Current Artificial Intelligence Technology Research Articles

Artificial intelligence-aided diagnosis and treatment in the field of optometry.

With the rapid development of computer technology, the application of artificial intelligence (AI) to ophthalmology has gained prominence in modern medicine. As modern optometry is closely related to ophthalmology, AI research on optometry has also increased. This review summarizes current AI research and technologies used for diagnosis in optometry, related to myopia, strabismus, amblyopia, optical glasses, contact lenses, and other aspects. The aim is to identify mature AI models that are suitable for research on optometry and potential algorithms that may be used in future clinical practice.

Machine Learning and AI in Cancer Prognosis, Prediction, and Treatment Selection: A Critical Approach.

Cancer is a leading cause of morbidity and mortality worldwide. While progress has been made in the diagnosis, prognosis, and treatment of cancer patients, individualized and data-driven care remains a challenge. Artificial intelligence (AI), which is used to predict and automate many cancers, has emerged as a promising option for improving healthcare accuracy and patient outcomes. AI applications in oncology include risk assessment, early diagnosis, patient prognosis estimation, and treatment selection based on deep knowledge. Machine learning (ML), a subset of AI that enables computers to learn from training data, has been highly effective at predicting various types of cancer, including breast, brain, lung, liver, and prostate cancer. In fact, AI and ML have demonstrated greater accuracy in predicting cancer than clinicians. These technologies also have the potential to improve the diagnosis, prognosis, and quality of life of patients with various illnesses, not just cancer. Therefore, it is important to improve current AI and ML technologies and to develop new programs to benefit patients. This article examines the use of AI and ML algorithms in cancer prediction, including their current applications, limitations, and future prospects.

Application of Artificial Intelligence to the Diagnosis and Therapy of Nasopharyngeal Carcinoma.

Artificial intelligence (AI) is an interdisciplinary field that encompasses a wide range of computer science disciplines, including image recognition, machine learning, human-computer interaction, robotics and so on. Recently, AI, especially deep learning algorithms, has shown excellent performance in the field of image recognition, being able to automatically perform quantitative evaluation of complex medical image features to improve diagnostic accuracy and efficiency. AI has a wider and deeper application in the medical field of diagnosis, treatment and prognosis. Nasopharyngeal carcinoma (NPC) occurs frequently in southern China and Southeast Asian countries and is the most common head and neck cancer in the region. Detecting and treating NPC early is crucial for a good prognosis. This paper describes the basic concepts of AI, including traditional machine learning and deep learning algorithms, and their clinical applications of detecting and assessing NPC lesions, facilitating treatment and predicting prognosis. The main limitations of current AI technologies are briefly described, including interpretability issues, privacy and security and the need for large amounts of annotated data. Finally, we discuss the remaining challenges and the promising future of using AI to diagnose and treat NPC.

P‐2.12: A Comprehensive Study of Content Generation Using Diffusion Model

The essence of the Metaverse is the process of integrating a large number of existing technologies to virtualize and digitize the real world. With the development of artificial intelligence technology, a large number of digital native content in the Metaverse needs to be completed by artificial intelligence. The current artificial intelligence technology allows computers to automatically and efficiently generate text, pictures, audio, video, and even 3D models. With the further development of natural language processing technology and generative network models, future artificial intelligence generation will subvert the existing content generation methods and gradually become an accelerator for the development of the Metaverse. In this paper, we have analyzed and studied the basic principles of diffusion models, clarified some existing problems of diffusion models, considered the key areas of concern and potential areas to be further explored, and looked forward to the future application of diffusion models in the field of Metaverse.

Artificial intelligence in dentistry—A review

Artificial Intelligence (AI) is the ability of machines to perform tasks that normally require human intelligence. AI is not a new term, the concept of AI can be dated back to 1950. However, it did not become a practical tool until two decades ago. Owing to the rapid development of three cornerstones of current AI technology—big data (coming through digital devices), computational power, and AI algorithm—in the past two decades, AI applications have started to provide convenience to people's lives. In dentistry, AI has been adopted in all dental disciplines, i.e., operative dentistry, periodontics, orthodontics, oral and maxillofacial surgery, and prosthodontics. The majority of the AI applications in dentistry are for diagnosis based on radiographic or optical images, while other tasks are not as applicable as image-based tasks mainly due to the constraints of data availability, data uniformity, and computational power for handling 3D data. Evidence-based dentistry (EBD) is regarded as the gold standard for decision making by dental professionals, while AI machine learning (ML) models learn from human expertise. ML can be seen as another valuable tool to assist dental professionals in multiple stages of clinical cases. This review describes the history and classification of AI, summarizes AI applications in dentistry, discusses the relationship between EBD and ML, and aims to help dental professionals better understand AI as a tool to support their routine work with improved efficiency.

