Hasil untuk "artificial intelligence"

J. Horvat

Joshua S. Gans, Avi Goldfarb

O. Adir, M. Poley, Gal Chen et al.

Artificial intelligence (AI) and nanotechnology are two fields that are instrumental in realizing the goal of precision medicine—tailoring the best treatment for each cancer patient. Recent conversion between these two fields is enabling better patient data acquisition and improved design of nanomaterials for precision cancer medicine. Diagnostic nanomaterials are used to assemble a patient‐specific disease profile, which is then leveraged, through a set of therapeutic nanotechnologies, to improve the treatment outcome. However, high intratumor and interpatient heterogeneities make the rational design of diagnostic and therapeutic platforms, and analysis of their output, extremely difficult. Integration of AI approaches can bridge this gap, using pattern analysis and classification algorithms for improved diagnostic and therapeutic accuracy. Nanomedicine design also benefits from the application of AI, by optimizing material properties according to predicted interactions with the target drug, biological fluids, immune system, vasculature, and cell membranes, all affecting therapeutic efficacy. Here, fundamental concepts in AI are described and the contributions and promise of nanotechnology coupled with AI to the future of precision cancer medicine are reviewed.

N. Schork

K. Masters

Abstract Artificial intelligence (AI) is a growing phenomenon, and will soon facilitate wide-scale changes in many professions, including medical education. In order for medical educators to be properly prepared for AI, they will need to have at least a fundamental knowledge of AI in relation to learning and teaching, and the extent to which it will impact on medical education. This Guide begins by introducing the broad concepts of AI by using fairly well-known examples to illustrate AI’s implications within the context of education. It then considers the impact of AI on medicine and the implications of this impact for educators trying to educate future doctors. Drawing on these strands, it then identifies AI’s direct impact on the methodology and content of medical education, in an attempt to prepare medical educators for the changing demands and opportunities that are about to face them because of AI.

T. Maddox, J. Rumsfeld, Philip R. O. Payne

Artificial intelligence (AI) is gaining high visibility in the realm of health care innovation. Broadly defined, AI is a field of computer science that aims to mimic human intelligence with computer systems.1 This mimicry is accomplished through iterative, complex pattern matching, generally at a speed and scale that exceed human capability. Proponents suggest, often enthusiastically, that AI will revolutionize health care for patients and populations. However, key questions must be answered to translate its promise into action.

Wang Tong, Azhar Hussain, Wang Bo et al.

Recently, the advancement in communications, intelligent transportation systems, and computational systems has opened up new opportunities for intelligent traffic safety, comfort, and efficiency solutions. Artificial intelligence (AI) has been widely used to optimize traditional data-driven approaches in different areas of the scientific research. Vehicle-to-everything (V2X) system together with AI can acquire the information from diverse sources, can expand the driver’s perception, and can predict to avoid potential accidents, thus enhancing the comfort, safety, and efficiency of the driving. This paper presents a comprehensive survey of the research works that have utilized AI to address various research challenges in V2X systems. We have summarized the contribution of these research works and categorized them according to the application domains. Finally, we present open problems and research challenges that need to be addressed for realizing the full potential of AI to advance V2X systems.

A. Basile, Alexandre Yahi, N. Tatonetti

Interventional pharmacology is one of medicine's most potent weapons against disease. These drugs, however, can result in damaging side effects and must be closely monitored. Pharmacovigilance is the field of science that monitors, detects, and prevents adverse drug reactions (ADRs). Safety efforts begin during the development process, using in vivo and in vitro studies, continue through clinical trials, and extend to postmarketing surveillance of ADRs in real-world populations. Future toxicity and safety challenges, including increased polypharmacy and patient diversity, stress the limits of these traditional tools. Massive amounts of newly available data present an opportunity for using artificial intelligence (AI) and machine learning to improve drug safety science. Here, we explore recent advances as applied to preclinical drug safety and postmarketing surveillance with a specific focus on machine and deep learning (DL) approaches.

