Artificial Intelligence Capabilities Research Articles

Macroergonomic components of the patient work system shaping dyadic care management during adolescence: a case study of type 1 diabetes.

The role of the social, physical, and organisational environments in shaping how patients and their caregivers perform work remains largely unexplored in human factors/ergonomics literature. This study recruited 19 dyads consisting of a parent and their child with type 1 diabetes to be interviewed individually and analysed using a macroergonomic framework. Our findings aligned with the macroergonomic factors as presented in previous models, while highlighting the need to expand upon certain components to gain a more comprehensive representation of the patient work system as relevant to dyadic management. Examples of design efforts that should follow from these findings include expanding existing data sharing options to include information from the external environment and capitalising on the capabilities of artificial intelligence as a decision support system. Future research should focus on longitudinally assessing patient work systems throughout transition periods in addition to more explicitly exploring the roles of social network members.

Artificial Intelligence in Enhancing the Kosovo Health Information System

The contemporary healthcare landscape faces unprecedented challenges, ranging from data fragmentation within health information systems to the need for timely and accurate diagnostics. This research explores the transformative potential of Artificial Intelligence (AI) in enhancing the Kosovo Health Information System (HIS). By leveraging the capabilities of AI, we aim to address existing limitations in data interoperability, predictive analytics, and personalized healthcare. The study incorporates a comprehensive literature review, methodological data collection, and analysis of the current state of the Kosovo HIS. Drawing inspiration from successful global implementations, we delve into the possibilities of AI applications in diagnosis, treatment personalization, and population health management. The research also examines ongoing initiatives and collaborations aimed at integrating AI into the Kosovo HIS. Through a critical assessment of technical challenges and ethical considerations, the paper provides insights into the opportunities and hurdles associated with the implementation of AI in healthcare. Ultimately, this research contributes to the discourse on the future prospects of healthcare in Kosovo, highlighting the potential long-term impacts of AI integration and offering recommendations for advancing the country's health information infrastructure.   Received: 5 February 2024 / Accepted: 23 April 2024 / Published: 5 May 2024

Comparative analysis of diagnostic accuracy in endodontic assessments: dental students vs. artificial intelligence.

This study evaluates the comparative diagnostic accuracy of dental students and artificial intelligence (AI), specifically a modified ChatGPT 4, in endodontic assessments related to pulpal and apical conditions. The findings are intended to offer insights into the potential role of AI in augmenting dental education. Involving 109 dental students divided into junior (54) and senior (55) groups, the study compared their diagnostic accuracy against ChatGPT's across seven clinical scenarios. Juniors had the American Association of Endodontists (AEE) terminology assistance, while seniors relied on prior knowledge. Accuracy was measured against a gold standard by experienced endodontists, using statistical analysis including Kruskal-Wallis and Dwass-Steel-Critchlow-Fligner tests. ChatGPT achieved significantly higher accuracy (99.0 %) compared to seniors (79.7 %) and juniors (77.0 %). Median accuracy was 100.0 % for ChatGPT, 85.7 % for seniors, and 82.1 % for juniors. Statistical tests indicated significant differences between ChatGPT and both student groups (p<0.001), with no notable difference between the student cohorts. The study reveals AI's capability to outperform dental students in diagnostic accuracy regarding endodontic assessments. This underscores AIs potential as areference tool that students could utilize to enhance theirunderstanding and diagnostic skills. Nevertheless, the potential for overreliance on AI, which may affect the development of critical analytical and decision-making abilities, necessitates a balanced integration of AI with human expertise and clinical judgement in dental education. Future research is essential to navigate the ethical and legal frameworks for incorporating AI tools such as ChatGPT into dental education and clinical practices effectively.

