINAHL (EBSCOhost), and Scopus. Gray literature will also be searched. Articles will be limited to the English and French languages, commencing from the year 2000. The reference lists of all included articles will be screened for additional articles. Data will then be extracted from included articles and the results will be presented in a table.

Artificial Intelligence Teaching as Part of Medical Education: Qualitative Analysis of Expert Interviews.

The use of artificial intelligence (AI) in medicine is expected to increase significantly in the upcoming years. Advancements in AI technology have the potential to revolutionize health care, from aiding in the diagnosis of certain diseases to helping with treatment decisions. Current literature suggests the integration of the subject of AI in medicine as part of the medical curriculum to prepare medical students for the opportunities and challenges related to the use of the technology within the clinical context. We aimed to explore the relevant knowledge and understanding of the subject of AI in medicine and specify curricula teaching content within medical education. For this research, we conducted 12 guideline-based expert interviews. Experts were defined as individuals who have been engaged in full-time academic research, development, or teaching in the field of AI in medicine for at least 5 years. As part of the data analysis, we recorded, transcribed, and analyzed the interviews using qualitative content analysis. We used the software QCAmap and inductive category formation to analyze the data. The qualitative content analysis led to the formation of three main categories ("Knowledge," "Interpretation," and "Application") with a total of 9 associated subcategories. The experts interviewed cited knowledge and an understanding of the fundamentals of AI, statistics, ethics, and privacy and regulation as necessary basic knowledge that should be part of medical education. The analysis also showed that medical students need to be able to interpret as well as critically reflect on the results provided by AI, taking into account the associated risks and data basis. To enable the application of AI in medicine, medical education should promote the acquisition of practical skills, including the need for basic technological skills, as well as the development of confidence in the technology and one's related competencies. The analyzed expert interviews' results suggest that medical curricula should include the topic of AI in medicine to develop the knowledge, understanding, and confidence needed to use AI in the clinical context. The results further imply an imminent need for standardization of the definition of AI as the foundation to identify, define, and teach respective content on AI within medical curricula.

Artificial Intelligence for Upper Aerodigestive Tract Endoscopy and Laryngoscopy: A Guide for Physicians and State-of-the-Art Review.

The endoscopic and laryngoscopic examination is paramount for laryngeal, oropharyngeal, nasopharyngeal, nasal, and oral cavity benign lesions and cancer evaluation. Nevertheless, upper aerodigestive tract (UADT) endoscopy is intrinsically operator-dependent and lacks objective quality standards. At present, there has been an increased interest in artificial intelligence (AI) applications in this area to support physicians during the examination, thus enhancing diagnostic performances. The relative novelty of this research field poses a challenge both for the reviewers and readers as clinicians often lack a specific technical background. Four bibliographic databases were searched: PubMed, EMBASE, Cochrane, and Google Scholar. A structured review of the current literature (up to September 2022) was performed. Search terms related to topics of AI, machine learning (ML), and deep learning (DL) in UADT endoscopy and laryngoscopy were identified and queried by 3 independent reviewers. Citations of selected studies were also evaluated to ensure comprehensiveness. Forty-one studies were included in the review. AI and computer vision techniques were used to achieve 3 fundamental tasks in this field: classification, detection, and segmentation. All papers were summarized and reviewed. This article comprehensively reviews the latest developments in the application of ML and DL in UADT endoscopy and laryngoscopy, as well as their future clinical implications. The technical basis of AI is also explained, providing guidance for nonexpert readers to allow critical appraisal of the evaluation metrics and the most relevant quality requirements.

Reasoning about causality in games

Causal reasoning and game-theoretic reasoning are fundamental topics in artificial intelligence, among many other disciplines: this paper is concerned with their intersection. Despite their importance, a formal framework that supports both these forms of reasoning has, until now, been lacking. We offer a solution in the form of (structural) causal games, which can be seen as extending Pearl's causal hierarchy to the game-theoretic domain, or as extending Koller and Milch's multi-agent influence diagrams to the causal domain. We then consider three key questions:i)How can the (causal) dependencies in games – either between variables, or between strategies – be modelled in a uniform, principled manner?ii)How may causal queries be computed in causal games, and what assumptions does this require?iii)How do causal games compare to existing formalisms? To address question i), we introduce mechanised games, which encode dependencies between agents' decision rules and the distributions governing the game. In response to question ii), we present definitions of predictions, interventions, and counterfactuals, and discuss the assumptions required for each. Regarding question iii), we describe correspondences between causal games and other formalisms, and explain how causal games can be used to answer queries that other causal or game-theoretic models do not support. Finally, we highlight possible applications of causal games, aided by an extensive open-source Python library.

