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Aim: This article aimed to explore the role of AI in advancing nursing practice, focusing on its impact on readiness for the future. Design and Methods: A position paper, the methodology comprises three key steps. First, a comprehensive literature search using specific keywords in reputable databases was conducted to gather current information on AI in nursing. Second, data extraction and synthesis from selected articles were performed. Finally, a thematic analysis identifies recurring themes to provide insights into AI's impact on future nursing practice. Results: The findings highlight the transformative role of AI in advancing nursing practice and preparing nurses for the future, including enhancing nursing practice with AI, preparing nurses for the future (AI education and training) and associated, ethical considerations and challenges. AI‐enabled robotics and telehealth solutions expand the reach of nursing care, improving accessibility of healthcare services and remote monitoring capabilities of patients' health conditions. [ABSTRACT FROM AUTHOR]

Artificial intelligence education (AIEd) is defined in the field of education as the utilization of artificial intelligence. There are currently many AIEd‐driven applications in schools and universities. This paper applies an artificial intelligence module combined with the knowledge recommendation to the system and develops an online English teaching system in comparison with the common teaching auxiliary system. The method of English teaching is useful in investigating the potential internal connections between evaluation outcomes and various factors. This article develops deep learning‐assisted online intelligent English teaching system that utilizes to create a modern tool platform to help students improve their English language teaching efficiency in line with their mastery of knowledge and personality. The decision tree algorithm and neural networks have been used and to generate an English teaching assessment implementation model based on decision tree technologies. It provides valuable data from extensive information, summarizes rules and data, and helps teachers to improve their education and the English scores of students. This system reflects the thinking of the artificial intelligence expert system. Test application demonstrates that the system can help students improve their learning efficiency and will make learning content more relevant. Besides, the system provides an example model with similar methods and has a referential definition. [ABSTRACT FROM AUTHOR]

This paper discusses the factors affecting quality of school educational process. It describes the application of artificial intelligence approaches (neural networks) into the educational process. The analysis of methods for tracking changes in the position of the head while studying at school or at home is presented. The results obtained make it possible to evaluate the psycho‐physiological, psycho‐emotional state of primary school students in the process of interacting with a computer when performing educational tasks. The goal is to create computer tools that monitors changes in head position using images from a laptop or tablet digital camera. This article presents the stages of developing a neural network for assessing head turns when performing a school assignment and the results of applying the program. A new tool is proposed for assessing the state of a student in the learning process, to determine the ability to perceive different types of educational information. [ABSTRACT FROM AUTHOR]

Medical artificial intelligence (MAI) creates an opportunity to radically expand access to healthcare across the globe by allowing us to overcome the persistent labor shortages that limit healthcare access. This democratization of healthcare is the greatest moral promise of MAI. Whatever comes of the enthusiastic discourse about the ability of MAI to improve the state‐of‐the‐art in high‐income countries (HICs), it will be far less impactful than improving the desperate state‐of‐the‐actual in low‐ and middle‐income countries (LMICs). However, the almost exclusive development of MAI in HICs risks this promise being thwarted by contextual bias, an algorithmic bias that arises when the context of the training data is significantly dissimilar from potential contexts of application, which makes the unreflective application of HIC‐based MAI in LMIC contexts dangerous. The use of MAI in LMICs demands careful attention to context. In this paper, I aim to provide that attention. First, I illustrate the dire state of healthcare in LMICs and the hope that MAI may help us to improve this state. Next, I show that the radical differences between the health contexts of HICs and those of LMICs create an extraordinary risk of contextual bias. Then, I explore ethical challenges raised by this risk, and propose policies that will help to overcome those challenges. Finally, I sketch a wide range of related issues that need to be addressed to ensure that MAI has a positive impact on LMICs—and is able to improve, rather than worsen, global health equity. [ABSTRACT FROM AUTHOR]

