Artificial Intelligence Technology Research Articles

Research on Medical Device Software Based on Artificial Intelligence and Machine Learning Technologies

With the rapid development of Artificial Intelligence (AI) and Machine Learning (ML) technologies, new opportunities for innovation and application in medical device software have emerged. This paper explores the current research on medical device software based on AI and ML, analyzing the practical applications and outcomes of these technologies in the medical field. First, the basic concepts of AI and ML and their significance in medical devices are introduced. Then, an in-depth analysis of the functional requirements of medical device software, user experience, and relevant regulations is provided. Next, the paper discusses the specific applications of AI and ML in data processing, model training, and software implementation, showcasing their practical effects through case studies. Finally, the paper summarizes the current technical challenges and future opportunities, hoping to provide insights for further development in the medical device industry.

Case Study on the Inherent Mechanisms Driving Industrial Innovation through Data Empowerment

In the context of the profound integration of big data, artificial intelligence technologies, and industries, both data resources and digital technologies became crucial pillars supporting the development of industrial innovation. They played key roles in enhancing the innovation capabilities of industries. In this regard, the present study employed the specific case of the intelligent brain of the Taizhou Machine Tool Industry. Utilizing grounded theory, the research integrated relevant data through coding analysis and category extraction to construct a comprehensive theoretical model of the intrinsic mechanisms driving industrial innovation through data empowerment. The study revealed that the realization of data-empowered industrial innovation followed a logical path of 'data resource services data capability spillover industrial innovation achievement.' Specifically, data empowerment operated across three dimensions: industrial resources, production, and ecology, effectively propelling the realization of industrial innovation. From a systematic perspective, this research analyzed the intrinsic mechanisms of data-empowered industrial innovation, deepening the theoretical understanding of industrial innovation. In the context of past industrial digitization transformation, these findings provided valuable insights for guiding industrial innovation development.

A Decision Risk Assessment and Alleviation Framework under Data Quality Challenges in Manufacturing

The ability and rapid access to execution data and information in manufacturing workshops have been greatly improved with the wide spread of the Internet of Things and artificial intelligence technologies, enabling real-time unmanned integrated control of facilities and production. However, the widespread issue of data quality in the field raises concerns among users about the robustness of automatic decision-making models before their application. This paper addresses three main challenges relative to field data quality issues during automated real-time decision-making: parameter identification under measurement uncertainty, sensor accuracy selection, and sensor fault-tolerant control. To address these problems, this paper proposes a risk assessment framework in the case of continuous production workshops. The framework aims to determine a method for systematically assessing data quality issues in specific scenarios. It specifies the preparation requirements, as well as assumptions such as the preparation of datasets on typical working conditions, and the risk assessment model. Within the framework, the data quality issues in real-time decision-making are transformed into data deviation problems. By employing the Monte Carlo simulation method to measure the impact of these issues on the decision risk, a direct link between sensor quality and risks is established. This framework defines specific steps to address the three challenges. A case study in the steel industry confirms the effectiveness of the framework. This proposed method offers a new approach to assessing safety and reducing the risk of real-time unmanned automatic decision-making in industrial settings.

The Role of Artificial Intelligence in Autonomous Vehicles: Challenges and Opportunities

Autonomous vehicles (AVs) represent a paradigm shift in transportation, promising safer, more efficient, and convenient mobility. Central to the realization of AVs are advancements in artificial intelligence (AI) technologies, enabling vehicles to perceive their environment, make decisions, and navigate autonomously. This paper examines the multifaceted role of AI in AVs, focusing on the challenges and opportunities it presents. The discussion encompasses the application of AI techniques such as machine learning, computer vision, and sensor fusion in enabling autonomous driving systems. Furthermore, it addresses the technical, regulatory, and ethical challenges associated with AI implementation in AVs, including sensor limitations, data privacy concerns, and legal frameworks. Despite these challenges, AI-enabled AVs offer significant opportunities, including improved road safety, enhanced mobility, and reduced environmental impact. Recent innovations and advancements in AI technologies pave the way for overcoming existing barriers and accelerating the adoption of AVs. Through case studies and real-world applications, this paper highlights the transformative potential of AI-driven AVs across various sectors, from transportation and logistics to urban planning. Looking ahead, future research and development efforts must focus on addressing remaining challenges and maximizing the societal benefits of AI in autonomous vehicles.

