Analysis of the Implementation for the State-of-art AI Techniques in Self-driving

This introductory article opens the “Applications of AI in the Semiconductor Industry” section by giving a holistic overview of the development of artificial intelligence (AI) technologies applied to the industry. Historically, the semiconductor industry has utilised complex automation for many tasks and areas, especially in repetitive work and uniform processes. The high need for flexibility in manufacturing, increased diversification of products, complexity, and demand for more autonomous operations, including human-machine interaction, have led to a strong push towards using AI technologies in semiconductor manufacturing. AI technologies are applied in semiconductor product development, digitised product definition (DPD), knowledge management system for risk assessment and root cause analysis, image recognition for inspection and defect classification in front end (FE) and back end (BE) applications for anomaly detection in process chains. Deep learning (DL) and Machine Learning (ML) techniques have given a new stimulus to semiconductor industry research to address the unique challenges for semiconductor manufacturing as the technologies nods are evolving and the number of process parameters to be controlled is increasing. In the end, 106the article introduces the four contributions to this section, highlighting the use of AI, computer vision, neural networks (NNs) in various use cases in semiconductor manufacturing processes.

Artificial intelligence (AI) is transforming ophthalmology by leveraging machine learning (ML) and deep learning (DL) techniques, particularly artificial neural networks (ANN) and convolutional neural networks (CNN) to mimic human brain functions and enhance accuracy through data exposure. These AI systems are particularly effective in analyzing ophthalmic images for early disease detection, improving diagnostic precision, streamlining clinical workflows, and ultimately enhancing patient outcomes. This study aims to explore the specific applications and impact of AI in the fields of glaucoma, corneal diseases, and oculoplastics. This study reviews current AI technologies in ophthalmology, examining the implementation of ML and DL techniques. It evaluates AI's role in early disease detection, diagnostic accuracy, clinical workflow enhancement, and patient outcomes. AI has significantly advanced the early detection and management of various ocular conditions. In glaucoma, AI systems provide standardized, rapid identification of disease characteristics, reducing intra- and interobserver bias and workload. For corneal diseases, AI tools enhance diagnostic methods for conditions such as keratitis and keratoconus, improving early detection and treatment planning. In oculoplastics, AI assists in the diagnosis and monitoring of eyelid and orbital diseases, facilitating precise surgical planning and postoperative management. The integration of AI in ophthalmology has revolutionized eye care by enhancing diagnostic precision, streamlining clinical workflows, and improving patient outcomes. As AI technologies continue to evolve, their applications in ophthalmology are expected to expand, offering innovative solutions for the diagnosis, monitoring, treatment, and surgical outcomes of various eye conditions.

Artificial intelligence (AI) technology, as one of the most sophisticated science and technology in today's world, is increasingly being used to production and life, particularly in the manufacturing business. it demonstrates how artificial intelligence technology is used in mechanical manufacturing, namely in defect detection, quality inspection, enhancing workplace safety, and other areas. Artificial intelligence technology is becoming increasingly important in people's lives as it becomes more widely used in people's daily lives, such as the widespread use of smart dishwashers and smart sweepers, which are the products of the fusion of artificial intelligence and the mechanical manufacturing industry. Indeed, artificial intelligence technology has been widely utilized in the mechanical manufacturing business, which not only ensures production precision, but also enhances job productivity and workplace safety. The rise of artificial intelligence has caused significant changes in the manufacturing industry as a whole. Without exception, the manufacturing industry must rely on AI technology to accomplish automation and intelligent development, as well as to improve productivity. Using artificial intelligence to categorize mechanical components, we may propose parts from a based solely on an image or CAD model. To find a necessary component in a machines we must currently browse through a catalogue and be able to discern which part you want based on the available possibilities and your understanding of the catalogue. There are serial numbers to memorize since a single digit or character change might indicate a different sort of part. The algorithm will choose which sections are the best and will significantly facilitate our search.

Objective : To analyze foreign experience in the use of artificial intelligence (AI) in oncology, as well as examine current advances in AI and its impact on clinical practice. Material and methods. A systematic review of foreign literature was carried out, current AI programs and technologies in oncology were analyzed. The total number of identified records via the PubMed/MEDLINE search was 7680. After publication selection in accordance with the PRISMA guidelines, 32 studies that met all criteria were randomly included in the review and formed the basis for the analysis. Results . AI demonstrates high efficiency in cancer diagnostics, including early detection of tumors, analysis of medical images and pathological data. A literature review on the use of AI models in oncology revealed exponential growth from 2010 to 2022, confirming the active development of the field. Diagnostic and treatment reports generated by the AI technologies indicated a comparable level of accuracy to that of experienced oncologists; they also revealed the ability to improve clinical outcomes. Furthermore, the introduction of AI stimulates the development of personalized treatment, increased patient adherence to therapy and optimization of the work of medical organizations. AI’s impact was revealed on physicians (reduced diagnostic and treatment errors), patients (personalized support), and hospitals (smart management systems). Conclusion . AI is becoming an integral part of modern oncology, offering new opportunities to improve diagnostics, treatment, outcome prediction, and patient support. A study of the dynamics of publications and AI models indicates that the use of AI in clinical oncology is rapidly developing, opening up new prospects for the creation of smart hospitals, simplification of medical data exchange, development of personalized medicine, and improvement of the quality of patient care. The integration of AI with emerging technologies such as wearable devices and multimodal analysis promises to revolutionize oncology practice.

COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), spread rapidly and affected most of the world since its outbreak in Wuhan, China, which presents a major challenge to the emergency response mechanism for sudden public health events and epidemic prevention and control in all countries. In the face of the severe situation of epidemic prevention and control and the arduous task of social management, the tremendous power of science and technology in prevention and control has emerged. The new generation of information technology, represented by big data and artificial intelligence (AI) technology, has been widely used in the prevention, diagnosis, treatment and management of COVID-19 as an important basic support. Although the technology has developed, there are still challenges with respect to epidemic surveillance, accurate prevention and control, effective diagnosis and treatment, and timely judgement. The prevention and control of sudden infectious diseases usually depend on the control of infection sources, interruption of transmission channels and vaccine development. Big data and AI are effective technologies to identify the source of infection and have an irreplaceable role in distinguishing close contacts and suspicious populations. Advanced computational analysis is beneficial to accelerate the speed of vaccine research and development and to improve the quality of vaccines. AI provides support in automatically processing relevant data from medical images and clinical features, tests and examination findings; predicting disease progression and prognosis; and even recommending treatment plans and strategies. This paper reviews the application of big data and AI in the COVID-19 prevention, diagnosis, treatment and management decisions in China to explain how to apply big data and AI technology to address the common problems in the COVID-19 pandemic. Although the findings regarding the application of big data and AI technologies in sudden public health events lack validation of repeatability and universality, current studies in China have shown that the application of big data and AI is feasible in response to the COVID-19 pandemic. These studies concluded that the application of big data and AI technology can contribute to prevention, diagnosis, treatment and management decision making regarding sudden public health events in the future.

Pharmacovigilance, the science of monitoring drug safety, plays a crucial role in identifying and mitigating adverse drug reactions (ADRs). However, underreporting in pharmacovigilance systems—estimated to have a median rate of 94 %—poses a significant threat to patient safety by hindering the detection of safety signals. The need to address these gaps is paramount, especially with the rising complexity of healthcare data. The advent of artificial intelligence (AI) and big data technologies offers promising solutions to overcome the limitations of traditional pharmacovigilance methods.The application of AI and machine learning (ML) technologies, including natural language processing (NLP) and deep learning, has the potential to revolutionize drug safety monitoring by automating the detection of ADRs from diverse data sources, such as electronic health records (EHRs), spontaneous reporting systems, and social media. These tools can process unstructured data and uncover patterns not easily identifiable through conventional approaches. Additionally, AI can enable real-time pharmacovigilance, which is especially critical in an era of increasing polypharmacy and diverse patient populations. AI-driven models are being utilized to detect drug-drug interactions (DDIs), predict ADRs, and enhance the overall efficiency of pharmacovigilance processes.Despite these advancements, several challenges remain. The performance of AI models is heavily dependent on the quality and quantity of data available. Inadequate or poorly curated datasets can lead to inaccurate ADR detection, particularly in resource-limited settings. Moreover, the heterogeneity of data sources necessitates robust AI models capable of integrating various types of data while ensuring accurate and reliable outputs. There is also a pressing need to address the transparency and explainability of AI models, as the opaque decision-making processes of current algorithms often impede their acceptance among pharmacovigilance professionals.Future directions must focus on improving the quality and standardization of datasets, advancing NLP techniques for better interpretation of clinical narratives, and developing explainable AI models. Regulatory frameworks should evolve to support AI deployment in pharmacovigilance, ensuring the establishment of best practices for AI implementation and the creation of large-scale, publicly available training datasets.Additionally, AI models should go beyond correlation-based approaches by integrating causal inference techniques, which will allow for a more accurate understanding of the relationship between drugs and ADRs. Human oversight will still be required to validate AI findings, but ongoing efforts to improve the robustness of AI systems will reduce dependency on manual interventions and scale the use of AI in pharmacovigilance.The integration of AI and big data in pharmacovigilance has the potential to transform drug safety monitoring, addressing many of the challenges posed by increasing data complexity and the need for real-time analysis. As these technologies continue to evolve, they promise to make pharmacovigilance more efficient, accurate, and comprehensive, thereby improving patient safety and rational drug use.