C. Prentice, Sergio Dominique Lopes, Xuequn Wang

ABSTRACT Emotional intelligence as personal intelligence and artificial intelligence as a machine intelligence have been popular in the relevant literature over the last two decades. The current study integrates these two concepts and explores how emotional and artificial intelligence influences employee retention and performance with a focus on service employees in the hotel industry. Employee performance is operationalised into internal and external dimensions that captures employees’ task efficiency over both internal and external service encounters with co-workers and customers respectively. The data were collected from a variety of different ranking hotels. The results show that emotional intelligence has a significant effect on employee retention and performance; whereas artificial intelligence plays a significant moderating role in employee performance. A discussion of the findings and implications concludes this paper.

Justin B. Bullock

This essay highlights the increasing use of artificial intelligence (AI) in governance and society and explores the relationship between AI, discretion, and bureaucracy. AI is an advanced information communication technology tool (ICT) that changes both the nature of human discretion within a bureaucracy and the structure of bureaucracies. To better understand this relationship, AI, discretion, and bureaucracy are explored in some detail. It is argued that discretion and decision-making are strongly influenced by intelligence, and that improvements in intelligence, such as those that can be found within the field of AI, can help improve the overall quality of administration. Furthermore, the characteristics, strengths, and weaknesses of both human discretion and AI are explored. Once these characteristics are laid out, a further exploration of the role AI may play in bureaucracies and bureaucratic structure is presented, followed by a specific focus on systems-level bureaucracies. In addition, it is argued that task distribution and task characteristics play a large role, along with the organizational and legal context, in whether a task favors human discretion or the use of AI. Complexity and uncertainty are presented as the major defining characteristics for categorizing tasks. Finally, a discussion is provided about the important cautions and concerns of utilizing AI in governance, in particular, with respect to existential risk and administrative evil.

Jinyi Wang, Congyuan Xu, Jun Yang

Low-rate Denial-of-Service (LDoS) attacks exploit periodic traffic pulses to trigger congestion while maintaining a low average rate, making them highly stealthy and difficult to distinguish from legitimate bursty traffic using threshold-based or simple statistical detectors. To address this challenge, this paper proposes DELP-Net, an end-to-end Differentiable Entropy Layer Pyramid Network for window-level online LDoS detection directly from raw traffic. DELP-Net combines a multi-scale one-dimensional convolutional pyramid with a differentiable Rényi-entropy-driven attention mechanism to capture distributional regularity and weak repetitive patterns characteristic of LDoS traffic. In addition, an entropy-conditioned temporal convolutional network is employed to model cross-window periodic dependencies in a lightweight manner, together with an entropy-regularized hybrid loss to enhance robustness under complex background traffic. Experiments on the low-rate DoS dataset show that DELP-Net achieves an average F1 score of 0.9877 across six LDoS attack types, with a detection rate of 98.69% and a false-positive rate of 1.15%, demonstrating its effectiveness and suitability for practical online intrusion detection deployments.

E. Le, Y. Wang, Yuanlu Huang et al.

This article reviews current limitations and future opportunities for the application of computer-aided detection (CAD) systems and artificial intelligence in breast imaging. Traditional CAD systems in mammography screening have followed a rules-based approach, incorporating domain knowledge into hand-crafted features before using classical machine learning techniques as a classifier. The first commercial CAD system, ImageChecker M1000, relies on computer vision techniques for pattern recognition. Unfortunately, CAD systems have been shown to adversely affect some radiologists' performance and increase recall rates. The Digital Mammography DREAM Challenge was a multidisciplinary collaboration that provided 640,000 mammography images for teams to help decrease false-positive rates in breast cancer screening. Winning solutions leveraged deep learning's (DL) automatic hierarchical feature learning capabilities and used convolutional neural networks. Start-ups Therapixel and Kheiron Medical Technologies are using DL for breast cancer screening. With increasing use of digital breast tomosynthesis, specific artificial intelligence (AI)-CAD systems are emerging to include iCAD's PowerLook Tomo Detection and ScreenPoint Medical's Transpara. Other AI-CAD systems are focusing on breast diagnostic techniques such as ultrasound and magnetic resonance imaging (MRI). There is a gap in the market for contrast-enhanced spectral mammography AI-CAD tools. Clinical implementation of AI-CAD tools requires testing in scenarios mimicking real life to prove its usefulness in the clinical environment. This requires a large and representative dataset for testing and assessment of the reader's interaction with the tools. A cost-effectiveness assessment should be undertaken, with a large feasibility study carried out to ensure there are no unintended consequences. AI-CAD systems should incorporate explainable AI in accordance with the European Union General Data Protection Regulation (GDPR).