The Impact of Quantum Computing on Cryptography

Abstract: The purpose of this paper's abstract is to explain how quantum computing works in terms of current cryptography and to provide the reader a rudimentary understanding of post-quantum algorithms. Community key encoding methods affected, symmetric structures affected, the influence on hash purposes, upright quantum cryptography, distinctions amongst quantum and standard computing, obstacles in quantum computation, and quantum procedures (Shor's and Grover's). The PostQuantum Cryptography section specifically discusses various mathematically based quantum crucial circulation techniques, lattice-built cryptography, multivariate-built cryptography, hash-based signs, and code-based encoding. One of the modern technologies in today's society is quantum computation. The advance of quantum computing applications is the focus of numerous communities and research institutions worldwide. Another developing field at the moment that is becoming stable is artificial intelligence. The major goal of this work is to determine the effects of the development of quantum computing research on applications involving artificial intelligence. Hence, computational methods are utilised in the study's methodology. So that this study's findings about the expanding impact of quantum computing research for a particular application of artificial intelligence can be drawn. The impact and potential of quantum computing on the subject of artificial intelligence is also discussed in this study, along with how quantum computing affects that discipline.

Facial Expression Recognition Based on Pruning Optimization Technology

Facial expression recognition is based on deep learning, which has become one of the main topics in artificial intelligence. Nevertheless, the complex network structure causes numerous parameter redundancy, which leads to low recognition efficiency. Therefore, researching how to reduce parameter redundancy makes sense. In addition, today's machine expression recognition has some limitations and constraints, while the generalization ability is poor. Aiming at addressing the problems that excessive parameter redundancy brings, for example, low efficiency and massive limitations. This paper will put forward a facial expression recognition technology that is based on pruning optimization to improve existing models. Based on VGGNet, ResNet and SENet, these three convolutional neural networks modify the convolution layer of the network, then add the attention module and pruning optimization. Through face detection, face area feature representation, expression recognition, and other steps. Realize the recognition of multiple facial expressions. The CK+ dataset and JAFFE dataset are independently divided into 4/5 for training and 1/5 for testing. Experimental results show that after inserting CBAM, except ResNet18, the accuracy of the other two networks all improved, SENet18 improved the most significantly. After pruning, the accuracy and speed of VGG16 and ResNet18 improved, but the accuracy of SENet18 decreased. In summary, the optimized model has good applicability and generalization ability.

Understanding Prospective Physicians' Intention to Use Artificial Intelligence in Their Future Medical Practice: Configurational Analysis.

Prospective physicians are expected to find artificial intelligence (AI) to be a key technology in their future practice. This transformative change has caught the attention of scientists, educators, and policy makers alike, with substantive efforts dedicated to the selection and delivery of AI topics and competencies in the medical curriculum. Less is known about the behavioral perspective or the necessary and sufficient preconditions for medical students' intention to use AI in the first place. Our study focused on medical students' knowledge, experience, attitude, and beliefs related to AI and aimed to understand whether they are necessary conditions and form sufficient configurations of conditions associated with behavioral intentions to use AI in their future medical practice. We administered a 2-staged questionnaire operationalizing the variables of interest (ie, knowledge, experience, attitude, and beliefs related to AI, as well as intention to use AI) and recorded 184 responses at t0 (February 2020, before the COVID-19 pandemic) and 138 responses at t1 (January 2021, during the COVID-19 pandemic). Following established guidelines, we applied necessary condition analysis and fuzzy-set qualitative comparative analysis to analyze the data. Findings from the fuzzy-set qualitative comparative analysis show that the intention to use AI is only observed when students have a strong belief in the role of AI (individually necessary condition); certain AI profiles, that is, combinations of knowledge and experience, attitudes and beliefs, and academic level and gender, are always associated with high intentions to use AI (equifinal and sufficient configurations); and profiles associated with nonhigh intentions cannot be inferred from profiles associated with high intentions (causal asymmetry). Our work contributes to prior knowledge by showing that a strong belief in the role of AI in the future of medical professions is a necessary condition for behavioral intentions to use AI. Moreover, we suggest that the preparation of medical students should go beyond teaching AI competencies and that educators need to account for the different AI profiles associated with high or nonhigh intentions to adopt AI.