The Integration of Artificial Intelligence in the Forensic Medicine and its Applications in Medico-Legal Autopsy, Forensic Toxicology, and Disaster Victim Identification

Artificial Intelligence (AI) is revolutionizing various fields of medicine and science. In the realm of forensic medicine, AI is playing an increasingly significant role, not only in enhancing traditional forensic practices but also in improving the teaching of forensic medicine. This article explores the integration of AI in the teaching of forensic medicine and its applications in medico-legal autopsy, forensic toxicology, and disaster victim identification. By examining current AI technologies and their potential impacts, this article sheds light on the future of forensic medicine education and practice, emphasizing the need for continued research and development in this dynamic field.

ChatGPT - još jedan korak bliže digitalnoj eri ili pretnja osnovnim pravima i slobodama?

Artificial Intelligence (AI) constitutes one of the most fundamental pillars for the implementation of the EU Digital Agenda. It corresponds to the tremendous ongoing technological evolution which is marked by the spread of the digitalization in both private and public sector. AI tools provide numerous services, such as faster decision-making, performance of multiple tasks and repetitive jobs on our behalf and diagnosis of risky situations. This paper puts a special emphasis on the ChatGPT which is considered the most illustrative representative of the current AI technology. Within a minimal time of its existence this innovative viral chatbot has started to dominate the world of AI. However, its use raises serious legal and ethical risks for our privacy and protection of fundamental rights and freedoms, born by the lack of a binding regulatory framework governing AI. Therefore, at first level, this study focuses on the legal regime which governs the use of ChatGPT, by interpreting the legal status, after giving a short demonstration of its function and services (Section I). Secondly, a critical approach will be pursued focusing on special issues regarding this new AI tool on the basis of its application in practice at the area of journalism (Section II). Following that intense analysis, the paper aims to lead to fruitful and original conclusions with the ultimate goal to enhance the establishment of a powerful, safe and trustful digital environment.

Biases in human perception of facial age are present and more exaggerated in current AI technology

Our estimates of a person’s age from their facial appearance suffer from several well-known biases and inaccuracies. Typically, for example, we tend to overestimate the age of smiling faces compared to those with a neutral expression, and the accuracy of our estimates decreases for older faces. The growing interest in age estimation using artificial intelligence (AI) technology raises the question of how AI compares to human performance and whether it suffers from the same biases. Here, we compared human performance with the performance of a large sample of the most prominent AI technology available today. The results showed that AI is even less accurate and more biased than human observers when judging a person’s age—even though the overall pattern of errors and biases is similar. Thus, AI overestimated the age of smiling faces even more than human observers did. In addition, AI showed a sharper decrease in accuracy for faces of older adults compared to faces of younger age groups, for smiling compared to neutral faces, and for female compared to male faces. These results suggest that our estimates of age from faces are largely driven by particular visual cues, rather than high-level preconceptions. Moreover, the pattern of errors and biases we observed could provide some insights for the design of more effective AI technology for age estimation from faces.

Prediction Model and Data Simulation of Sports Performance Based on the Artificial Intelligence Algorithm.

There is still a certain deviation between the current artificial intelligence technology and the traditional learning mode, which makes it unable to be effectively applied in teaching and learning. Therefore, an effective method needs to be proposed to use functions to predict data. Function calculation can not only solve the complex problems of data calculation process but also make the data evenly distributed to take full advantage of the capabilities of each system. In this experiment, we mainly use the control function. After substituting the data into the control function, the function will automatically classify the data. In this paper, according to the actual situation of physical education in colleges and universities, from the two aspects of artificial intelligence and comprehensive learning algorithm, to build a system which can collect and analyze the past achievements of college students' physical education performance simulation can effectively help the design of physical education curriculum. According to the distribution of experimental data, a specific conclusion can be drawn; that is, the test model we choose can calculate and measure the physical fitness level of students, but there are big differences. In contrast, our experimental method using the ensemble computing model can not only predict and analyze the physical fitness level of college students but also reduce errors and shorten the time required for the experiment.