Designing and Analysing an APP based on "Internet+" for Integrating Health Data of University Physical Classes

INTRODUCTION: University physical education programs still largely use traditional methods without significant innovation in teaching or health evaluation. With the growing capabilities of Internet technology and artificial intelligence, there's a critical need to leverage these advancements to enhance the physical health assessments of college students.OBJECTIVES: The study proposes an integrated APP design for health data collection and analysis both inside and outside physical education classes, utilizing Internet technologies and intelligent learning algorithms. This is aimed at precisely analyzing and improving the health outcomes of university students by fostering more tailored and responsive physical education experiences..METHODS: The method involves constructing an app design analysis index system, integrating a vulture search heuristic optimization algorithm with a convolutional neural network (CNN). This setup uses smart sports APP behavioral data as input to refine and optimize health data integration, aiming to enhance the analysis and feedback mechanisms within university sports programs.RESULTS: The implementation of this method showed that it meets real-time requirements while significantly improving the accuracy and efficiency of integrated APP design analyses for health data. The use of smart algorithms allows for more precise adjustments and feedback in physical education programs, suggesting a substantial improvement over traditional physical health monitoring and evaluation methods.CONCLUSION: The proposed APP design successfully integrates and analyzes health data, enhancing the management and evaluation of physical education programs. It represents a significant step forward in utilizing modern technology to address the stagnation in physical education health monitoring, with potential implications for broader educational and health management practices in universities. Future iterations of the APP could incorporate more diverse data inputs and advanced analytical features to further refine its effectiveness and usability.

The Evolution of Consumer Adoption in AI-Enhanced Social Commerce

The Evolution of Consumer Adoption in AI-Enhanced Social Commerce

Investigating awareness of artificial intelligence in healthcare among medical students and professionals in Pakistan: a cross-sectional study.

The purpose of this study is to find out the level of awareness and acceptance of artificial intelligence (AI) in Pakistan's medical community so as to comment on its future in our healthcare system. A survey consisting of 15 close-ended questions was conducted. The questions inquired about awareness about AI and discovered the opinions of healthcare professionals regarding its benefits and expected problems. The data were analyzed using SPSS version 26, and descriptive statistics for percentage and frequency were computed. χ2 test was used to analyze the subgroups (Significant p value <0.05). A total of 351 participants were included in this study. General familiarity with AI was low. Only 75 (21.3%) participants answered that they had good familiarity with AI, and only 56 (16%) of them had good familiarity with the role of AI in medicine. One hundred sixty-eight (47.9%) participants disagreed that AI would out-compete the physician in the important traits of professionalism. Only 71 (20.2%) participants believed AI to be diagnostically superior to the physician. Two hundred fourteen (61.0%) were worried about completely trusting AI in its decisions, and 204(58.1%) believed that AI systems lacking human traits would not be able to mirror the doctor-patient relationship. Two hundred sixty-one (74.4%) participants believed that AI would be useful in Administrative tasks. A majority, 162 (46.2%), do not believe that AI would replace them. Finally, a huge majority of participants [225 (64.1%)] demanded the integration of AI in Pakistan's healthcare system. This study suggests that a majority of healthcare professionals in Pakistan do not believe that they are sufficiently aware of the role of AI in healthcare. This was corroborated by their answers to various questions regarding the capabilities of AI. This study indicates the need for a more comprehensive ascertainment of healthcare professionals' perceptions regarding the role of Artificial Intelligence in medicine and bridging the gap between doctors and technology to further promote a patient-centred approach to medicine.

Transforming Healthcare with AI: Promises, Pitfalls, and Pathways Forward.

This perspective paper provides a comprehensive examination of artificial intelligence (AI) in healthcare, focusing on its transformative impact on clinical practices, decision-making, and physician-patient relationships. By integrating insights from evidence, research, and real-world examples, it offers a balanced analysis of AI's capabilities and limitations, emphasizing its role in streamlining administrative processes, enhancing patient care, and reducing physician burnout while maintaining a human-centric approach in medicine. The research underscores AI's capacity to augment clinical decision-making and improve patient interactions, but it also highlights the variable impact of AI in different healthcare settings. The need for context-specific adaptations and careful integration of AI technologies into existing healthcare workflows is emphasized to maximize benefits and minimize unintended consequences. Significant attention is given to the implications of AI on the roles and competencies of healthcare professionals. The emergence of AI necessitates new skills in data literacy and technology use, prompting a shift in educational curricula towards digital health and AI training. Ethical considerations are a pivotal aspect of the discussion. The paper explores the challenges posed by data privacy concerns, algorithmic biases, and ensuring equitable access to AI-driven healthcare. It advocates for the development of comprehensive ethical frameworks and ongoing research to guide the responsible use of AI in healthcare. Conclusively, the paper advocates for a balanced approach to AI adoption in healthcare, highlighting the importance of ongoing research, strategic implementation, and the synergistic combination of human expertise with AI technologies for optimal patient care.

