Hasil untuk "artificial intelligence"

P. Brézillon, P. Bouquet

Jakub Olczak, Niklas Fahlberg, A. Maki et al.

Background and purpose — Recent advances in artificial intelligence (deep learning) have shown remarkable performance in classifying non-medical images, and the technology is believed to be the next technological revolution. So far it has never been applied in an orthopedic setting, and in this study we sought to determine the feasibility of using deep learning for skeletal radiographs. Methods — We extracted 256,000 wrist, hand, and ankle radiographs from Danderyd’s Hospital and identified 4 classes: fracture, laterality, body part, and exam view. We then selected 5 openly available deep learning networks that were adapted for these images. The most accurate network was benchmarked against a gold standard for fractures. We furthermore compared the network’s performance with 2 senior orthopedic surgeons who reviewed images at the same resolution as the network. Results — All networks exhibited an accuracy of at least 90% when identifying laterality, body part, and exam view. The final accuracy for fractures was estimated at 83% for the best performing network. The network performed similarly to senior orthopedic surgeons when presented with images at the same resolution as the network. The 2 reviewer Cohen’s kappa under these conditions was 0.76. Interpretation — This study supports the use for orthopedic radiographs of artificial intelligence, which can perform at a human level. While current implementation lacks important features that surgeons require, e.g. risk of dislocation, classifications, measurements, and combining multiple exam views, these problems have technical solutions that are waiting to be implemented for orthopedics.

V. Dunjko, H. Briegel

Quantum information technologies, and intelligent learning systems, are both emergent technologies that will likely have a transforming impact on our society. The respective underlying fields of research -- quantum information (QI) versus machine learning (ML) and artificial intelligence (AI) -- have their own specific challenges, which have hitherto been investigated largely independently. However, in a growing body of recent work, researchers have been probing the question to what extent these fields can learn and benefit from each other. QML explores the interaction between quantum computing and ML, investigating how results and techniques from one field can be used to solve the problems of the other. Recently, we have witnessed breakthroughs in both directions of influence. For instance, quantum computing is finding a vital application in providing speed-ups in ML, critical in our "big data" world. Conversely, ML already permeates cutting-edge technologies, and may become instrumental in advanced quantum technologies. Aside from quantum speed-up in data analysis, or classical ML optimization used in quantum experiments, quantum enhancements have also been demonstrated for interactive learning, highlighting the potential of quantum-enhanced learning agents. Finally, works exploring the use of AI for the very design of quantum experiments, and for performing parts of genuine research autonomously, have reported their first successes. Beyond the topics of mutual enhancement, researchers have also broached the fundamental issue of quantum generalizations of ML/AI concepts. This deals with questions of the very meaning of learning and intelligence in a world that is described by quantum mechanics. In this review, we describe the main ideas, recent developments, and progress in a broad spectrum of research investigating machine learning and artificial intelligence in the quantum domain.

D. Bonderman

SummaryDecision-making is complex in modern medicine and should ideally be based on available data, structured knowledge and proper interpretation in the context of an individual patient. Automated algorithms, also termed artificial intelligence that are able to extract meaningful patterns from data collections and build decisions upon identified patterns may be useful assistants in clinical decision-making processes. In this article, artificial intelligence-based studies in clinical cardiology are reviewed. The text also touches on the ethical issues and speculates on the future roles of automated algorithms versus clinicians in cardiology and medicine in general.

Fabio Massimo Zanzotto

Little by little, newspapers are revealing the bright future that Artificial Intelligence (AI) is building. Intelligent machines will help everywhere. However, this bright future has a dark side: a dramatic job market contraction before its unpredictable transformation. Hence, in a near future, large numbers of job seekers will need financial support while catching up with these novel unpredictable jobs. This possible job market crisis has an antidote inside. In fact, the rise of AI is sustained by the biggest knowledge theft of the recent years. Learning AI machines are extracting knowledge from unaware skilled or unskilled workers by analyzing their interactions. By passionately doing their jobs, these workers are digging their own graves. In this paper, we propose Human-in-the-loop Artificial Intelligence (HIT-AI) as a fairer paradigm for Artificial Intelligence systems. HIT-AI will reward aware and unaware knowledge producers with a different scheme: decisions of AI systems generating revenues will repay the legitimate owners of the knowledge used for taking those decisions. As modern Robin Hoods, HIT-AI researchers should fight for a fairer Artificial Intelligence that gives back what it steals.