Randi Williams, Hae Won Park, C. Breazeal

We developed a novel early childhood artificial intelligence (AI) platform, PopBots, where preschool children train and interact with social robots to learn three AI concepts: knowledge-based systems, supervised machine learning, and generative AI. We evaluated how much children learned by using AI assessments we developed for each activity. The median score on the cumulative assessment was 70% and children understood knowledge-based systems the best. Then, we analyzed the impact of the activities on children's perceptions of robots. Younger children came to see robots as toys that were smarter than them, but their older counterparts saw them more as people that were not as smart as them. Children who performed worse on the AI assessments believed that robots were like toys that were not as smart as them, however children who did better on the assessments saw robots as people who were smarter than them. We believe early AI education can empower children to understand the AI devices that are increasingly in their lives.

Yuan Chi, Yijie Dong, Lei Zhang et al.

Real-time dynamic capture of a single moving target is one of the most crucial and representative tasks in UAV capture problems. This paper proposes a multi-UAV real-time dynamic capture strategy based on a differential game model to address this challenge. In this paper, the dynamic capture problem is divided into two parts: pursuit and capture. First, in the pursuit–evasion problem based on differential games, the capture UAVs and the target UAV are treated as adversarial parties engaged in a game. The current pursuit–evasion state is modeled and analyzed according to varying environmental information, allowing the capture UAVs to quickly track the target UAV. The Nash equilibrium solution in the differential game is optimal for both parties in the pursuit–evasion process. Then, a collaborative multi-UAV closed circular pipeline control method is proposed to ensure an even distribution of capture UAVs around the target, preventing excessive clustering and thereby significantly improving capture efficiency. Finally, simulations and real-flight experiments are conducted on the RflySim platform in typical scenarios to analyze the computational process and verify the effectiveness of the proposed method. Results indicate that this approach effectively provides a solution for multi-UAV dynamic capture and achieves desirable capture outcomes.

Ricardo França Santos, Mathis Berthet

Abstract This study introduces an AI model using a random forest algorithm to predict dropout risk among engineering students at the Instituto Politécnico (IPOLI) of the Federal University of Rio de Janeiro. The model provides academic performance, demographic information, and survey responses. Key factors linked to dropout are identified, providing a practical tool for early intervention and prevention. For instance, proactive mentoring could be initiated as early as week two for students flagged by the model, facilitating timely support. Feature importance analysis highlights strong predictors, such as early GPA and socioeconomic conditions, which are correlated rather than causal. The model allows institutions to identify at-risk students early and supports strategies to enhance retention.

Maurício Vasconcellos Leão Lyrio, Rogério João Lunkes, Miklos Vasarhelyi

This study aimed to explore the use of emerging and data technologies (Rotolo et al., 2015) in the public sector, investigating their applications, challenges, and benefits through the analytical perspectives proposed by Criado et al. (2024). To amplify the analytical capacity, minimize data processing time and analyze relevant studies on the topic in high-impact journals, the study adopts a systematic literature review process, inspired by H. Gu's et al. (2024) co-piloted artificial intelligence (AI) in audit studies and informed by prior literature review methods (Lyrio et al., 2018; Page et al., 2021; Ruijer et al., 2023; Straub et al., 2023). Based on Criado’s et al. (2024) perspectives, the results showed that, at a macro level, AI and big data stand out in the formulation of public policies. At a meso level, use cases demonstrated the potential of these technologies to optimize processes and improve organizational efficiency. At a micro level, the studies highlighted the personalization of public services and improvements in interaction with citizens, although they also warned of risks such as digital exclusion and loss of trust in governments. The study concludes that research on the topic is still in an evolving phase and has prioritized ethical and regulatory issues to balance efficiency, innovation, and the democratic values of the public sector.

Jiankang Wang, Qiyuan Cao, Ye Chen et al.