Development of artificial intelligence technology in diagnosis, treatment, and prognosis of colorectal cancer.

Artificial intelligence (AI) technology has made leaps and bounds since its invention. AI technology can be subdivided into many technologies such as machine learning and deep learning. The application scope and prospect of different technologies are also totally different. Currently, AI technologies play a pivotal role in the highly complex and wide-ranging medical field, such as medical image recognition, biotechnology, auxiliary diagnosis, drug research and development, and nutrition. Colorectal cancer (CRC) is a common gastrointestinal cancer that has a high mortality, posing a serious threat to human health. Many CRCs are caused by the malignant transformation of colorectal polyps. Therefore, early diagnosis and treatment are crucial to CRC prognosis. The methods of diagnosing CRC are divided into imaging diagnosis, endoscopy, and pathology diagnosis. Treatment methods are divided into endoscopic treatment, surgical treatment, and drug treatment. AI technology is in the weak era and does not have communication capabilities. Therefore, the current AI technology is mainly used for image recognition and auxiliary analysis without in-depth communication with patients. This article reviews the application of AI in the diagnosis, treatment, and prognosis of CRC and provides the prospects for the broader application of AI in CRC.

Artificial Intelligence: Review of Current and Future Applications in Medicine.

The role of artificial intelligence (AI) in health care is expanding rapidly. Currently, there are at least 29 US Food and Drug Administration-approved AI health care devices that apply to numerous medical specialties and many more are in development. With increasing expectations for all health care sectors to deliver timely, fiscally-responsible, high-quality health care, AI has potential utility in numerous areas, such as image analysis, improved workflow and efficiency, public health, and epidemiology, to aid in processing large volumes of patient and medical data. In this review, we describe basic terminology, principles, and general AI applications relating to health care. We then discuss current and future applications for a variety of medical specialties. Finally, we discuss the future potential of AI along with the potential risks and limitations of current AI technology. AI can improve diagnostic accuracy, increase patient safety, assist with patient triage, monitor disease progression, and assist with treatment decisions.

Harnessing artificial intelligence and big data for SDGs and prosperous urban future in South Asia

Artificial intelligence (AI) and big data solutions are currently being utilized to offer low cost and efficient solutions in solving pressing urban socio-economic and environmental problems globally. The study found big data and AI have the potentiality to solve the common urban problems in South Asia and upsurge the efficiency of urban industries, increase competitiveness and productivity of the human and natural resources, reduce the cost of urban service delivery, and build climate resilience. The study has assessed the current AI and big data initiatives and technologies in mitigating the urban development challenges and their potentiality for scaling up in South Asian cities. The study also examined the latest innovations in AI and big data solutions for SDG monitoring and implementation in South Asia and their implication for transformational change. The study suggested that South Asia can harness the maximum benefit of AI and big data technologies by building big data and associated IT infrastructure, advancing research and innovations with regional cooperation, enhancing technological readiness, and eliminating week enabling conditions.

Innovation farm: Teaching artificial intelligence through gamified social entrepreneurship in an introductory MIS course*

ABSTRACTIn the past few years, an increased use of artificial intelligence (AI) by businesses across a range of industries has led to greater demand for employees with AI skills. This raises the question of how educators can introduce AI technologies to undergraduate business students while also developing their higher‐order thinking skills and preparing them for the complex challenges facing today's business leaders. At the same time, we have also witnessed a growing interest in social entrepreneurship among our student population. To address both needs, we designed and implemented the “Innovation Farm,” a gamified social entrepreneurship startup project, to help students learn the concepts and applications of AI in an introductory management information systems (MIS) course. The students work in teams to propose AI‐powered products and services targeting real‐world social issues. The project cultivates social awareness and empathy in students, fosters innovation, and introduces them to current AI technologies while also reinforcing their understanding of MIS course content. Students found the gamified approach engaging, as they are tackling real‐world social issues while also learning about innovation, collaboration, branding, and fundraising for a social startup. Students reported positive outcomes related to innovating using technology, making an impact through real‐world projects, and practicing their teamwork and leadership skills.