Artificial Intelligence in Aspect of Deforestation and Plantation Sustainability: Bibliometric Approach

This study examines the application of Artificial Intelligence (AI) in addressing deforestation and promoting sustainability in plantations using a bibliometric approach. Deforestation, a critical global issue, results from agricultural expansion, plantation development, and land-use changes, leading to significant environmental degradation. AI has been proposed as a powerful tool to monitor and manage deforestation more effectively, offering solutions such as satellite imagery analysis and predictive models. Through a bibliometric analysis spanning the last decade (2013–2023), this study uses VOSviewer to visualize co-citation networks, identifying key research trends and clusters related to AI in deforestation and plantation sustainability. The findings reveal that research is concentrated in regions like Indonesia and Brazil, where AI technologies like machine learning are employed to predict deforestation and enhance resource management. Emerging research areas include the integration of AI with the Internet of Things (IoT) and blockchain for improved data management and sustainability practices. This analysis provides insights into the growing role of AI in mitigating deforestation and offers recommendations for future research, including addressing ethical challenges and regulatory frameworks to further enhance sustainable plantation management.

Applications of AI in martial arts: A survey

Artificial intelligence (AI) has become an interdisciplinary subject that integrates digital image processing, machine learning, and computer science, etc. A lot of work in martial arts benefits from AI technology. In this paper, we highlight and summarize the applications of AI in martial arts and indicate the main progress of related studies. Through the investigation of martial arts styles, tasks, data acquisition methods, and algorithm improvements, a paradigm that represents a possible technological process for an intelligent martial arts training system was summarized. These applications bring more scientific training and more effective technical analysis to martial arts. Furthermore, this paper delves into specific AI applications in areas such as action recognition, pose estimation, action evaluation, elite athlete support, sports betting, health care, animation generation, and competition field segmentation. By exploring these areas, the research prospects of AI applications in martial arts are comprehensively discussed. This paper inspires further research on martial arts by using AI, which includes helping researchers expand their research ideas and research methods, identify frontier issues, and innovate based on existing research.

Examining the impact of personalization and carefulness in AI-generated health advice: Trust, adoption, and insights in online healthcare consultations experiments

Artificial intelligence (AI) technologies, exemplified by health chatbots, are transforming the healthcare industry. Their widespread application has the potential to enhance decision-making efficiency, improve the quality of healthcare services, and reduce medical costs. While there is ongoing discussion about the opportunities and challenges brought by AI, more needs to be known about the public's attitude towards its use in the healthcare domain. Understanding public attitudes can help policymakers better grasp their needs and involve them in making decisions that benefit both technological development and social welfare. Therefore, this study presents evidence from two between-subjects experiments. This study aims to compare the public's adoption and trust levels in health advice provided by human vs. AI doctors and explore the potential effects of personalization and carefulness on the public's attitudes. Experimental designs adopt a trust-centered, cognitively and emotionally balanced perspective to study the public's intention to adopt AI. In Experiment 1, the experimental conditions involve the types of decision-makers providing online consultation advice, either AI or human doctors. In Experiment 2, the experimental conditions involve varying levels of perceived personalization and carefulness (high vs. low). A total of 734 participants took part in the study. They were randomly assigned to one of the intervention conditions and responded to manipulation checks after reading the materials. Using a seven-point Likert-type scale, participants rated their cognitive and emotional trust levels and intention to adopt the advice. Partial Least Squares Structural Equation Modeling (PLS-SEM) is conducted to estimate the proposed theoretical perspective. Qualitative interviews on both real-world and AI-generated treatment recommendations further enriched the understanding of public perceptions.The results show that AI-generated advice is generally slightly less trusted and adopted by the public. However, a noticeable inclination towards AI-generated advice emerges when AI demonstrates proficiency in understanding individuals' health conditions and providing empathetic consultations. Further analyses confirm the mediating influence of emotional trust between cognitive trust and adoption intention. These findings provide deeper insights into the process of adoption and trust formation. Moreover, they offer guidance to digital healthcare providers, empowering them with the knowledge to co-design AI implementation strategies that cater to the public's expectations.