The use of artificial intelligence (AI) in healthcare comes with opportunities but also numerous challenges. A specific challenge that remains underexplored is the lack of clear and distinct definitions of the concepts used in and/or produced by these algorithms, and how their real world meaning is translated into machine language and vice versa, how their output is understood by the end user. This "semantic" black box adds to the "mathematical" black box present in many AI systems in which the underlying "reasoning" process is often opaque. In this way, whereas it is often claimed that the use of AI in medical applications will deliver "objective" information, the true relevance or meaning to the end‐user is frequently obscured. This is highly problematic as AI devices are used not only for diagnostic and decision support by healthcare professionals, but also can be used to deliver information to patients, for example to create visual aids for use in shared decision‐making. This paper provides an examination of the range and extent of this problem and its implications, on the basis of cases from the field of intensive care nephrology. We explore how the problematic terminology used in human communication about the detection, diagnosis, treatment, and prognosis of concepts of intensive care nephrology becomes a much more complicated affair when deployed in the form of algorithmic automation, with implications extending throughout clinical care, affecting norms and practices long considered fundamental to good clinical care. [ABSTRACT FROM AUTHOR]

Recent years have witnessed intensive efforts to specify which requirements ethical artificial intelligence (AI) must meet. General guidelines for ethical AI consider a varying number of principles important. A frequent novel element in these guidelines, that we have bundled together under the term explicability, aims to reduce the black‐box character of machine learning algorithms. The centrality of this element invites reflection on the conceptual relation between explicability and the four bioethical principles. This is important because the application of general ethical frameworks to clinical decision‐making entails conceptual questions: Is explicability a free‐standing principle? Is it already covered by the well‐established four bioethical principles? Or is it an independent value that needs to be recognized as such in medical practice? We discuss these questions in a conceptual‐ethical analysis, which builds upon the findings of an empirical document analysis. On the example of the medical specialty of radiology, we analyze the position of radiological associations on the ethical use of medical AI. We address three questions: Are there references to explicability or a similar concept? What are the reasons for such inclusion? Which ethical concepts are referred to? [ABSTRACT FROM AUTHOR]

Surgical robot systems (SRS) represent an innovative cross‐disciplinary research field using robotic technology to assist surgeons in operations. Current bottlenecks in SRS, such as the limited ability to process complex information and make surgical decisions, have not been effectively solved. Artificial intelligence (AI) is a valuable technique for simulating and extending human intelligence. AI offers a new direction and impetus for SRS by enhancing performance in areas such as perception, navigation, surgical planning, and control strategies. This review introduces the developmental history of AI‐aided SRS, summarizes the basic SRS architecture, and analyzes how AI can improve SRS performance. Classical cases of AI‐aided SRS, the impact of evidence in clinical settings, and associated ethical and legal considerations are explored. Finally, the challenges in AI‐aided SRS are discussed, including algorithm development, surgical data science, human–robot coordination, and trust building between humans and robots. [ABSTRACT FROM AUTHOR]

Background and Aims: This systematic review aimed to evaluating the effectiveness of machine learning (ML) algorithms for the prediction and diagnosis of meningitis. Methods: On November 12, 2022, a systematic review was carried out using a keyword search in the reliable scientific databases PubMed, EMBASE, Scopus, and Web of Science. The recommendations of Preferred Reporting for Systematic Reviews and Meta‐Analyses (PRISMA) were adhered to. Studies conducted in English that employed ML to predict and identify meningitis were deemed to match the inclusion criteria. The eligibility requirements were used to independently review the titles and abstracts. The whole text was then obtained and independently reviewed in accordance with the eligibility requirements. Results: After all the research matched the inclusion criteria, a total of 16 studies were added to the systematic review. Studies on the application of ML algorithms in the three categories of disease diagnosis ability (8.16) and disease prediction ability (8.16) (including cases related to identifying patients (50%), risk of death in patients (25%), the consequences of the disease in childhood (12.5%), and its etiology [12.5%]) were placed. Among the ML algorithms used in this study, logistic regression (LR) (4.16, 25%) and multiple logistic regression (MLR) (4.16, 25%) were the most used. All the included studies indicated improvements in the processes of diagnosis, prediction, and disease outbreak with the help of ML algorithms. Conclusion: The results of the study showed that in all included studies, ML algorithms were an effective approach to facilitate diagnosis, predict consequences for risk classification, and improve resource utilization by predicting the volume of patients or services as well as discovering risk factors. The role of ML algorithms in improving disease diagnosis was more significant than disease prediction and prevalence. Meanwhile, the use of combined methods can optimize differential diagnoses and facilitate the decision‐making process for healthcare providers. [ABSTRACT FROM AUTHOR]