SpikeExplorer: Hardware-Oriented Design Space Exploration for Spiking Neural Networks on FPGA

One of today’s main concerns is to bring artificial intelligence capabilities to embedded systems for edge applications. The hardware resources and power consumption required by state-of-the-art models are incompatible with the constrained environments observed in edge systems, such as IoT nodes and wearable devices. Spiking Neural Networks (SNNs) can represent a solution in this sense: inspired by neuroscience, they reach unparalleled power and resource efficiency when run on dedicated hardware accelerators. However, when designing such accelerators, the amount of choices that can be taken is huge. This paper presents SpikExplorer, a modular and flexible Python tool for hardware-oriented Automatic Design Space Exploration to automate the configuration of FPGA accelerators for SNNs. SpikExplorer enables hardware-centric multiobjective optimization, supporting target factors such as accuracy, area, latency, power, and various combinations during the exploration process. The tool searches the optimal network architecture, neuron model, and internal and training parameters leveraging Bayesian optimization, trying to reach the desired constraints imposed by the user. It allows for a straightforward network configuration, providing the full set of explored points for the user to pick the trade-off that best fits their needs. The potential of SpikExplorer is showcased using three benchmark datasets. It reaches 95.8% accuracy on the MNIST dataset, with a power consumption of 180 mW/image and a latency of 0.12 ms/image, making it a powerful tool for automatically optimizing SNNs.

Infa-Care: AI/ML Integrated Application

Abstract: Infa Care is an all-encompassing Android app designed for parents and caregivers, offering crucial knowledge on infant care, from feeding and hygiene to sleep patterns and developmental milestones. It includes a user-friendly shopping section for baby essentials. A standout feature is its integration of cutting-edge machine learning (ML) and artificial intelligence (AI) capabilities. By analyzing real-time images from the device's camera, the app accurately interprets baby sign language, promoting effective communication for non-verbal infants. This ML-AI integration enhances the app's utility by fostering a deeper understanding of infants' needs and enabling more meaningful interactions between caregivers and babies..

Tour Sentiments Personalized Recommendations System Using AI and Deep Learning

In a world saturated with travel information overload and generic recommendations, "Tour Sentiments: A Personalized Travel Recommendation System Using AI " emerges as a groundbreaking initiative. This project seeks to transform travel planning by harnessing the capabilities of Artificial Intelligence and real-time google Maps with street view and duration of travel. The primary goal of 'Tour Sentiments' is to create a personalized travel recommendation system that adapts to the changing emotions and preferences of travelers. By employing real-time sentiment analysis using google gemini, the system aims to offer tailored suggestions that align closely with individuals' current feelings and desires, departing from conventional one-size-fits-all approaches. Beyond mere efficiency, the ultimate vision of 'Tour Sentiments' is to elevate the travel experience, infusing it with joy, discovery, and lasting memories. Through the amalgamation of AI and sentiment analysis, the project aspires to make global exploration more accessible and captivating, setting a new standard for personalized travel guidance and enriching journeys worldwide.

Artificial Intelligence in Blockchain Technology

Abstract: As the digital landscape continues to evolve, the integration of artificial intelligence (AI) and blockchain technology has emerged as a promising avenue for innovation. This paper examines the intersection of AI and blockchain, exploring the synergistic relationship between these two transformative technologies. By combining the decentralized and immutable nature of blockchain with the intelligent capabilities of AI, novel solutions can be developed across various domains such as finance, healthcare, supply chain management, and more. Through a comprehensive review of existing literature and case studies, this paper highlights the potential applications, challenges, and future directions of integrating AI into blockchain technology

Synergies and Challenges: Exploring the Intersection of Artificial Intelligence and Cybersecurity

