Development Of Artificial Intelligence Technology Research Articles

Landscape and challenges in economic evaluations of artificial intelligence in healthcare: a systematic review of methodology

BackgroundThe potential for artificial intelligence (AI) to transform healthcare cannot be ignored, and the development of AI technologies has increased significantly over the past decade. Furthermore, healthcare systems are under tremendous pressure, and efficient allocation of scarce healthcare resources is vital to ensure value for money. Health economic evaluations (HEEs) can be used to obtain information about cost-effectiveness. The literature acknowledges that the conduct of such evaluations differs between medical technologies (MedTechs) and pharmaceuticals, and poor quality evaluations can provide misleading results. This systematic review seeks to map the evidence on the general methodological quality of HEEs for AI technologies to identify potential areas which can be subject to quality improvements. We used the 35-item checklist by Drummond and Jefferson and four additional checklist domains proposed by Terricone et al. to assess the methodological quality of full HEEs of interventions that include AI.ResultsWe identified 29 studies for analysis. The included studies had higher completion scores for items related to study design than for items related to data collection and analysis and interpretation of results. However, none of the studies addressed MedTech-specific items.ConclusionsThere was a concerningly low number of full HEEs relative to the number of AI publications, however the trend is that the number of studies per year is increasing. Mapping the evidence of the methodological quality of HEEs of AI shows a need to improve the quality in particular the use of proxy measures as outcome, reporting, and interpretation of the ICER.

MedExpQA: Multilingual benchmarking of Large Language Models for Medical Question Answering

Large Language Models (LLMs) have the potential of facilitating the development of Artificial Intelligence technology to assist medical experts for interactive decision support. This potential has been illustrated by the state-of-the-art performance obtained by LLMs in Medical Question Answering, with striking results such as passing marks in licensing medical exams. However, while impressive, the required quality bar for medical applications remains far from being achieved. Currently, LLMs remain challenged by outdated knowledge and by their tendency to generate hallucinated content. Furthermore, most benchmarks to assess medical knowledge lack reference gold explanations which means that it is not possible to evaluate the reasoning of LLMs predictions. Finally, the situation is particularly grim if we consider benchmarking LLMs for languages other than English which remains, as far as we know, a totally neglected topic. In order to address these shortcomings, in this paper we present MedExpQA, the first multilingual benchmark based on medical exams to evaluate LLMs in Medical Question Answering. To the best of our knowledge, MedExpQA includes for the first time reference gold explanations, written by medical doctors, of the correct and incorrect options in the exams. Comprehensive multilingual experimentation using both the gold reference explanations and Retrieval Augmented Generation (RAG) approaches show that performance of LLMs, with best results around 75 accuracy for English, still has large room for improvement, especially for languages other than English, for which accuracy drops 10 points. Therefore, despite using state-of-the-art RAG methods, our results also demonstrate the difficulty of obtaining and integrating readily available medical knowledge that may positively impact results on downstream evaluations for Medical Question Answering. Data, code, and fine-tuned models will be made publicly available.11https://huggingface.co/datasets/HiTZ/MedExpQA.

Application of artificial intelligence-based magnetic resonance imaging in diagnosis of cerebral small vessel disease.

Cerebral small vessel disease (CSVD) is an important cause of stroke, cognitive impairment, and other diseases, and its early quantitative evaluation can significantly improve patient prognosis. Magnetic resonance imaging (MRI) is an important method to evaluate the occurrence, development, and severity of CSVD. However, the diagnostic process lacks quantitative evaluation criteria and is limited by experience, which may easily lead to missed diagnoses and misdiagnoses. With the development of artificial intelligence technology based on deep learning, the extraction of high-dimensional features in imaging can assist doctors in clinical decision-making, and it has been widely used in brain function and mental disorders, and cardiovascular and cerebrovascular diseases. This paper summarizes the global research results in recent years and briefly describes the application of deep learning in evaluating CSVD signs in MRI imaging, including recent small subcortical infarcts, lacunes of presumed vascular origin, vascular white matter hyperintensity, enlarged perivascular spaces, cerebral microbleeds, brain atrophy, cortical superficial siderosis, and cortical cerebral microinfarct.