Mechanism Study on Innovative Influences of AI and Blockchain on Supply Chain Logistics: Case Study of JD and Alibaba

This study explores the innovative impact mechanisms of artificial intelligence (AI) and blockchain in supply chain and logistics through the case study of JD and Alibaba. The successful cases of JD and Alibaba in supply chain management show that by integrating AI and blockchain technologies, companies can automate, smarten and make supply chain logistics more efficient, thereby reducing costs, improving efficiency and enhancing overall competitiveness. Applying AI in supply chain logistics, such as demand forecasting, inventory optimization and transport route planning, can provide accurate data analysis and decision support. It can help reduce inventory costs, shorten transportation time and optimize resource allocation in the supply chain. However, AI and blockchain technologies still face several challenges, such as data privacy and security issues, technology costs and standardization. Future research and practice will continue to work on solving these challenges, further optimizing and improving the application of AI and blockchain in supply chain logistics, and promoting innovation and development in supply chain logistics.

New intelligent flower arrangement system

Currently, the art of flower arrangement plays a significant role in both personal life and collective work. However, for non-professionals, creating flower arrangements with high aesthetic value can be challenging. With the rapid advancement of artificial intelligence technology, its application in the field of flower arrangement offers new possibilities for addressing this issue. To achieve a balance between aesthetic appeal and efficiency in intelligent flower arranging, this paper proposes an innovative intelligent flower arrangement assistance system. This system encompasses core functions such as intelligent recognition of floral materials, matching suitable options from a database, realizing flower arrangement designs, and constructing the database. Through a series of experiments, the system has demonstrated a good balance between aesthetic appeal and efficiency in flower arrangement design, with results indicating the effectiveness and feasibility of the proposed method.

Application of artificial intelligence in construction

This paper mainly introduces the application and assistance of artificial intelligence in architecture and construction cost, as well as the future prospects and development trends. The study adopts a quantitative research design of survey research and data collection method of literature analysis. In addition, it can further explore the innovative applications of artificial intelligence in architectural design, such as spatial planning optimization based on big data analysis and simulation experience through virtual reality technology. At the same time, it can also deeply explore the role of artificial intelligence in the automated management of construction processes, material selection optimization, etc., and give detailed explanations with specific cases. In addition, when discussing the future prospects and development trends, it can consider the prediction of the impact of the continuous evolution of artificial intelligence technology on the construction industry, such as the wider application of machine learning algorithms in project management, the gradual maturity of intelligent sensor systems in building equipment monitoring. At the same time, it can also analyze the profound impact of artificial intelligence on the future development of the construction industry from economic, social and environmental angles, and put forward corresponding solutions.

NATO’s Artificial Intelligence Strategy and Interoperability Challenges: The Case of Turkey

ABSTRACT The rapid advancement of artificial intelligence (AI) has significantly changed military applications, creating new competitive advantages and shifting the global balance of power. This article examines NATO’s AI strategy and the associated interoperability challenges, with a particular focus on Turkey. NATO’s AI strategy seeks to enhance interoperability among its member states by fostering the integration of AI technologies into military capabilities. However, achieving this goal is complicated by the varying levels of AI technological advancement, divergent national AI-military strategies and differing geopolitical considerations among member countries. Using Turkey as a case study, this paper explores how the rapid development of AI-based military drones contributes to Turkey’s strategic autonomy and enhances regime resilience while also highlighting certain interoperability considerations within NATO. The analysis underlines the need for a cohesive approach to AI integration that addresses these disparities to maintain NATO’s collective defence capabilities.

The Intersection of Artificial Intelligence, Wearable Devices, and Sexual Medicine.

The aim of our review paper is to provide a comprehensive overview of the current technologies in artificial intelligence and wearable devices dedicated to sexual health. Currently, AI-powered technologies are enhancing our understanding of reproductive health and sexually transmitted infections, and facilitating empathetic education and outreach to diverse populations. Additionally, innovative wearable devices are providing insights into men's erectile health, addressing ejaculatory concerns, and exploring women's orgasms in relation to pelvic floor muscles and clitoral blood flow. The field of sexual health technology is rapidly expanding, with recent innovations transforming our understanding of sexual health. As technology progresses, it is crucial to address significant ethical considerations to protect users, particularly due to the sensitive nature of the data involved.