Abstract: The fusion of artificial intelligence (AI) and cybersecurity signifies a profound turning point in our perpetual struggle against cyber threats. With the expansion of digital landscapes and the escalating sophistication of cyber adversaries, harnessing the capabilities of AI to bolster our defences has become not only advisable but imperative. This paper embarks on a deep exploration of the intricate relationship between AI and cybersecurity, uncovering the rich tapestry of synergies that underlie their integration while deftly navigating the labyrinth of complex challenges and ethical quandaries inherent in this convergence. Through a meticulous examination of advanced threat detection methodologies, adaptive defence mechanisms, and the unfolding panorama of emerging trends, this paper endeavours to lay the groundwork for a seismic shift in cybersecurity paradigms. It is a journey through the corridors of innovation, where AI-driven insights illuminate the path forward and interdisciplinary collaboration serves as the compass guiding us toward a future fortified by AI innovation and fortified by collective wisdom.

생성형 인공지능과 교육에 관한 언론보도 토픽 분석

Objectives The purpose of this study is to analyze topics of media reports regarding generative artificial intellignece and education. Methods For this purpose, topic model, one of the text mining techniques, was used. First, we collected newspaper article data on ‘Generative Artificial Intelligence and Education’ through the Big Kinds service and then performed pre-processing and morphological analysis. Based on this, 30 keywords were analyzed. By measuring the number of topics and deriving the top 10 words for each topic, 13 topics were presented and explained. Results The 13 topics presented through the research results are as follows. Researchers focus on education that fosters artificial intelligence leadership, Gyeonggi GPT and fair art education, digital finance professional training, problems and responses to digital democracy, reliability and ethical use of artificial intelligence, artificial intelligence used in creation in various fields, training programs between Gyeonggi-do and State University of New York, regional cooperation model for artificial intelligence convergence education, artificial intelligence edutech support project and core talent training, Metaverse Lab support project, strengthening employee artificial intelligence capabilities for use in local government administrative work, Busan Metropolitan Office of Education's digital program attracting attention from overseas and changes in cultural arts education methods in the era of artificial intelligence were presented. Conclusions Based on the topics presented in this study, we proposed ways to apply and activate generative artificial intelligence in education. The five measures are strengthening teacher capacity for generative artificial intelligence, establishing guidelines for the development and use of artificial intelligence, learner capacity to use generative artificial intelligence correctly, developing and disseminating generative artificial intelligence teaching cases, and based on spontaneity. This is support from the Artificial Intelligence Teacher Research Group.

The Implementation of Artificial Intelligence and its Future Potential

The implementation of artificial intelligence (AI) involves the utilization of various methodologies, including machine learning, deep learning, natural language processing, and robotics. These methodologies enable computers to perform tasks that traditionally require human intelligence, such as recognizing patterns in data, understanding natural language, and making decisions. AI's future potential is vast and spans across numerous industries and applications. In healthcare, AI can assist in disease diagnosis, personalized treatment plans, and drug discovery. In finance, AI algorithms can analyze market trends, detect fraud, and optimize investment portfolios. In transportation, AI powers autonomous vehicles, optimizing routes and reducing accidents. In entertainment, AI-driven recommendation systems personalize content for users. However, realizing this potential requires addressing several challenges. Ethical considerations are paramount, including ensuring fairness, transparency, accountability, and privacy in AI systems. Bias in algorithms must be mitigated to prevent discrimination, and AI decisions must be interpretable and explainable to users. Research efforts are underway to advance AI capabilities while addressing these challenges. This includes developing more robust and efficient algorithms, improving AI safety and security, and exploring applications of AI for social good, such as addressing climate change and promoting inclusivity. Collaboration between academia, industry, and government is crucial for driving AI research forward and ensuring its responsible integration into society. This collaboration facilitates knowledge sharing, resource allocation, and the development of standards and regulations to guide the ethical and equitable deployment of AI technologies. Ultimately, by harnessing the power of AI responsibly, society can benefit from its transformative potential while mitigating potential risks.