Bridging Motivation and AI in Education: An Activity Theory Perspective

After the pandemic, research on Artificial Intelligence (AI) in the field of education has seen a significant increase globally. Although this topic is not new to education, very few studies conducted before the pandemic addressed the problem of supporting internal motivation in students, crucial for the quality of learning and knowledge retention. This study explores the extent to which this topic is covered in recent research by conducting a cross-disciplinary literature review within the theoretical framework of Activity Theory (AT). It aims to identify the extent of coverage of all types of relationships between nodes in the educational activity system, with special attention to Subject (students) and Object, as this central relationship embodies the motive-driven nature of human activity. The analysis incorporated 69 articles from Scopus published from 2020 till present. The results demonstrate coverage of only some relationships: Subject-Tools (students interaction with AI technology), Tools-Object (development of AI technologies), Tools-Community (adapting AI within an educational community). The central relationship between Subject and Object remains unexplored. Practical implications involve developing and integrating AI tools to stimulate intrinsic motivation by emphasizing personal meaning. Theoretical outcomes involve exploring how to foster and sustain students' intrinsic motivation in relation to AI.

Transformation of Students' Career Orientation in the Era of Artificial Intelligence: A Systematic Literature Review

This research revolves around the challenges faced by students in aligning their career orientation with demands and changes, as many traditional jobs are threatened by AI technology. The aim of this study is to identify trends in the transformation of students' career orientation in the era of Artificial Intelligence (AI), map the career challenges for students in the AI era, analyze the skills and competencies required, and assess the role of educational institutions in supporting this career transformation. The research method applied in this study is a systematic literature review. The initial stages involve collecting literature sources from scholarly databases and proceeding with a screening process to select literature relevant to the research focus. Finally, in-depth analysis of selected literature is conducted to identify patterns, trends, and key points related to the research topic. The results of the study describe that the development of AI technology has a significant impact on students' career orientation in higher education. Furthermore, students also face career challenges such as competition with technology, uncertainty about future employment, and skills gaps. To address these challenges, students need to develop technical AI skills, ethical AI understanding, problem-solving abilities, and continuous learning skills to succeed in the AI job market. On the other hand, higher education institutions should play a proactive role in addressing these challenges by developing relevant curricula, organizing training sessions, collaborating with industries, and enhancing AI learning facilities. Further research is suggested to focus on the implementation of these strategies in the specific context of higher education institutions and evaluate their impact on student career preparedness in the evolving AI era.

The Important Role Of Artificial Intelligence Technology Regulation In Protecting The Public Interest

Artificial intelligence provides both good and bad. For this reason, artificial intelligence must be regulated to protect the public interest. The results of the study show that regulating artificial intelligence is not easy, very complicated, there are many challenges, moreover the development of artificial intelligence technology is very rapid while the law is slow to anticipate it. In 2022, globally there have been 37 regulations regulating artificial intelligence. From the results of the comparison of various best practices and regulations of other countries that are at the forefront in the field of artificial intelligence, such as the European Union, China, and the United States framework approach, it can be an input for the development of artificial intelligence regulations in Indonesia that ensure the responsible use of artificial intelligence, respect human values, and do not hinder the creation of an artificial intelligence development ecosystem.

Potential of artificial intelligence technologies in language training of future media specialists

Importance. Artificial intelligence (AI) technologies are widely utilized in the fields of journalism and marketing communications, including advertising and public relations. Within the realm of promotion, AI is integrated at various stages, ranging from analyzing the target audience and devising a communication strategy, to creating media products, and subsequently evaluating the success of the campaign. Given this context, it seems appropriate to incorporate AI-powered media products as a part of the curriculum for a foreign language class. This would enable students to gain insight into the latest developments in AI technology and its application in various industries.Research Methods. The research is based on recent developments in the area of artificial intelligence (AI) technologies in education. Specifically, it takes into account current experience that reflects the vectors and principles of using AI technologies in foreign language teaching for university students.Results and Discussion. The options for using ChatGPT and Midjourney neural networks in the development of media texts and advertising products as part of a foreign language course for future media professionals are presented.Conclusion. The neural networks’ use in the creation of media content and advertising materials within the context of the “Foreign Language” discipline contributes to the skills’ development in the main types of speech activity; the acquisition of the ability to utilize the learned foreign language as a tool for professional interaction in the media sector; and the enhancement of professional competencies within the field of media communication.