Права на результаты интеллектуальной деятельности, созданные с использованием искусственного интеллекта

The widespread use of neurotechnology and artificial intelligence technology in all spheres of our life has led to the emergence of new problems for civil law. Among them, the problem of determining the legal regime of intellectual property objects created with the help of these end-to-end digital technologies is of particular relevance. First of all, it is necessary to determine whether they protect such works, and if so, who is their copyright holder. The authors devote it to the analysis of possible solutions to the problem of granting protection to intellectual property objects created by neural networks and substantiating the author’s position on the possibility of forming a new exclusive right to such objects. The authors come to the conclusion that it is impossible and unnecessary to qualify artificial intelligence as a subject of law and the author of the created works and propose a new legal structure for the initial emergence of the exclusive right to objects created by neural networks from the subject using this technology, since it is its activity aimed at the operation of a device or software based on artificial intelligence that leads to the emergence of a new original result.

Mechanisms of Controllable Growth and Ohmic Contact of Two-Dimensional Molybdenum Disulfide: Insight from Atomistic Simulations.

ConspectusTwo-dimensional (2D) semiconducting transition metal dichalcogenides (TMDs), in particular molybdenum disulfide (MoS2), have recently attracted huge interest due to their proper bandgap, high mobility at 2D limit, and easy-to-integrate planar structure, which are very promising for extending Moore's law in postsilicon electronics technology. Great effort has been devoted toward such a goal since the demonstration of protype MoS2 devices with high room-temperature on/off current ratios, ultralow standby power consumption, and atomic level scaling capacity down to sub-1-nm technology node. However, there are still several key challenges that need to be addressed prior to the real application of MoS2-based electronics technology. The controllable growth of wafer-scale single-crystal MoS2 on industry-compatible insulating substrates is the prerequisite of application while the currently synthesized MoS2 films mostly are polycrystalline with limited sizes of single-crystal domains and may involve metal substrates. The precise layer-control is also very important for MoS2 growth since its electronic properties are layer-dependent, whereas the layer-by-layer growth of multilayer MoS2 dominated by the van der Waals (vdW) epitaxy leads to poor thickness uniformity and noncontinuously distributed domains. High density up to 1013 cm-2 of sulfur vacancies (SVs) in grown MoS2 can cause unfavorable carrier scatting and electronic properties variations and will inevitably disturb the device performance. The dangling-bond-free surface of MoS2 gives rise to an inherent vdW gap at metal-semiconductor (M-S) contact, which leads to high electrical resistance and poor current-delivery capability at the contact interface and thereby substantially limits the performances of MoS2 devices.In this Account, we briefly review recent experimental and theoretical attempts for addressing the aforementioned challenges and present our own insights from atomistic simulations. We theoretically revealed the vital role of substrate steps for guiding unidirectional nucleation of monolayer MoS2 and uniform nucleation and edge-aligned growth of bilayer MoS2 by advanced simulations. The established thermodynamic mechanisms have successfully directed the experimental works on the controllable growth of 2 in. single-crystal monolayer and centimeter-scale uniform bilayer MoS2. The postgrowth repair mechanism of SV defect in MoS2 via thiol chemistry treatment has been theoretically explored with the consideration of side reaction of surface functionalization to help experimentally reduce SV defect density by 75%. Beyond the atomic level understanding, theoretical simulations proposed the electronic states hybridization mechanism across the semimetal-MoS2 vdW interface, thereby guiding experimental effort for realizing Ohmic contact at the MoS2-Sb(0112) vdW interface with record-low contact resistance.These advances provide a sound basis with an atomic-level understanding for addressing the related issues. However, there are still notable gaps in terms of system size and time scale of dynamics between atomistic simulations and experimental observations for the studies of MoS2 growth and interfaces. The combination of multiscale simulations and artificial intelligence technology is expected to narrow these gaps and provide a more insightful understanding of the controllable growth and interfacial properties modulation of MoS2. We conclude the Account with the standing challenges and outlook on future research directions from the theoretical perspective.