Sobel edge detector is an algorithm commonly used in image processing and computer vision to extract edges from input images using derivative of image pixels in x and y directions against surrounding pixels. Most artificial intelligence and machine learning applications require image processing algorithms running in real time on hardware systems like field‐programmable gate array (FPGAs). They typically require high throughput to match real‐time speeds and since they run alongside other processing algorithms, they are required to be area efficient as well. This article proposes a high‐speed and low‐area implementation of the Sobel edge detection algorithm. We created the design using a novel high‐level synthesis (HLS) design method based on application specific bit widths for intermediate data nodes. Register transfer level code was generated using MATLAB hardware description language (HDL) coder for HLS. The generated HDL code was implemented on Xilinx Kintex 7 field programmable gate array (FPGA) using Xilinx Vivado software. Our implementation results are superior to those obtained for similar implementations using the vendor library block sets as well as those obtained by other researchers using similar implementations in the recent past in terms of area and speed. We tested our algorithm on Kintex 7 using real‐time input video with a frame resolution of 1920 × 1080. We also verified the functional simulation results with a golden MATLAB implementation using FPGA in the loop feature of HDL Verifier. In addition, we propose a generic area, speed, and power improvement methodology for different HLS tools and application designs. [ABSTRACT FROM AUTHOR]

In the event of a fire breaking out or in other complicated situations, a mobile computing solution combining the Internet of Things and wearable devices can actually assist tracking solutions for rescuing and evacuating people in multistory structures. Thus, it is crucial to increase the positioning technology's accuracy. The sequential Monte Carlo (SMC) approach is used in various applications such as target tracking and intelligent surveillance, which rely on smartphone‐based inertial data sequences. However, the SMC method has intrinsic flaws, such as sample impoverishment and particle degeneracy. A novel SMC approach is presented, which is built on the weighted differential evolution (WDE) algorithm. Sequential Monte Carlo approaches start with random particle placements and arrives at the desired distribution with a slower variance reduction, like in a high‐dimensional space, such as a multistory structure. Weighted differential evolution is included before the resampling procedure to guarantee the appropriate variety of the particle set, prevent the usage of an inadequate number of valid samples, and preserve smartphone user position accuracy. The values of the smartphone‐based sensors and BLE‐beacons are set as input to the SMC, which aids in fast approximating the posterior distributions, to speed up the particle congregation process in the proposed SMC‐based WDE approach. Lastly, the robustness and efficacy of the suggested technique more accurately reflect the actual situation of smartphone users. According to simulation findings, the suggested approach provides improved location estimation with reduced localization error and quick convergence. The results confirm that the proposed optimal fusion‐based SMC‐WDE scheme performs 9.92% better in terms of MAPE, 15.24% for the case of MAE, and 0.031% when evaluating based on the R2 Score. [ABSTRACT FROM AUTHOR]

Purpose: To validate a novel deep learning‐based metal artifact correction (MAC) algorithm for CT, namely, AI‐MAC, in preclinical setting with comparison to conventional MAC and virtual monochromatic imaging (VMI) technique. Materials and methods: An experimental phantom was designed by consecutively inserting two sets of pedicle screws (size Φ 6.5 × 30‐mm and Φ 7.5 × 40‐mm) into a vertebral specimen to simulate the clinical scenario of metal implantation. The resulting MAC, VMI, and AI‐MAC images were compared with respect to the metal‐free reference image by subjective scoring, as well as by CT attenuation, image noise, signal‐to‐noise ratio (SNR), contrast‐to‐noise ratio (CNR), and correction accuracy via adaptive segmentation of the paraspinal muscle and vertebral body. Results: The AI‐MAC and VMI images showed significantly higher subjective scores than the MAC image (all p < 0.05). The SNRs and CNRs on the AI‐MAC image were comparable to the reference (all p > 0.05), whereas those on the VMI were significantly lower (all p < 0.05). The paraspinal muscle segmented on the AI‐MAC image was 4.6% and 5.1% more complete to the VMI and MAC images for the Φ 6.5 × 30‐mm screws, and 5.0% and 5.1% for the Φ 7.5 × 40‐mm screws, respectively. The vertebral body segmented on the VMI was closest to the reference, with only 3.2% and 7.4% overestimation for Φ 6.5 × 30‐mm and Φ 7.5 × 40‐mm screws, respectively. Conclusions: Using metal‐free reference as the ground truth for comparison, the AI‐MAC outperforms VMI in characterizing soft tissue, while VMI is useful in skeletal depiction. [ABSTRACT FROM AUTHOR]