Improved faster region convolutional neural network algorithm for UAV target detection in complex environment

With the development of artificial intelligence technology and Unmanned Aerial Vehicle (UAV) technology, the traditional UAV target detection methods are difficult to achieve high detection accuracy in complex and changeable scenes. The paper selects Faster Region Convolutional Neural Network (Faster R-CNN) as the basic algorithm due to its high scalability and excellent classification performance. The convolution mode of the convolution layer in the network structure is adjusted to deformable convolution. Additionally, a hybrid attention mechanism module is added to combine channel attention and spatial attention modules behind the output layer of the Faster R-CNN network. Finally, the Soft Non-Maximum Suppression (Soft-NMS) method is selected to combine the improved Faster R-CNN algorithm of multiple individuals into the final target detection model. The performance of the improved Faster R-CNN algorithm and the original Faster R-CNN algorithm was verified through the VisDrone 2018-DET dataset and the full class average accuracy Mean Average Precision (mAP), accuracy and precision. The accuracy and logarithm of loss values of the improved Faster R-CNN algorithm and the original Faster R-CNN algorithm's Region Proposal Network (RPN) were 0.985, 0.981, 0.018, and 0.052, respectively. The target detection model based on the hybrid attention mechanism of ResNet-50 network fusion Spartial Attention Module (SAM) and Channel Attention Module (CAM) had the best classification performance, with the accuracy, precision and overall average accuracy of 25.8 %, 24.6 % and 22.7 %, respectively. The results are helpful to improve the target detection ability of UAV in complex environment, and contribute to the development of target detection technology in the future.

Онтологічний підхід в системі забезпечення безпеки використання IP-телефонії

In today's information world, the use of VoIP has become an attractive option for user communication. With the downward trend in paying for basic broadband services and the rapid increase in internet speeds, the use of VoIP should only continue to grow in popularity. However, as the use of VoIP increases, so do the potential threats to ordinary users. This article examines the peculiarities of organizing corporate VoIP telephony systems, highlights the main problems in information protection systems in VoIP telephony, and outlines ways to solve them. The development of methods for speech analysis and corresponding processing of natural language, which allows for creating more accurate and effective systems for detecting anomalous traffic and potentially dangerous communications, is especially relevant. With the continuous development of artificial intelligence technologies, the direction of using intelligent means for content analysis in the VoIP system is becoming interesting. A method for detecting anomalies in IP-telephony traffic, based on grouping VoIP messages through context-frequency analysis, is proposed. Additionally, a method for automated filling of the ontology of thematic messages in corporate IP-telephony systems is proposed, based on the formalized presentation of messages using tree-like structures and the description of interaction operations through tuple algebra. Furthermore, a software implementation for converting voice messages into text representations using the SpeechRecognition library for voice-to-text conversion in the Python programming language was created. Experimental studies of the proposed approaches were conducted, and a software subsystem for detecting anomalous messages based on the ontological approach was implemented in the current corporate IP-telephony system.

Analysis of the Application of Artificial Intelligence in Computer Network Technology

The application of artificial intelligence (AI) technology in computer network technology is becoming increasingly extensive and in-depth. This paper analyzes the application of artificial intelligence in network security, network management, Internet of Things, edge computing and network services in detail. In network management, AI technology realizes network traffic optimization, automatic configuration and fault prediction, and improves the efficiency of network management. In the field of Internet of Things, AI technology helps manage and analyze a large amount of device data, supporting the development of smart homes and smart cities. In edge computing, AI technology enhances the data processing capabilities of edge nodes and reduces latency. In network services, artificial intelligence technology optimizes routing, voice recognition and personalized recommendation services. However, the application of artificial intelligence in network technology also faces challenges such as data privacy, computing resources and laws and regulations. This paper aims to provide a comprehensive reference for industry insiders to promote the application and development of artificial intelligence technology in computer networks.

Analysis of the Application of Artificial Intelligence in Computer Network Technology

The application of artificial intelligence (AI) technology in computer network technology is becoming increasingly extensive and in-depth. This paper analyzes the application of artificial intelligence in network security, network management, Internet of Things, edge computing and network services in detail. In network management, AI technology realizes network traffic optimization, automatic configuration and fault prediction, and improves the efficiency of network management. In the field of Internet of Things, AI technology helps manage and analyze a large amount of device data, supporting the development of smart homes and smart cities. In edge computing, AI technology enhances the data processing capabilities of edge nodes and reduces latency. In network services, artificial intelligence technology optimizes routing, voice recognition and personalized recommendation services. However, the application of artificial intelligence in network technology also faces challenges such as data privacy, computing resources and laws and regulations. This paper aims to provide a comprehensive reference for industry insiders to promote the application and development of artificial intelligence technology in computer networks.