Editorial note of the special issue on emerging topics in artificial intelligence selected from IEA/AIE2021

Editorial note of the special issue on emerging topics in artificial intelligence selected from IEA/AIE2021

Healthcare Professionals' Expectations of Medical Artificial Intelligence and Strategies for its Clinical Implementation: A Qualitative Study.

Although medical artificial intelligence (AI) systems that assist healthcare professionals in critical care settings are expected to improve healthcare, skepticism exists regarding whether their potential has been fully actualized. Therefore, we aimed to conduct a qualitative study with physicians and nurses to understand their needs, expectations, and concerns regarding medical AI; explore their expected responses to recommendations by medical AI that contradicted their judgments; and derive strategies to implement medical AI in practice successfully. Semi-structured interviews were conducted with 15 healthcare professionals working in the emergency room and intensive care unit in a tertiary teaching hospital in Seoul. The data were interpreted using summative content analysis. In total, 26 medical AI topics were extracted from the interviews. Eight were related to treatment recommendation, seven were related to diagnosis prediction, and seven were related to process improvement. While the participants expressed expectations that medical AI could enhance their patients' outcomes, increase work efficiency, and reduce hospital operating costs, they also mentioned concerns regarding distortions in the workflow, deskilling, alert fatigue, and unsophisticated algorithms. If medical AI decisions contradicted their judgment, most participants would consult other medical staff and thereafter reconsider their initial judgment. Healthcare professionals wanted to use medical AI in practice and emphasized that artificial intelligence systems should be trustworthy from the standpoint of healthcare professionals. They also highlighted the importance of alert fatigue management and the integration of AI systems into the workflow.

Robotic Process Automation (RPA) – Bibliometric Analysis and Literature Review

Abstract This article aims to identify the main approach to robotic process automation (RPA) method and trends in the scientific literature in the field of RPA. Moreover, this analysis is needed to build a model of automation of a certain class of business processes. The author focuses on an analysis of publications from 2012 to 2022, collected from the Web of Science, Scopus, and IEEE Xplore databases. The methodology includes source selection, main search limited to RPA, narrowing criteria, adding criteria for subject area, cluster analysis, and limitation to RPA shortcut to achieve most to the point results. The study presents analysis of publication year, type of publication, cluster analysis, as well as the most cited articles. The cluster analysis allowed identification of four primary areas for which publication analysis was conducted. The main value resulting from the creation of this article is the cluster analysis of the most cited publications in the context of RPA seen in the topics of artificial intelligence and digital transformation. On the top of that, it proves how well RPA is defined as a method and whether description of the RPA implementation model is available.

The Impact of Artificial Intelligence on Employment: Evidence in Africa

The purpose of this research paper is to examine the effect of artificial intelligence on employment in Africa. To accomplish this, the study employs a convenience sampling technique across 42 countries out of 54 African countries from 2012 to 2021 and utilizes a two-step system known as the Generalized Method of Moments (GMM) to estimate the impact of artificial intelligence on employment. In the course of the research, two models are specified to determine the influence of artificial intelligence on employment and examine its impact in the absence of a human development index. To analyze the research data, descriptive statistics and regression analysis are employed. The findings suggest that artificial intelligence has a strong positive impact on employment, while revenue demonstrates a moderate positive effect. On the other hand, political stability appears to have a negative impact. The paper makes a valuable and timely contribution to the existing literature on the subject of artificial intelligence’s impact on employment in Africa. The paper concludes by providing suggestions on how to effectively harness AI’s creative effects to counteract its negative consequences and utilize AI to reduce corruption and create sustainable employment for African economies.

Artificial intelligence (AI) integration in medical education: A pan-India cross-sectional observation of acceptance and understanding among students