As the online retail market expands and brand competition intensifies, e-commerce platforms and retailers are facing increasing service and operational costs. The emergence of artificial intelligence (AI) technology has transformed the consumer shopping experience while simultaneously reducing retailers' operational costs. However, platforms must judiciously balance the operational efficiency improvements enabled by AI technology with the potential risks stemming from intensified competition and rising technology adoption costs. This study constructs a two-stage supply chain model involving a retailer and a platform, who are selling national brand products and substitutable private label brand, respectively. The platform strategically adopts and shares AI technology with retailer to optimise sales performance. We systematically analyze the viability of AI adoption and its impact on brand competition. The findings suggest that under moderate cost conditions, the platform's AI adoption may result in a lose–lose situation for both firms. When AI R&D costs are low, sharing AI technology, despite potentially weakening the platform's private-label competitiveness and reducing overall market demand, can still lead to a win-win situation for both parties. Additionally, extended analysis indicates that changes in consumer preferences have a limited effect on AI investment decisions, although different AI applications may introduce more complex supply chain dynamics.

- Artificial Intelligence (AI) has revolutionized healthcare and dentistry by enabling accurate diagnosis, prediction, and personalized treatment. This review article provides an in-depth understanding of AI in healthcare and its impact on dentistry. AI technology has transformed various aspects of dental practice, including diagnosis, treatment, and patient care. AI-powered image processing systems have significantly assisted dental professionals in diagnosing and treating dental diseases. Additionally, AI technology has improved patient management and communication, enabling personalized care and enhancing the overall dental experience. In dentistry, AI has been applied in various areas, including restorative dentistry, endodontics, and regenerative dentistry. AI models have shown accuracy in detecting dental caries, vertical root fractures, and periapical lesions. In endodontics, AI has displayed accuracy in diagnostic and prognostic evaluations, enhancing treatment planning and success rates. Despite the benefits, AI technology faces challenges, including the need for large datasets, complex mechanisms, and high costs. However, the future of AI in healthcare and dentistry looks promising, with potential applications in precision and accuracy, research exploration, and repetitive task management. Recent advances in AI have opened up new possibilities, including deep learning models and integration with other technologies. As AI continues to evolve, it is expected to drive innovation, improve efficiency, and enhance human life. Further research is needed to test the dependability, relevance, and expenditure of AI models before integrating them into routine clinical work. Nevertheless, AI has the potential to transform dentistry, improving diagnostic accuracy, reducing costs, and enhancing patient outcomes. Key Words: Artificial Intelligence, Machine learning, Deep learning, Neural networks, Artificial Intelligence in healthcare, AI in restorative dentistry, AI in Endodontics, AI in regenerative endodontics

The article is devoted to the study of the application of artificial intelligence technologies in law enforcement activities in Ukraine and the world. The purpose of the article is to analyze the international and domestic experience of using digital technologies in law enforcement activities, to identify the shortcomings of the legal regulation of the use of artificial intelligence in this area, to provide proposals for its improvement in Ukraine. It was found that one of the countries that is actively expanding the use of artificial intelligence and successfully introducing relevant technologies into public administration is China. The artificial intelligence system which is used in China called «Zero Trust». The purpose of using this system is to prevent the commission of corruption offenses. It is emphasized, that the disadvantage of the «Zero Trust» system is that artificial intelligence, identifying the cause of corruption, does not indicate the algorithms and principles of this process. At the same time, the experience of using artificial intelligence in the field of preventing and combating corruption in Ukraine is considered when carrying out automated verification of persons declarations of authorized to perform state and local government functions. Ways of improving legal regulation when conducting financial control of declarations using artificial intelligence technologies are proposed. It has been found that artificial intelligence is widely used in the USA to predict the prevalence and state of crime. It was emphasized that Ukraine also has experience in the application of artificial intelligence technologies in law enforcement activities. Artificial intelligence is used in the development and implementation of departmental specialized intelligent software, the implementation of operative and investigative actions (for the recognition of video objects observation by comparing images in social networks, etc). It was concluded that artificial intelligence and digital technologies help to quickly process large volumes of information in the investigation and prevention of criminal activity. Modern developments in the field of artificial intelligence and advanced digital technologies, which are implemented in foreign countries to prevent and fight crime, are of great importance for the improvement of legal regulation in this field in Ukraine.