Predictive modeling in neurocritical care using causal artificial intelligence

Artificial intelligence (AI) and digital twin models of various systems have long been used in industry to test products quickly and efficiently. Use of digital twins in clinical medicine caught attention with the development of Archimedes, an AI model of diabetes, in 2003. More recently, AI models have been applied to the fields of cardiology, endocrinology, and undergraduate medical education. The use of digital twins and AI thus far has focused mainly on chronic disease management, their application in the field of critical care medicine remains much less explored. In neurocritical care, current AI technology focuses on interpreting electroencephalography, monitoring intracranial pressure, and prognosticating outcomes. AI models have been developed to interpret electroencephalograms by helping to annotate the tracings, detecting seizures, and identifying brain activation in unresponsive patients. In this mini-review we describe the challenges and opportunities in building an actionable AI model pertinent to neurocritical care that can be used to educate the newer generation of clinicians and augment clinical decision making.

Artificial Intelligence Solutions to Increase Medication Adherence in Patients With Non-communicable Diseases.

Artificial intelligence (AI) tools are increasingly being used within healthcare for various purposes, including helping patients to adhere to drug regimens. The aim of this narrative review was to describe: (1) studies on AI tools that can be used to measure and increase medication adherence in patients with non-communicable diseases (NCDs); (2) the benefits of using AI for these purposes; (3) challenges of the use of AI in healthcare; and (4) priorities for future research. We discuss the current AI technologies, including mobile phone applications, reminder systems, tools for patient empowerment, instruments that can be used in integrated care, and machine learning. The use of AI may be key to understanding the complex interplay of factors that underly medication non-adherence in NCD patients. AI-assisted interventions aiming to improve communication between patients and physicians, monitor drug consumption, empower patients, and ultimately, increase adherence levels may lead to better clinical outcomes and increase the quality of life of NCD patients. However, the use of AI in healthcare is challenged by numerous factors; the characteristics of users can impact the effectiveness of an AI tool, which may lead to further inequalities in healthcare, and there may be concerns that it could depersonalize medicine. The success and widespread use of AI technologies will depend on data storage capacity, processing power, and other infrastructure capacities within healthcare systems. Research is needed to evaluate the effectiveness of AI solutions in different patient groups and establish the barriers to widespread adoption, especially in light of the COVID-19 pandemic, which has led to a rapid increase in the use and development of digital health technologies.

Research on Basketball Players’ Training Strategy Based on Artificial Intelligence Technology

The current artificial intelligence technology, as a new frontier science and technology, can track and analyze a single sports goal in the training methods of basketball players, and use convenient training strategies to achieve the best training effect. This article first elaborates the current development trend of artificial intelligence technology, and also analyzes the development of artificial intelligence at home and abroad, expounds the current status of artificial intelligence in sports, and finally focuses on the training strategy of artificial intelligence in basketball players. In-depth analysis and research have been done in the application of other aspects, the purpose is to provide theoretical support and guidance for the further development of modern basketball. Based on artificial intelligence, the basketball player training system should fully understand and evaluate the athlete’s physical condition and athletic ability, timely measure the basketball player’s sports skills, give training strategies for the basketball player’s sports situation, and optimize the training plan for the athletes. In the software and hardware architecture, the Baum-Welch algorithm should be contacted to collect athletes’ sports data, and a data management platform should be established to fully explore the athletes’ exercise laws and strengthen the athletes’ daily training. The experimental research results show that the use of artificial intelligence technology can greatly improve the training efficiency of basketball players and fully stimulate the potential of basketball players. The use of artificial intelligence technology can not only provide basketball players with convenient and effective strategies, but also develop different training plans according to the physical fitness of each basketball player.