The close‐range autonomous air combat has gained significant attention from researchers involved in applications related to artificial intelligence (AI). A majority of the previous studies on autonomous air combat were focused on one‐on‐one air combat scenarios, however, the modern air combat is mostly conducted in formations. With regard to the aforementioned factors, a novel hierarchical maneuvering control architecture is introduced that is applied to the multi‐aircraft close‐range air combat scenario, which can handle air combat scenarios with variable‐size formation. Subsequently, three air combat sub‐tasks are designed, and recurrent soft actor‐critic (RSAC) algorithm combined with competitive self‐play (SP) is incorporated to learn the sub‐strategies. A novel hierarchical multi‐agent reinforcement learning (HMARL) algorithm is proposed to obtain the high‐level strategy for target and sub‐strategy selection. The training performance of the training algorithm of sub‐strategies and high‐level strategy in different air combat scenarios is evaluated. The obtained strategies are analyzed and it is found that the formations exhibit effective cooperative behavior in symmetric and asymmetric scenarios. Finally, the ideas of engineering implementation of the maneuvering control architecture are given. The study provides a solution for future multi‐aircraft autonomous air combat. [ABSTRACT FROM AUTHOR]

It is shown experimentally a new digital optical decoding scheme based on the transmission or polarized light at p polarization planes using a K‐Nearest Neighbour (KNN) algorithm through a single‐mode optical fibre at 633 nm. The optical power signal is sent at p polarization planes which constitute p classes required for signal bit recognition. Results show that it is possible to recognize 32 polarizations planes, 5 bits, using 4 features corresponding to the measurement of optical power at 4 different angles at the photodetector side with an average assertiveness of 99.02%. [ABSTRACT FROM AUTHOR]

The article presents the discussion on competitiveness being a determining factor for the survival of businesses. Topics include sciences research to combine experimental approaches with theoretical modelling tools from systems theory, physics, engineering, computer science; and situation driven organizations on the search for simulation models.

Deep learning has emerged as the leading method in machine learning, spawning a rapidly growing field of academic research and commercial applications across medicine. Deep learning could have particular relevance to rheumatology if correctly utilized. The greatest benefits of deep learning methods are seen with unstructured data frequently found in rheumatology, such as images and text, where traditional machine learning methods have struggled to unlock the trove of information held within these data formats. The basis for this success comes from the ability of deep learning to learn the structure of the underlying data. It is no surprise that the first areas of medicine that have started to experience impact from deep learning heavily rely on interpreting visual data, such as triaging radiology workflows and computer‐assisted colonoscopy. Applications in rheumatology are beginning to emerge, with recent successes in areas as diverse as detecting joint erosions on plain radiography, predicting future rheumatoid arthritis disease activity, and identifying halo sign on temporal artery ultrasound. Given the important role deep learning methods are likely to play in the future of rheumatology, it is imperative that rheumatologists understand the methods and assumptions that underlie the deep learning algorithms in widespread use today, their limitations and the landscape of deep learning research that will inform algorithm development, and clinical decision support tools of the future. The best applications of deep learning in rheumatology must be informed by the clinical experience of rheumatologists, so that algorithms can be developed to tackle the most relevant clinical problems. [ABSTRACT FROM AUTHOR]

This study explores the impact of artificial intelligence (AI) on user entrepreneurs, driving factors and user entrepreneurship process combined with its characteristics and development trends. Moreover, this study sorts out the logic of user entrepreneurship change in the AI era. This study achieves four findings. First, AI contributes to entrepreneurs by collecting large user data and intelligently analysing them to obtain optimal entrepreneurial judgements and decisions. Second, entrepreneurs may use AI systems to understand users' potential needs and get user demand information (e.g. more accurate and more major shortcomings of the products). Third, AI assists entrepreneurs in obtaining robust data of product users, including leading and ordinary users, for a wider audience. Fourth, AI replaces the original intergenerational product replacement model with intermittent and periodic characteristics for entrepreneurial activities and reforms the three‐stage user entrepreneurial process: product element deconstruction, product verification matching and innovative product commercialization. This study provides a feasible direction for the key research issues. [ABSTRACT FROM AUTHOR]