Objective: To establish a disease risk prediction model for the newborn screening system of inherited metabolic diseases by artificial intelligence technology. Methods: This was a retrospectively study. Newborn screening data (n=5 907 547) from February 2010 to May 2019 from 31 hospitals in China and verified data (n=3 028) from 34 hospitals of the same period were collected to establish the artificial intelligence model for the prediction of inherited metabolic diseases in neonates. The validity of the artificial intelligence disease risk prediction model was verified by 360 814 newborns' screening data from January 2018 to September 2018 through a single-blind experiment. The effectiveness of the artificial intelligence disease risk prediction model was verified by comparing the detection rate of clinically confirmed cases, the positive rate of initial screening and the positive predictive value between the clinicians and the artificial intelligence prediction model of inherited metabolic diseases. Results: A total of 3 665 697 newborns' screening data were collected including 3 019 cases' positive data to establish the 16 artificial intelligence models for 32 inherited metabolic diseases. The single-blind experiment (n=360 814) showed that 45 clinically diagnosed infants were detected by both artificial intelligence model and clinicians. A total of 2 684 cases were positive in tandem mass spectrometry screening and 1 694 cases were with high risk in artificial intelligence prediction model of inherited metabolic diseases, with the positive rates of tandem 0.74% (2 684/360 814)and 0.46% (1 694/360 814), respectively. Compared to clinicians, the positive rate of newborns was reduced by 36.89% (990/2 684) after the application of the artificial intelligence model, and the positive predictive values of clinicians and artificial intelligence prediction model of inherited metabolic diseases were 1.68% (45/2 684) and 2.66% (45/1 694) respectively. Conclusion: An accurate, fast, and the lower false positive rate auxiliary diagnosis system for neonatal inherited metabolic diseases by artificial intelligence technology has been established, which may have an important clinical value.

Artificial intelligence for diabetic retinopathy screening in Africa

Currently, artificial intelligence (AI) technology offers opportunities to address serious challenges in waste management in the fashion industry. This study systematically reviewed and analyzed literature on AI applications in textile waste management from 2014 to 2024. We aimed to review the intervention effects of AI technologies on textile waste across stages of the fashion industry lifecycle and explore AI’s role in promoting circular supply chains (CSCs). The results show that AI plays a prominent role in four key areas: Enhanced intelligent analysis of market dynamics, optimizing consumer experience-oriented durability design, collaboratively optimizing production processes and inventory management, and advancement of intelligent waste management. Convolutional neural networks are the most widely used AI technology due to their strength in image recognition. However, AI technologies also face limitations such as high data requirements. Future research should attempt to promote improvements in policies, enterprise, and technologies to unleash greater potential for AI in CSCs.

This paper explores the application of artificial intelligence (AI) technology in youth sports training, focusing on skill prediction, performance forecasting, and injury prevention. Through case studies, it demonstrates AI's role in enhancing biomechanical precision, physiological monitoring, and tactical execution during soccer, basketball, and rugby training. Research findings indicate that AI technology significantly improves training efficiency and athlete performance while reducing injury risks through real-time data analysis and feedback. For instance, in soccer training, a convolutional neural network (CNN)-based system achieved over 90% accuracy in motion recognition, substantially reducing technical errors and injury risks. In basketball training, AI-driven physiological monitoring systems lowered the incidence of non-contact injuries by dynamically adjusting training loads. Furthermore, AI technology optimized match strategies for youth soccer teams through real-time tactical decision support. Despite challenges such as cost, coach training, and data privacy, AI applications in youth sports training hold immense potential to enhance training outcomes and athletes' long-term development.

Real-World Surveillance of FDA-Cleared Artificial Intelligence Models: Rationale and Logistics.

Art education has undergone significant transformation in the era of digitalization. With the advancement of Artificial Intelligence (AI) technology, there is tremendous potential to enhance the art learning experience. This research aims to elucidate how AI technology can be effectively utilized to improve art education in the context of digitalization. The study aims to introduce AI technology and explore its potential applications in enhancing the art learning experience. In the context of art learning experience, AI technology can provide broader access to digital art collections, online galleries, and virtual museums. Students can explore various artworks and cultures through digital platforms, thereby enriching their understanding and appreciation of art. The research findings reveal that AI technology can offer personalized guidance to students in developing their artistic skills. Intelligent AI systems can analyze students' artworks, provide tailored suggestions, and assist in enhancing their technical and creative abilities. In conclusion, the integration of AI technology in art education holds immense potential to elevate students' learning experiences. By expanding access, providing personalized guidance, and creating interactive experiences, AI technology enriches art learning in the era of digitalization. This is expected to provide greater insights and understanding of how AI technology can be harnessed to enhance the art learning experience in the context of digitalization. By optimizing the potential of AI, art education can become more inclusive, personalized, and engaging.