Research on the Influence Mechanism of Artificial Intelligence Capability on Ambidextrous Innovation

With the continuous development and widespread adoption of artificial intelligence (AI) technology, enterprises have realized the importance of possessing artificial intelligence capabilities to enhance organizational performance. However, there is still a relative lack of empirical research on how to build AI capabilities, and how AI capabilities impact organizational innovation performance. Based on the resource-based theory, this study constructs a conceptual model of “AI Capabilities - Organizational Response (Organizational Learning and Organizational Agility) - Ambidextrous Innovation”, exploring the direct impact of AI capabilities on ambidextrous innovation, and the mediating effects of organizational learning and organizational agility, also the moderating role of digital strategy in this linkage. With data from 253 survey questionnaires of Chinese enterprises, the results indicate that: first, AI capabilities have a significantly positive impact on organizational ambidextrous innovation;  second, organizations with high levels of organizational learning ability and agility can better leverage AI capabilities to promote the development of ambidextrous innovation;  Thirdly, digital strategy has played an active role in regulating the influence path of two-way innovation within the organization and provided sufficient boundary conditions; fourth, The data capability, technological capability, and fundamental resource capability of AI capabilities have differentiating effects on exploratory and exploitative innovations. The findings of this research hold significant theoretical and managerial value for understanding the driving mechanisms of organizational innovation performance in the era of AI.

Opportunities and challenges of integrating artificial intelligence in China's elderly care services

The challenge of elderly care presents a formidable task, demanding the collective attention of governmental bodies and diverse sectors of society. The integration of Artificial Intelligence (AI) into the research and development of Social Elderly Care Service (ECS) has emerged as a dominant trend, holding substantial importance in the establishment of an efficient ECS system. This study aims to serve as a comprehensive reference for the advancement of China's ECS system, achieved through the harmonious integration of a social ECS system with AI capabilities. This paper introduces the fundamental theory of AI, delving into the intricacies of the greyscale model of AI. Furthermore, it provides an overview of the current landscape of elderly care and elder care institutions, offering scientific data and insights to propel further research on AI development and system construction. Through an analysis of the existing research status, the study identifies prevalent issues within the AI-ECS integration, emphasizing pivotal factors influencing the construction of a robust social ECS system. To address these concerns, the study puts forth specific and viable policy recommendations. Notably, the questionnaire's statistics underscore that 83% of the elderly populace would opt for AI-driven solutions in selecting intelligent products, thereby underscoring the pivotal role of AI within the social ECS system. The challenges facing elderly care systems, including demographic shifts, resource constraints, and evolving societal norms, demand innovative solutions for providing efficient and effective care. This study addresses these challenges by exploring the integration of Artificial Intelligence (AI) into Social Elderly Care Services (ECS) in China. By delving into the theory of AI and assessing the existing research status, the study identifies key issues in AI-ECS integration and proposes viable policy recommendations. Insights from stakeholder surveys further highlight the importance of AI-driven solutions in meeting the needs of the elderly population.

Demonstrating ChatGPT possibilities for the libraries

The authors analyze the materials in librarianship generated with artificial intelligence (AI). They demonstrate that AI generates the texts very close to the common style and format of publications in the area. The goal of the article is to warn the users of the AI-generated works and possibly, to prevent violation of publishing ethics. The authors undertook comprehensive comparison to evaluate the quality and similarity of text materials, and application of the generated predictive text (GPT) technology in librarianship. The advantages and disadvantages of both approaches are revealed. The findings of the study are important to understand the AI capabilities and limitations in librarianship and to identify the contexts where the computerized systems are most efficient as compared to human authors. The ChatGPT is an excellent reference and complementary tool based on the accumulated samples. The materials created by ChatGPT are legally close to encyclopedic or dictionary entries, however, they have no actual author(s), i.e. these materials are independent creative works therefore they can hardly be protected by general copyright. The authors recommend to indicate the AIgenerated parts of works, argue that further development of juridical foundations for GPT materials is needed, and invite everyone to discussion.The study is accomplished within the framework of the NRU HSE Fundamental Studies Program. 