The Electrochemical Machining (ECM) method is one of the most widely used processing methods in metal surface processing, due to its unique advantages. However, the electrolyte in ECM causes stray corrosion on the workpiece. To overcome these shortcomings, we have developed a no-stray-corrosion ECM method called the controllable electrolyte distribution ECM (CED-ECM) method. However, its practical application in metal surface processing remains largely unexplored. In this study, to improve the CED-ECM method, we delved deeper into the aforementioned aspects by simulating the actual ECM process using COMSOL Multiphysics and rigorously validating the simulation results through practical experimental observations. Then, our efforts led to the application of the CED-ECM method to metal surface processing for the SUS304 workpiece, producing noteworthy results that manifest in diverse cross-sectional profiles on the processed surfaces. This research demonstrates a validated simulation framework for the CED-ECM process and establishes a method for creating user-defined surface profiles by controlling pass intervals, enabling new applications in surface texturing.

Liu Yuxin

With the advent of the Transformer, the attention mechanism has been applied to Large Language Model (LLM), evolving from initial single- modal large models to today's multi-modal large models. This has greatly propelled the development of Artificial Intelligence (AI) and ushered humans into the era of large models. Single-modal large models can be broadly categorized into three types based on their application domains: Text LLM for Natural Language Processing (NLP), Image LLM for Computer Vision (CV), and Audio LLM for speech interaction. Multi-modal large models, on the other hand, can leverage multiple data sources simultaneously to optimize the model. This article also introduces the training process of the GPT series. Large models have also had a significant impact on industry and society, bringing with them a number of unresolved problems. The purpose of this article is to assist researchers in comprehending the various forms of LLM, as well as its development, pre- training architecture, difficulties, and future objectives.

Miriam López Santos, Alba Lozano, Carolina Blanco Fontao

In the current landscape, the rapid evolution of educational technology, particularly AI tools like ChatGPT, necessitates understanding how educators perceive their integration into the education system. This study uses a quantitative, non-experimental, descriptive-comparative, and cross-sectional study was conducted with 379 active teachers in Castilla y León, Spain. The research instrument, a validated questionnaire, sought to assess prior knowledge, usage, and perceptions of ChatGPT's application in educational settings. Findings reveal high awareness and exploratory use of ChatGPT among teachers, though practical implementation and specific training remain limited. Teachers acknowledge ChatGPT's potential to enhance educational processes, particularly in generating educational materials and planning tasks. However, significant concerns about plagiarism, critical thinking, and ethical use persist. Differences in perceptions are mainly influenced by specialty, age, and gender, highlighting the need for tailored training and policies to support effective and ethical AI integration in education. These insights underscore the importance of continuous professional development to harness AI's benefits while mitigating associated risks.

Lakshmi Sree Pugalenthi, Chris Garapati, Srivarshini Maddukuri et al.

Background: Varicose veins (VVs) of the lower limbs, characterized by palpable, dilated, and tortuous veins, affect 2–73% of the global population. Artificial intelligence (AI) offers significant potential to enhance healthcare efficiency and decision-making, particularly in managing VVs through improved risk factor identification, diagnosis, and treatment planning. Objective: This abstract explores the role of AI in VV management, focusing on its applications in risk detection, image analysis, treatment planning, and surgical interventions, while addressing challenges to its widespread adoption. Methods: AI leverages advanced techniques such as computer vision and deep learning to analyze patient data, including medical history, symptoms, physical examinations, and imaging (e.g., ultrasounds, venography). It identifies patterns in large datasets to support personalized treatment plans, early risk detection, and disease severity assessment. Results: AI demonstrates promise in automating VV detection and classification, assessing disease severity, and aiding treatment planning. It enhances surgical interventions through preoperative planning, intraoperative navigation, and recurrence risk prediction. However, its adoption is limited by a lack of large-scale studies, concerns over accuracy, and the need for regulatory and ethical oversight. Conclusion: AI has the potential to revolutionize VV management by improving diagnosis, treatment precision, and patient outcomes. Further research, validation, and integration are critical to overcoming current limitations and fully realizing AI’s capabilities in clinical practice.