Entertainment robot based on IoT and VR interaction for motion training posture monitoring

With the development of artificial intelligence technology, the role of social robots in sports activities has gradually become prominent. Robots can help with intelligent control of sports venues, assist in motion guidance, and engage in emotional communication with users, helping athletes adjust their emotions. This article studies the motion training posture monitoring of entertainment robots based on the interaction between the Internet of Things and VR. A new high-intensity motion attitude monitoring system based on quantum dot photodetectors has been developed to improve monitoring accuracy and stability. Entertainment robots can simulate and analyze motion training postures, and provide relevant technical guidance. The quantum dot photodetectors were integrated into the sensor components, and the collected data was transmitted to the data acquisition and processing unit through signal transmission and processing modules. The collected data was processed and analyzed to achieve real-time monitoring and tracking of the target object’s attitude. Multiple experiments were conducted on the entire system, simulating various high-intensity exercise environments, and evaluating the performance parameters of the system. Through continuous optimization and improvement, the stability and accuracy of the system were ultimately ensured.

Experience in psychological counseling supported by artificial intelligence technology.

In recent years, artificial intelligence (AI) technology has been continuously advancing and finding extensive applications, with one of its core technologies, machine learning, being increasingly utilized in the field of healthcare. This research aims to explore the role of Artificial Intelligence (AI) technology in psychological counseling and utilize machine learning algorithms to predict counseling outcomes. Firstly, by employing natural language processing techniques to analyze user conversations with AI chatbots, researchers can gain insights into the psychological states and needs of users during the counseling process. This involves detailed analysis using text analysis, sentiment analysis, and other relevant techniques. Subsequently, machine learning algorithms are used to establish predictive models that forecast counseling outcomes and user satisfaction based on data such as user language, emotions, and behavior. These predictive results can assist counselors or AI chatbots in adjusting counseling strategies, thereby enhancing counseling effectiveness and user experience. Additionally, this study explores the potential and prospects of AI technology in the field of psychological counseling. The research findings indicate that the designed machine learning models achieve an accuracy rate of approximately 89% in analyzing psychological conditions. This demonstrates significant innovation and breakthroughs in AI technology. Consequently, AI technology will gradually become a highly important tool and method in the field of psychological counseling. In the future, AI chatbots will become more intelligent and personalized, providing users with precise, efficient, and convenient psychological counseling services. The results of this research provide valuable technical insights for further improving AI-supported psychological counseling, contributing positively to the application and development of AI technology.

TREND IN THE DEVELOPMENT OF ARTIFICIAL INTELLIGENCE IN THE WORLD

The article discusses current trends and prospects for the development of artificial intelligence technology. International research data and ratings of leading trends in the digital economy are analyzed. The volume of the global market for AI technologies and the leading countries in terms of the amount of investment in this area have been studied. The purpose of this article is to consider how artificial intelligence can improve the process. The article analyze various techniques that make positive changes to the practice of artificial intelligence and discuss the potential benefits, challenges and ethical issues associated with this development. Using examples of the successful implementation of artificial intelligences, the article shows how technology is changing the way of learning and people are thereby helping to reach new heights.

Open data and explanatory artificial intelligence: legal perspectives

The article comprehensively analyzes the legal issues of open data. The concept of “citizen-generated data” is explored, and its role, place and importance for legal regulation in the open data system are determined. The paper also describes the features that distinguish this category of data from similar “citizen science” and “public participation” data. The process of forming training datasets for the development of artificial intelligence technologies using open data is comprehended. The concept and features of “explanatory artificial intelligence” are investigated. It is established that the development of explanation algorithms should take into account the human thought processes and cognitive biases of the person who forms, perceives and evaluates the decisions of explanatory artificial intelligence. The use of open data in other areas, in particular for analyzing the indicators of the Sustainable Development Goals, is investigated. It was found that some of the data necessary for comparing SDG indicators are published by the same data managers on different platforms. This leads to duplication of some data, the need to transform or compile data for publication on the Open Data Portal and the Open SDG Platform in Ukraine.