Topic Modelling of Management Research Assertions to Develop Insights into the Role of Artificial Intelligence in Enhancing the Value Propositions of Early-Stage Growth-Oriented Companies

The article suggests a Value Proposition (VP) framework that enables analysis of the beneficial impact of Artificial Intelligence (AI) resources and capabilities on specific VP activities. To develop such a framework, we examined existing business and management research publications to identify and extract assertions that could be used as a source of actionable insights for early-stage growth-oriented companies. The extracted assertions were assembled into a corpus of texts that was subjected to topic modelling analysis—a machine learning approach to natural language processing that is used to identify latent themes in large corpora of text documents. The topic modelling resulted in the identification of seven topics. Each topic is defined by a set of most frequent words co-occurring in a distinctive subset of texts that could be interpreted in terms of activities constituting the core elements of the VP framework. We then examined each activity in terms of its potential to be enhanced by employing AI resources and capabilities. The interpretation of the topic modelling results led to the identification of seven topics: (1) Value created; (2) Stakeholder value propositions; (3) Foreign market entry; (4) Customer base; (5) Continuous improvement; (6) Cross-border operations; and (7) Company image. The uniqueness of the adopted topic modelling approach consists in the quality of the assertions and the interpretation of the seven topics as an activity framework, i.e., in its capacity to generate actionable insights for practitioners. The additional analysis suggests that there is a potential for AI to enhance the emerging four core elements of the VP framework: Value created, Stakeholder value propositions, Foreign market entry, and Customer base. More importantly, we found that the greatest number of assertions related to activities that could be enhanced by AI are part of the Customer base topic, i.e., the topic that is most directly related to the growth potential of the companies. In addition, the VP framework suggests that a company’s customer base growth is continuously enhanced through a positive loop enabled by activities focused on the Continuous improvement of the activities and the amount of Value created, the alignment of Stakeholder value propositions, and companies’ Foreign market entry. Thus, the multiple-stakeholder perspective on VP development and foreign market entry appears as a factor that helps in understanding the beneficial impact of AI on the enhancement of the VP of early-stage growth-oriented companies.

Remote Sensing and AI-based Monitoring of Legume Crop Health and Growth

Background: For the enhancement of agricultural productivity, while ensuring sustainability, this study delves into the under-explored domain of monitoring legume crop health and growth. Traditional methods of crop assessment encounter limitations, prompting a push for innovation by integrating advanced remote sensing technologies and artificial intelligence (AI). The purpose is to revolutionize crop assessment techniques and overcome existing constraints. Methods: The data was collected using a combination of satellite imagery and ground-based sensors, resulting in a rich repository of multispectral and spatial information. By using the capabilities of AI, a robust model was developed to interpret the gathered data, offering a detailed insight into the health and growth dynamics of legume crops. The AI algorithms not only identify anomalies but also forecast future states, facilitating timely interventions and informed decision-making in agriculture. Result: The findings of this study signify a significant change in precision agriculture, where the synergy of remote sensing and AI optimizes resource allocation, minimizes environmental impact and maximizes crop yields. The research unlocks the potential to transform legume farming practices, promoting sustainability and ushering in an era of data-driven cultivation. The implications extend beyond the legume crop sector, influencing the broader agricultural landscape with the promise of more efficient and sustainable practices.

Artificial Intelligence for Retrosynthetic Planning Needs Both Data and Expert Knowledge.

Rapid advancements in artificial intelligence (AI) have enabled breakthroughs across many scientific disciplines. In organic chemistry, the challenge of planning complex multistep chemical syntheses should conceptually be well-suited for AI. Yet, the development of AI synthesis planners trained solely on reaction-example-data has stagnated and is not on par with the performance of "hybrid" algorithms combining AI with expert knowledge. This Perspective examines possible causes of these shortcomings, extending beyond the established reasoning of insufficient quantities of reaction data. Drawing attention to the intricacies and data biases that are specific to the domain of synthetic chemistry, we advocate augmenting the unique capabilities of AI with the knowledge base and the reasoning strategies of domain experts. By actively involving synthetic chemists, who are the end users of any synthesis planning software, into the development process, we envision to bridge the gap between computer algorithms and the intricate nature of chemical synthesis.