Positive Strategies for Enhancing Elderly Interaction Experience in Smart Healthcare through Optimized Design Methods: An INPD-Based Research Approach

The breakthrough in artificial intelligence technology and the development of smart healthcare models have significantly improved modern healthcare services. However, the elderly population still faces numerous challenges. Therefore, the aim of this study is to enhance the interactive experience of elderly users and to propose effective design strategies through optimized design methods. Based on the INPD research methodology, the design process is divided into four stages. First, in the SET phase, product opportunity gaps are identified, followed by in-depth interviews and surveys to gather user needs. Second, the AHP method is used to establish a hierarchical model and judgment matrix to determine the subjective weights of each need, while the EWM method, based on survey data, determines the objective weights of each need. To ensure the scientific nature of the overall weight, a combined weighting approach is used, followed by a final prioritization of needs. Third, after translating user needs into design requirements, three design schemes are produced, and the TOPSIS method is used to calculate the weights and evaluate the optimal scheme. Fourth, the product opportunities are implemented and tested. The research results indicate that the proposed optimization design method is effective and not only reduces the barriers and challenges elderly users face when interacting with intelligent products but also enhances their overall experience. Moreover, it provides a practical approach to the sustainable development of smart healthcare. As an essential component of future healthcare services, the sustainability of smart healthcare will depend on a deep understanding of user needs and continuous optimization. The design strategy proposed in this study offers practical application value, improving elderly users’ satisfaction while also providing insights that may be useful for other smart services.

Digitization of local self-government based on the use of artificial intelligence in the context of sustainable development

The subject of the research is the processes of transformation of modern local self-government public administration based on the development and application of AI-based technologies. The article claims that the rapid development and advancement of digital technologies, especially artificial intelligence (AI) technologies, made significant impact on public governance at the local level, both in its ‘architecture’ and processes. It is shown that AI encompasses a range of capabilities that are accelerating progress toward the sustainable development and SDGs, while at the same time it may also inhibit some targets within sustainable development. It is emphasized that increasing government’s AI maturity in local self-government requires pairing human and technical capabilities with strategy and governance.

Application Evaluation and Performance-Directed Improvement of the Native and Engineered Biosensors.

Transcription factor (TF)-based biosensors (TFBs) have received considerable attention in various fields due to their capability of converting biosignals, such as molecule concentrations, into analyzable signals, thereby bypassing the dependence on time-consuming and laborious detection techniques. Natural TFs are evolutionarily optimized to maintain microbial survival and metabolic balance rather than for laboratory scenarios. As a result, native TFBs often exhibit poor performance, such as low specificity, narrow dynamic range, and limited sensitivity, hindering their application in laboratory and industrial settings. This work analyzes four types of regulatory mechanisms underlying TFBs and outlines strategies for constructing efficient sensing systems. Recent advances in TFBs across various usage scenarios are reviewed with a particular focus on the challenges of commercialization. The systematic improvement of TFB performance by modifying the constituent elements is thoroughly discussed. Additionally, we propose future directions of TFBs for developing rapid-responsive biosensors and addressing the challenge of application isolation. Furthermore, we look to the potential of artificial intelligence (AI) technologies and various models for programming TFB genetic circuits. This review sheds light on technical suggestions and fundamental instructions for constructing and engineering TFBs to promote their broader applications in Industry 4.0, including smart biomanufacturing, environmental and food contaminants detection, and medical science.