Anton Korinek, Anton Korinek, Joseph E. Stiglitz et al.

Ryan Calo

Sun-Chul Park, Young-Han Kim

System logs generated in IoT-based and sensor-driven cloud environments encode execution traces and complex relationships among services, functions, and data stores. In many IoT deployments, telemetry is pre-processed at the edge and then integrated into backend services (e.g., application servers and databases) for analytics and operations. During this integration, service executions record relational dependencies (e.g., function-to-data-store interactions) as operational logs (or aggregated statistics), which constitute key evidence for operating sensor-driven services. We therefore evaluate TLV using publicly reproducible backend execution logs as a representative backend model and discuss the generality and limitations of this choice. However, most existing retrieval-augmented generation (RAG) approaches remain document-centric, representing logs as flat textual chunks that fail to preserve execution flow and entity relationships, which are critical for diagnosing complex service execution pipelines in sensor-driven cloud backends. In this study, we propose Trace-LogVector (TLV), a relational log representation that transforms system logs into trace-level retrieval units while explicitly preserving execution order and entity interactions. TLV is constructed based on the Chunk as Relational Data (CARD) design principle, which represents execution flows using entity-centric multi-chunk structures rather than single aggregated text chunks. To evaluate the impact of relational log representation, we conduct controlled experiments comparing single-chunk and CARD-based multi-chunk TLV under identical embedding and retrieval settings. Retrieval performance is quantitatively assessed using Hit@5 and Mean Reciprocal Rank at 5 (MRR@5). Experimental results show that the proposed multi-chunk TLV achieves a Hit@5 of 1.000 and an MRR@5 of 0.900, consistently outperforming the single-chunk baseline across all evaluation queries. These findings demonstrate that preserving execution contexts and entity relationships as relational retrieval units is a key factor in improving RAG-based system log analysis for monitoring and diagnosing large-scale sensor networks and cloud systems.

Fahri Yavuz İlki, Emre Bülbül, Yusuf Kadir Topçu et al.

Objective: Artificial intelligence (AI)-based large language models (LLMs), such as ChatGPT-4.0, are increasingly being considered for clinical decision-making support. However, their reliability in providing clinical recommendations for varicocele-related infertility remains to be thoroughly evaluated. This study aimed to evaluate the reliability of ChatGPT-4.0 in providing clinical recommendations for patients with varicocele-related infertility. Materials and Methods: A standardized clinical scenario was created involving a 32-year-old male with varicocele and oligoasthenoteratozoospermia, including details from physical examination, hormonal profile, and semen analysis based on the World Health Organization 6th edition criteria. Sixteen diagnostic and therapeutic questions were developed and submitted to ChatGPT-4.0. The AI-generated responses were reviewed by 24 experienced urologists specializing in varicocele management, who rated the recommendations using a 5-point Likert scale. Results: The urologists demonstrated an 80.2% agreement, 10.7% disagreement, and 9.1% neutrality with ChatGPT-4.0 recommendations. For 14 of the 16 questions, the majority of urologists either agreed or strongly agreed with ChatGPT-4.0. Recommendations regarding varicocelectomy indication, antioxidant usage, the female partner age greater than 35, follow-up after varicocelectomy, testosterone deficiency, and normospermic varicocele showed the highest consensus. However, lower agreement rates were noted for microsurgical varicocelectomy (54.1%) and preoperative sperm cryopreservation (16.7%). Conclusion: ChatGPT-4.0 demonstrates reliability in providing clinical recommendations in most scenarios related to varicocele treatment, showing strong agreement with expert clinicians. However, specific “gray zone” scenarios requiring individualized decision-making highlight limitations; emphasizing the importance of experienced clinical judgment. ChatGPT-4.0 can serve as a reliable informational tool regarding varicocele treatment but should be used with caution in complex clinical decisions requiring personalized evaluation.

Pariwat Ongsulee

K. Benke, G. Benke

Artificial intelligence and automation are topics dominating global discussions on the future of professional employment, societal change, and economic performance. In this paper, we describe fundamental concepts underlying AI and Big Data and their significance to public health. We highlight issues involved and describe the potential impacts and challenges to medical professionals and diagnosticians. The possible benefits of advanced data analytics and machine learning are described in the context of recently reported research. Problems are identified and discussed with respect to ethical issues and the future roles of professionals and specialists in the age of artificial intelligence.