Background/Aim: From accurate diagnostics to personalised treatment plans, artificial intelligence (AI) has the potential to revolutionise healthcare. The abundance of medical data has led to AI being employed for accurate diagnoses, treatment protocols and patient care. Students' perception of AI integration in medical education is crucial for its successful implementation. This study aimed to assess the acceptance and understanding of AI integration among students in medical education across different regions of India through a cross-sectional observation. Methods: A pan-India survey was conducted among medical students between 1 August 2023 to 20 August 2023 with a pre-validated questionnaire covering AI awareness and understanding through Google Form, circulated via WhatsApp. Results: A total of 730 medical students completed the survey of which 58.6 % were males and 41.4 % were females. Most students (80.7 %) knew about AI, but 53.6 % had limited awareness of AI in medicine. Opinions on AI integration was diverse, with 46.8 % in favour. Workshops (45.2 %) and lectures (31.1 %) were preferred learning formats. Students were interested in various AI topics and expect AI to positively impact medicine (45.9 %). Radiology, surgery and general medicine were predicted to be most influenced by AI. Concerns about overreliance on AI (49.2 %) and lack of empathy (43.7 %) were highlighted. Conclusions: Medical students in India display a keen interest in AI and its integration into medical education. To fully harness AI's potential in healthcare, comprehensive AI curricula and faculty training are needed. Students are aware of the challenges and opportunities, emphasising the importance of balanced AI adoption in medical practice and education.

ETHICS IN AI AND MACHINE LEARNING

As Artificial Intelligence (AI) and Machine Learning (ML) technology maintain their speedy evolution, the moral issues surrounding their development, deployment, and impact on society have emerge as more and more paramount. This review paper gives a comprehensive examination of the multifaceted dimensions of ethics in AI and ML, exploring the moral challenges, frameworks, and rising traits that shape the ethical discourse in this dynamic subject. The paper begins by means of scrutinizing the moral implications embedded within the design and improvement stages of AI and ML structures. It delves into troubles of bias, equity, and duty, highlighting how algorithmic choices can unintentionally perpetuate existing inequalities. The examination extends to the ethical duties of researchers, developers, and corporations, emphasizing the need for obvious, responsible, and socially accountable AI practices. The societal effect of AI and ML technology is a relevant subject, with a focal point on ethical considerations in regions inclusive of healthcare, crook justice, finance, and autonomous structures. The paper explores the delicate stability between technological innovation and safeguarding fundamental human rights, privations, and dignity. It additionally addresses the moral demanding situations posed through using AI in influencing public opinion, political methods, and the potential for accidental societal consequences. The review seriously analyses present ethical frameworks and suggestions proposed through academia, industry, and regulatory bodies. It evaluates their effectiveness in addressing the evolving challenges posed by means of AI and ML. Special attention is given to the rising subject of explainable AI and the position of transparency in fostering ethical practices.

Machine Learning and Artificial Intelligence as Educational Games

Digital games are establishing themselves as a new paradigm in teaching. Everyone can play digital games, they're economical, and they're a terrific way to learn. Digital games that encourage computational thinking and programming have recently attracted more attention in pre-college (K–12) educational institutions. A growing number of students have been drawn to the subjects of In recent years, there has been an increase in artificial intelligence (AI) and machine learning (ML). Researchers in teaching and learning are interested in the integration of AI/ML with digital gaming, however there hasn't been a literature review in this field. This paper aims to summarize the most recent findings on instructional video games for AI and ML. A qualitative content analysis was conducted using the relevant essays and games that were selected following a comprehensive search. Using this summary as a base, we present a list of research publications and games that are relevant. illustrating the unique opportunities that diverse games offer to teach various AI and ML techniques and topics.

Guest Editorial for Special Section on the 16th International Conference on Intelligent Computing (ICIC)

The eight papers in this special section were presented at the Sixteenth International Conference on Intelligent Computing (ICIC) that was held in Bari, Italy, on October 2-5, 2020. ICIC was formed to provide an annual forum dedicated to the emerging and challenging topics in artificial intelligence, machine learning, bioinformatics, and computational biology, etc. It aims to bring together researchers and practitioners from both academia and industry to share ideas, problems and solutions related to the multifaceted aspects of intelligent computing.

ARTIFICIAL INTELLIGENCE IN DISTANCE EDUCATION: A SYSTEMATIC LITERATURE REVIEW OF BRAZILIAN STUDIES

Artificial Intelligence (AI) is changing the way people live in society. New technologies powered by AI have been applied in different sectors of the economy and the educational context is no different. AI has been considered a key to the development of learning strategies, especially in distance education. In this sense, this research aimed to identify the current state of Brazilian literature on AI applied to distance education. The Higher Education market in Brazil, which is the biggest in Latin America regarding the number of individuals able to enroll in a program, is still developing and distance education has grown rapidly. To reach the purpose of this paper, it was performed a Systematic Literature Review (SLR) to find the research conducted in graduate programs that investigate the subject of AI applied to distance education. The final analysis used a total of 63 studies – 26 master’s theses and 37 doctoral dissertations. The main results show that most of the research on AI in distance education in Brazil was conducted in Computer Science (56%) and Engineering (27%). Only 6% of the studies reviewed are from masters’ or doctoral programs in Education. The result also shows that limited attention is paid to critical topics related to the growing introduction of AI in distance education, as such teachers’ employability and technological training or the ethical implications of using AI for the educational process. As a result of this SLR, it was possible to suggest research opportunities considering the international agenda on AI. Keywords: artificial intelligence, distance education, educational technology, systematic literature review