Diabetic foot ulcer (DFU) is one of the most serious and alarming diabetic complications, which often leads to high amputation rates in diabetic patients. Machine learning is a part of the field of artificial intelligence, which can automatically learn models from data and better inform clinical decision‐making. We aimed to develop an accurate and explainable prediction model to estimate the risk of in‐hospital amputation in patients with DFU. A total of 618 hospitalised patients with DFU were included in this study. The patients were divided into non‐amputation, minor amputation or major amputation group. Light Gradient Boosting Machine (LightGBM) and 5‐fold cross‐validation tools were used to construct a multi‐class classification model to predict the three outcomes of interest. In addition, we used the SHapley Additive exPlanations (SHAP) algorithm to interpret the predictions of the model. Our area under the receiver‐operating‐characteristic curve (AUC) demonstrated a 0.90, 0.85 and 0.86 predictive ability for non‐amputation, minor amputation and major amputation outcomes, respectively. Taken together, our data demonstrated that the developed explainable machine learning model provided accurate estimates of the amputation rate in patients with DFU during hospitalisation. Besides, the model could inform individualised analyses of the patients' risk factors. [ABSTRACT FROM AUTHOR]

This study proposes a novel method to improve the fault identification performance of transformers. First, to couple multiple factors, a high‐dimensional feature map composed of the feature gas concentrations and some associated variables is constructed. Second, the deep residual shrinkage network is revised using the updated alternating direction multiplier, and the newly constructed variable soft thresholding is proposed to eliminate constant deviations. In addition, the fast iterative shrinkage‐thresholding algorithm is adopted, as it can speed up the determination of the threshold. For the output end, the uniform manifold approximation and projection algorithm are adopted to ensure the integrity of the local optimal solution and the global solution. Compared with traditional dissolved gas analysis methods, the novel refined deep residual shrinkage network exhibits superior precision, which is justified through experiments. The results show that the recognition accuracy of the new model is more than 1.3% higher than that of the existing methods. The new method has good scalability in power applications and fault prevention. [ABSTRACT FROM AUTHOR]

Objective: To develop an A.I‐based automatic descriptor that detects and grades, from selfie pictures, 23 facial signs, hairs included, as a help to making‐up procedures. Material and Methods: The selfie images taken in very different conditions by 3326 women and men were used to create (90% of dataset) and validate (10% of dataset) a new algorithm architecture to appraise and grade 23 different facial signs such as lips, nose, eye color, eyebrows, eyelashes, and hair color as defined by makeup artists. Each selfie image was annotated by 12 experts and defined references to train Artificial Intelligence (A.I)‐based algorithm. Results: As some the 23 signs present a continuous or discontinuous feature, these were analyzed by two different statistical approaches. The results provided by the automatic descriptor system were not only in good agreement with the expert's assessments but were even found of a better precision and reproducibility. This automatic descriptor system has proven a good and robust accuracy despite the very variable conditions in the acquisition of selfie pictures. Conclusion: Such automatic descriptor system seems providing a valuable help in making‐up procedures and may extend to other activities such as Skincare or Haircare. As such it should allow large investigations to better evaluate the consumers' needs of esthetical improvements. [ABSTRACT FROM AUTHOR]

Policy PointsWith increasing integration of artificial intelligence and machine learning in medicine, there are concerns that algorithm inaccuracy could lead to patient injury and medical liability.While prior work has focused on medical malpractice, the artificial intelligence ecosystem consists of multiple stakeholders beyond clinicians. Current liability frameworks are inadequate to encourage both safe clinical implementation and disruptive innovation of artificial intelligence.Several policy options could ensure a more balanced liability system, including altering the standard of care, insurance, indemnification, special/no‐fault adjudication systems, and regulation. Such liability frameworks could facilitate safe and expedient implementation of artificial intelligence and machine learning in clinical care. [ABSTRACT FROM AUTHOR]

The article presents the discussion on research on artificial intelligence (AI). Topics include made possible by the availability of big data and the rapid growth in information and communications technologies (ICT); and AI and blockchain proving to be quite a powerful combination showing the integration of AI and blockchain affecting many aspects.