Jiaying Liu, Xiangjie Kong, Feng Xia et al.

The field of artificial intelligence (AI) has shown an upward trend of growth in the 21st century (from 2000 to 2015). The evolution in AI has advanced the development of human society in our own time, with dramatic revolutions shaped by both theories and techniques. However, the multidisciplinary and fast-growing features make AI a field in which it is difficult to be well understood. In this paper, we study the evolution of AI at the beginning of the 21st century using publication metadata extracted from 9 top-tier journals and 12 top-tier conferences of this discipline. We find that the area is in the sustainable development and its impact continues to grow. From the perspective of reference behavior, the decrease in self-references indicates that the AI is becoming more and more open-minded. The influential papers/researchers/institutions we identified outline landmarks in the development of this field. Last but not least, we explore the inner structure in terms of topics’ evolution over time. We have quantified the temporal trends at the topic level and discovered the inner connection among these topics. These findings provide deep insights into the current scientific innovations, as well as shedding light on funding policies.

Peng Wang

Abstract Background The annotation of protein functions constitutes a key connection between genetic sequences, molecular conformations, and biochemical roles, driving progress in biomedical studies. Traditional experimental methods are time-consuming and resource-intensive, making it difficult to meet the demand for functional annotation of a vast number of proteins in the post-genomic era. The development of high-throughput sequencing technology has generated a large amount of protein-protein interaction (PPI) data. Prediction methods based on network topology have attracted attention due to their high efficiency and interpretability. The FSWeight algorithm calculates functional similarity by evaluating the commonality of second-order neighbors of proteins. However, it has limitations in terms of insufficient local information and a limited global perspective. Results In this study, we propose the topology-aware functional similarity (TAFS) framework, which integrates local neighborhood information with global topological information. A distance-dependent functional attenuation factor $$\gamma $$ is introduced to dynamically adjust the weights of distant nodes, and a bidirectional joint co-function probability model is constructed. Experiments show that TAFS outperforms traditional baseline methods in both single-species and cross-species evaluations. Conclusion TAFS significantly improves prediction accuracy and interpretability through refined topological modeling, providing new insights for functional inference in complex biological networks.

Kebang Lu, Minglei Guan, Zheng Cong et al.

Noise interference and multipath effects in complex marine environments seriously constrain the performance of hydroacoustic positioning systems. Traditional millisecond-level signal application and processing methods are widely used in existing research; however, it is difficult to meet the requirements of centimeter-level positioning accuracy in marine engineering. To address this problem, this study proposes a hydroacoustic positioning method based on a short baseline system for the cooperative reception of multi-channel signals. The method adopts ultra-short pulse signals with microsecond pulse width, and significantly improves the system signal-to-noise ratio and anti-interference capability through multi-channel signal alignment and coherent superposition techniques; meanwhile, a joint energy gradient-phase detection algorithm is designed, which solves the instability problem of the traditional cross-correlation algorithm in the detection of ultra-short pulse signals through the identification of signal stability intervals and accurate phase estimation. Simulation verification shows that the 8-hydrophone × 4-channel configuration can achieve 36.06% signal-to-noise gain under harsh environmental conditions (−10 dB), and the performance of the joint energy gradient-phase detection algorithm is improved by about 19.1% compared with the traditional method in an integrated manner. Marine tests further validate the engineering practicability of the method, with an average SNR gain of 2.27 dB achieved for multi-channel signal reception, and the TDOA estimation stability of the new algorithm is up to 32.0% higher than that of the conventional method, which highlights the significant advantages of the proposed method in complex marine environments. The results show that the proposed method can effectively mitigate the noise interference and multipath effects in complex marine environments, significantly improve the accuracy and stability of hydroacoustic positioning, and provide reliable technical support for centimeter-level accuracy applications in marine engineering.