Perceptions of US Medical Students on Artificial Intelligence in Medicine: Mixed Methods Survey Study.

Given the rapidity with which artificial intelligence is gaining momentum in clinical medicine, current physician leaders have called for more incorporation of artificial intelligence topics into undergraduate medical education. This is to prepare future physicians to better work together with artificial intelligence technology. However, the first step in curriculum development is to survey the needs of end users. There has not been a study to determine which media and which topics are most preferred by US medical students to learn about the topic of artificial intelligence in medicine. We aimed to survey US medical students on the need to incorporate artificial intelligence in undergraduate medical education and their preferred means to do so to assist with future education initiatives. A mixed methods survey comprising both specific questions and a write-in response section was sent through Qualtrics to US medical students in May 2021. Likert scale questions were used to first assess various perceptions of artificial intelligence in medicine. Specific questions were posed regarding learning format and topics in artificial intelligence. We surveyed 390 US medical students with an average age of 26 (SD 3) years from 17 different medical programs (the estimated response rate was 3.5%). A majority (355/388, 91.5%) of respondents agreed that training in artificial intelligence concepts during medical school would be useful for their future. While 79.4% (308/388) were excited to use artificial intelligence technologies, 91.2% (353/387) either reported that their medical schools did not offer resources or were unsure if they did so. Short lectures (264/378, 69.8%), formal electives (180/378, 47.6%), and Q and A panels (167/378, 44.2%) were identified as preferred formats, while fundamental concepts of artificial intelligence (247/379, 65.2%), when to use artificial intelligence in medicine (227/379, 59.9%), and pros and cons of using artificial intelligence (224/379, 59.1%) were the most preferred topics for enhancing their training. The results of this study indicate that current US medical students recognize the importance of artificial intelligence in medicine and acknowledge that current formal education and resources to study artificial intelligence-related topics are limited in most US medical schools. Respondents also indicated that a hybrid formal/flexible format would be most appropriate for incorporating artificial intelligence as a topic in US medical schools. Based on these data, we conclude that there is a definitive knowledge gap in artificial intelligence education within current medical education in the US. Further, the results suggest there is a disparity in opinions on the specific format and topics to be introduced.

International comparison of cross-disciplinary integration in industry 4.0: A co-authorship analysis using academic literature databases.

In innovation strategy, a type of Schumpeterian competitive strategy in business administration, "intra-individual diversity" has attracted attention as one factor for creating innovation. In this study, we redefine "framework for identifying researchers’ areas of expertise" as "a framework for quantifying intra-individual diversity among researchers. Note that diversity here refers to authorship of articles in multiple research fields. The application of this framework then made it possible to visualize organizational diversity by accumulating the intra-individual diversity of researchers and to discuss the innovation strategy of the organization. The analysis in this study discusses how countries are promoting research on the topics of artificial intelligence (AI), big data, and Internet of Things (IoT) technologies, which are at the core of Industry 4.0, from an innovation perspective. Note that Industry 4.0 is a technological framework that aims to “improve the efficiency of all social systems,” “create new industries,” and “increase intellectual productivity.” For the analysis, we used 19-year bibliographic data (2000–2018) from the top 20 countries in terms of the number of papers in AI, big data, and IoT technologies. As the results, this study classified the styles of cross-disciplinary fusion into four patterns in AI and three patterns in big data. This study did not consider the results in IoT because of only small differences between countries. Furthermore, regional differences in the style of cross-disciplinary fusion were also observed, and the global innovation patterns in Industry 4.0 were classified into seven categories. In Europe and North America, the cross-disciplinary integration style was similar to that between the United States, Germany, the Netherlands, Spain, England, Italy, Canada, and France. In Asia, the cross-disciplinary fusion style was similar between China, Japan, and South Korea.