Traffic modeling and optimization in datacenters with graph neural network

Traffic Optimization (TO) is a well-known and established topic in datacenters with the fundamental goal of operating networks efficiently. Traditional TO heuristics may suffer from performance penalty as it mismatches actual traffic, while Artificial Intelligence (AI) which has undergone a renaissance recently is gradually being applied to the network optimization and has shown excellent advantages. However, the current AI technologies (e.g., DGN, DRL, DBA, etc.) have difficulty in adapting to the dynamic and variable characteristics of the network due to their lack of generalization ability, which limits the development of intelligent networks. As Graph Neural Network (GNN) can support relational reasoning and combinatorial generalization, we research how to model and optimize traffic in datacenters with GNN in this paper. First, we proposed a GNN model for reasoning Flow Completion Time (FCT), which is able to provide accurate estimation of never-seen network states. Then we designed a GNN-based optimizer for TO, which can be used to in flow routing, flow scheduling and topology management. Finally, the experimental results verify that the GNN model has a high inference accuracy, and the GNN-based optimizer can significantly reduce the average / p10 (the 10th percentile) FCT. Therefore, GNN has a great potential in network modeling and optimization, and has a wide range of applications.

AI algorithms, price discrimination and collusion: a technological, economic and legal perspective

In recent years, important concerns have been raised about the increasing capabilities of pricing algorithms to make use of artificial intelligence (AI) technologies. Two issues have gained particular attention: algorithmic price discrimination (PD) and algorithmic tacit collusion (TC). Although the risks and opportunities of both practices have been explored extensively in the literature, neither has yet been observed in the actual practice. As a result, there remains much confusion as to the ability of algorithms to engage in potentially harmful behavior with respect to price discrimination and collusion. In this article, we embed the economic and legal literature on these topics in a technological grounding to provide a more objective account of the capabilities of current AI technologies to engage in price discrimination and collusion. We argue that attention to these current technological capabilities should more directly inform on-going discussions on the urgency to reform legal rules or enforcement practices governing algorithmic PD and TC.

The impact of artificial intelligence on corporate control

This paper investigates the relationship between artificial intelligence (AI) and corporate control in the United Arab Emirates (UAE) emerging market. An exploratory study was conducted to derive the research questions. The nonprobability purposive sampling technique was implemented to select 10 highly experienced interviewees. In-depth primary data was collected through semi-structured interviews during 2019. Qualitative content analysis was used to answer the research questions. The results show a positive impact of AI on firm productivity and the auditing process, but uncertain influence on accounting information systems. More specifically, AI intervention increases firm productivity, creates new jobs and speeds up work processes. However, current AI technology is less likely to redefine auditing roles and still insufficient for developing accounting information systems. Human integration with AI systems will lead to more efficient results. This paper increases our understanding of how AI techniques can improve corporate control practices and the importance of selecting appropriate accounting professionals to decrease AI operation risks.

Tractor Assistant Driving Control Method Based on EEG Combined With RNN-TL Deep Learning Algorithm

Nowadays, fieldwork is often accompanied by tight schedules, which tends to strain the shoulder muscles due to high-intensity work. Moreover, it is difficult and stressful for the disabled to drive agricultural machinery. Besides, current artificial intelligence technology could not fully realize tractor autonomous driving because of a high uncertain filed environment and short interruptions of satellite navigation signal shaded by trees. To reduce manual operations, a tractor assistant driving control method was proposed based on the human-machine interface utilizing the electroencephalographic (EEG) signal. First, the EEG signals of the tractor drivers were collected by a low-cost brain-computer interface (BCI), followed by denoising using a wavelet packet. Then the spectral features of EEG signals were calculated and extracted as the input of Recurrent Neural Network (RNN). Finally, the EEG-aided RNN driving model was trained for tractor driving robot control such as straight going, brake, left turn, and right turn operations, which control accuracy was 94.5% and time cost was 0.61 ms. Also, 8 electrodes were selected by the PCA algorithm for the design of a portable EEG controller. And the control accuracy reached 93.1% with the time cost of 0.48 ms. To solve the incomplete driving data set in the actual world because some driving manners may cause dangerous or even death, RNN-TL algorithm was employed by creating the complete driving data in the virtual environment followed by transferring the driving control experience to the actual world with small actual driving data set in the field, which control accuracy was 93.5% and time consumption was 0.48 ms. The experimental results showed the feasibility of the proposed tractor driving control method based on EEG signal combined with RNN-TL deep learning algorithm which can work with the displacement error less than 6.7 mm when the tractor speed is less than 50 km/h.