Social entertainment robot based on artificial intelligence algorithm application in interactive mode of music classroom

With the rapid development of artificial intelligence technology, in-depth research on the application of social entertainment robots in the field of education has become increasingly important. Therefore, this study aims to explore how to use social entertainment robots based on artificial intelligence algorithms to improve the interactive mode in music classrooms, in order to enhance learning effectiveness and classroom atmosphere. The study introduced the detection algorithm of social entertainment robots and designed a complete detection process for accurately locating and identifying social entertainment robots in music classrooms. A machine data classification detection algorithm has been proposed to accurately determine the behavior and function of social entertainment robots. The social entertainment robot teaching module guides students to actively participate in music classroom activities through simulated conversations and interactive demonstrations. Interactive tests were conducted in actual music classrooms to evaluate the effectiveness of social entertainment robots in the classroom and student feedback. Through experiments, it has been proven that social entertainment robots based on artificial intelligence algorithms can effectively enhance the interactivity and fun of music classrooms. It not only promotes communication and cooperation among students, but also enhances learning participation.

A Fine-Grained Image Classification Method Built on MobileViT

With a rapid development of artificial intelligence technology, fine-grained image classification has gained widespread application. For mobile terminals, this paper introduces an image classification method built on MobileViT, and it can apply into fine-grained image classification. The original MobileViT model has been optimized in three ways. Initially, the h-swish activation function is used to enhance the network performance. Second, the cross-entropy loss function is used to further realize the parameter optimization and model accuracy improvement. Finally, a dropout layer is joined before the fully connected layer can effectively decrease the model recognition time and prevent over-fitting. Experimental data on public tomato disease datasets demonstrate that the improved fine-grained image classification method put forward in this paper exhibits higher classification accuracy, better stability and network generalization ability than other models.

Research on Architectural Design Based on Stable Diffusion

With the rapid development of artificial intelligence technology, its application in the field of architectural design has gradually become a research hotspot. In particular, Stable Diffusion technology shows its unique advantages and potential in the production of architectural renderings, which has attracted wide attention. In this paper, the application of Stable Diffusion technology in architectural creation is deeply discussed, aiming at providing new perspectives and ideas for architectural design and promoting the innovation and development of architectural design process.

Visual Recalibration and Gating Enhancement Network for Radiology Report Generation.

Automatic radiology medical report generation is a necessary development of artificial intelligence technology in the health care. This technology serves to aid doctors in producing comprehensive diagnostic reports, alleviating the burdensome workloads of medical professionals. However, there are some challenges in generating radiological reports: (1) visual and textual data biases and (2) long-distance dependency problem. To tackle these issues, we design a visual recalibration and gating enhancement network (VRGE), which composes of the visual recalibration module and the gating enhancement module (gating enhancement module, GEM). Specifically, the visual recalibration module enhances the recognition of abnormal features in lesion areas of medical images. The GEM dynamically adjusts the contextual information in the report by introducing gating mechanisms, focusing on capturing professional medical terminology in medical text reports. We have conducted sufficient experiments on the public datasets of IU X-Ray to illustrate that the VRGE outperforms existing models.

Understanding AI innovation contexts: a review and content analysis of artificial intelligence and entrepreneurial ecosystems research

PurposeAn emerging research stream focuses on the place-based ecosystems where artificial intelligence (AI) innovations emerge and develop. This literature builds on the contextual turn in management research and, specifically, work on entrepreneurial ecosystems. However, as a nascent research area, the literature on AI and entrepreneurial ecosystems is fragmented across academic and practitioner boundaries and unconnected disciplines because of disparate and ill-defined concepts. As a result, the literature is disorganized and its main insights are latent. The purpose of this paper is to synthesize research on AI ecosystems and identify the main insights.Design/methodology/approachWe first consolidate research on the “where” of AI innovation through a scoping review. To address the fragmentation in the literature and understand how entrepreneurial ecosystems are associated with AI innovation, we then use content analysis to explore the literature.FindingsWe identify the main characteristics of the AI and ecosystems literature and the key dimensions of “AI entrepreneurial ecosystems”: the local actors and factors in geographic territories that are coordinated to support the creation and development of AI technologies. We clarify the relationships among AI technologies and ecosystem dimensions and uncover the latent themes and underlying structure of research on AI entrepreneurial ecosystems.Originality/valueWe increase conceptual precision by introducing and defining an umbrella concept—AI entrepreneurial ecosystem—and propose a research agenda to spur further insights. Our analysis contributes to research at the intersection of management, information systems, and entrepreneurship and creates actionable insights for practitioners influenced by the geographic agglomeration of AI innovation.