The risk assessment of public-private partnership projects using artificial intelligence algorithms

Purpose: is to develop an innovative approach to risk management in public-private partnership (PPP) projects using advanced artificial intelligence technologies that allow creating the risk assessment model that takes into account non-linear relationships between various risk factors.Methods: in addition to traditional methods of scientific knowledge, interdisciplinary approaches of risk management and established practice of machine learning were used in the work. The methodological basis of the study was formed by works on the risk assessment and the application of AI algorithms in this area. The empirical basis of the study was the data of the official portal of ROSINFRA on public-private partnership projects.Results: the practice of applying AI algorithms to the task of assessing the risks of PPP-projects in Russia and abroad was studied. It is established that the most effective result is shown by the models based on Random-Forest-Classifier. However, the presented solutions do not take into account Russian economic realities. The authors have structured a database of implemented PPP-projects suitable for risk modeling. The model for assessing the risk of failure to achieve the objectives of Russian PPP-projects has been developed and its quality has been assessed. Recommendations on implementation of the model in the operational loop of PPP projects realization processes are offered.Conclusions and Relevance: the developed model allows, according to the general parameters of PPP project (region, authority, term of agreement, industry and scope of implementation, etc.) with the accuracy of 93% (according to the ROC\AUC metric), to assess the risk that the project will end incorrectly (due to a failed tender, refusal to launch, termination by court decision, cancellation/annulment of the tender). With the help of the model the executive authorities of the Russian Federation can build risk management for PPP projects management in the regions and thus contribute to their efficiency improvement. The article may also become useful for project management practitioners and appraisers.

Use of AI in Mental Health Care: Community and Mental Health Professionals Survey.

Artificial intelligence (AI) has been increasingly recognized as a potential solution to address mental health service challenges by automating tasks and providing new forms of support. This study is the first in a series which aims to estimate the current rates of AI technology use as well as perceived benefits, harms, and risks experienced by community members (CMs) and mental health professionals (MHPs). This study involved 2 web-based surveys conducted in Australia. The surveys collected data on demographics, technology comfort, attitudes toward AI, specific AI use cases, and experiences of benefits and harms from AI use. Descriptive statistics were calculated, and thematic analysis of open-ended responses were conducted. The final sample consisted of 107 CMs and 86 MHPs. General attitudes toward AI varied, with CMs reporting neutral and MHPs reporting more positive attitudes. Regarding AI usage, 28% (30/108) of CMs used AI, primarily for quick support (18/30, 60%) and as a personal therapist (14/30, 47%). Among MHPs, 43% (37/86) used AI; mostly for research (24/37, 65%) and report writing (20/37, 54%). While the majority found AI to be generally beneficial (23/30, 77% of CMs and 34/37, 92% of MHPs), specific harms and concerns were experienced by 47% (14/30) of CMs and 51% (19/37) of MHPs. There was an equal mix of positive and negative sentiment toward the future of AI in mental health care in open feedback. Commercial AI tools are increasingly being used by CMs and MHPs. Respondents believe AI will offer future advantages for mental health care in terms of accessibility, cost reduction, personalization, and work efficiency. However, they were equally concerned about reducing human connection, ethics, privacy and regulation, medical errors, potential for misuse, and data security. Despite the immense potential, integration into mental health systems must be approached with caution, addressing legal and ethical concerns while developing safeguards to mitigate potential harms. Future surveys are planned to track use and acceptability of AI and associated issues over time.

Emerging challenges in the application of artificial intelligence for the eye disease screening in Chinese primary healthcare institutions

Breakthroughs have been achieved recently in the application of artificial intelligence (AI) for the eye disease screening in Chinese primary healthcare institutions, but challenges have also emerged. First, AI software has continuously evolved, expanding the range of eye diseases that can be screened, enhancing diagnostic accuracy, and progressing towards predicting the course of eye diseases. However, inadequate infrastructure such as 5G and a shortage of specialized personnel have hindered the coverage of screenings. Second, while the cost-effectiveness of AI is well-established, new screening models have impacted the equity of screenings. It is essential to tailor AI application models to the specific context of China. Third, AI screening guidelines have been increasingly improved, providing direction for AI development and reference for the promotion and application of AI technologies. Nonetheless, high-quality empirical research is urgently needed to provide scientific evidence for policymaking related to AI in the eye disease screening. Therefore, it is suggested to develop multimodal AI models that integrate basic data such as symptoms and medical history with simple ophthalmic examinations, to accelerate the construction of infrastructure like 5G and focus on cultivating interdisciplinary talents, to explore suitable service systems and models for the large-scale eye disease screening tailored to local conditions, and to conduct long-term, multi-center, empirical studies.