LI Gai, WANG Lei, SUN Xinjuan, XIANG Guangyuan, WANG Tianyuan, CHE Jianfang, CHEN Jin'an

Diabetic foot is often accompanied by vascular and neural damage, infections, and inflammation, which interact to hinder wound healing. The current treatment methods include debridement, infection control, vascular repair, pressure reduction, moist dressings, and blood glucose management. Despite these efforts, challenges such as ischemia, infection, neuropathy, and metabolic issues can lead to slow healing, even tissue necrosis and severe infections, with amputation as a last resort. Theres a clear need for innovative diagnostic and treatment methods of diabetic foot. In recent years, remarkable progress has been made in this field. First, the application of new noninvasive imaging, molecular biomarkers, artificial intelligence and machine learning, new wearable devices, remote monitoring and genetic research will contribute to the early detection and accurate diagnosis of diabetic foot. Secondly, the treatment progress of biomaterials and regenerative medicine, stem cell therapy, hyperbaric oxygen therapy, new drugs and delivery systems, photodynamic therapy, artificial intelligence and digital health can provide more comprehensive treatment options, allowing drugs to reach the site of infection more precisely, reducing systemic side effects, and providing new options for the treatment of diabetic foot ulcers. Future research will focus on seeking more effective and personalized treatment strategies.

Leonardo Arriagada

The burgeoning capacity of artificial intelligence (AI) to generate artworks has ignited substantial interdisciplinary interest. However, the absence of a shared conceptual framework has hitherto impeded effective communication and collaboration among cognitive science, computer science, and art theory. This study addresses this lacuna through a comprehensive literature review by developing a transdisciplinary definition of an AI-generated artwork. It is proposed that an AI-generated artwork constitutes the confluence of three essential elements: (1) an autonomous AI-production of a new and surprising idea or artifact, (2) which passes an internal evaluation mechanism embedded in the very same AI, and (3) is considered a candidate of appreciation by a human audience. This definition provides a unified conceptual foundation to facilitate interdisciplinary research and deepen understanding of the nature of AI-generated art. Subsequent research should explore the applicability of this definition to diverse forms of AI-generated artworks and evaluate its implications for artistic practices.

Gabriele De Carolis, Vincenzo Giannico, Leonardo Costanza et al.

This study investigated the application of high-resolution satellite imagery from SuperDove satellites combined with machine learning algorithms to estimate the spatiotemporal variability of some winter wheat parameters, including the relative leaf chlorophyll content (RCC), relative water content (RWC), and aboveground dry matter (DM). The research was carried out within an experimental field in Southern Italy during the 2024 growing season. Different machine learning (ML) algorithms were trained and compared using spectral band data and calculated vegetation indices (VIs) as predictors. Model performance was assessed using R² and RMSE. The ML models tested were random forest (RF), support vector regressor (SVR), and extreme gradient boosting (XGB). RF outperformed the other ML algorithms in the prediction of RCC when using VIs as predictors (R² = 0.81) and in the prediction of the RWC and DM when using spectral bands data as predictors (R² = 0.71 and 0.87, respectively). Model explainability was assessed with the SHAP method. A SHAP analysis highlighted that GNDVI, Cl1, and NDRE were the most important VIs for predicting RCC, while yellow and red bands were the most important for DM prediction, and yellow and nir bands for RWC prediction. The best model found for each target was used to model its seasonal trend and produce a variability map. This approach highlights the potential of integrating ML and high-resolution satellite imagery for the remote monitoring of wheat, which can support sustainable farming practices.

Logaiswari Indiran, Chen Fu, Noraindah Abdullah Fahim et al.

Abstract Eco-innovative methodologies are crucial for achieving sustainability by integrating environmental considerations into the innovation process. The study conducts a systematic literature review of eco-innovation methodologies, focusing on approaches such as life cycle assessment (LCA), environmental management systems (EMS), eco-design, and circular economy frameworks. Using Scopus-indexed journal articles from 2008 to 2024 (15 years), a total of 291 papers were initially retrieved, and 130 were selected based on relevance to eco-innovation methodology, peer-reviewed publication status, and language. Citation impact and methodological robustness were evaluated during the analysis phase, not as initial filters. The findings of this study highlight the evolution of eco-innovation research, the dominance of certain methodologies, and geographical research disparities. We critically assess the advantages, limitations, and industry applications of each methodology, contributing to the field by providing a comparative framework for future research. This study also identifies underrepresented yet promising methodologies such as Soft Systems Methodology (SSM), Causal Loop Diagrams (CLD), Sustainability Balanced Scorecard (SBSC), and Ecological Footprint Analysis, which may offer a broader comparative foundation for future research. Furthermore, recent advances in artificial intelligence (AI) applications are integrated into the discussion, illustrating how machine learning and data-driven tools are transforming life cycle assessment (LCA) modeling, real-time environmental management system (EMS) monitoring, and generative eco-design processes.

Weiwei Yin, Guofang Ren, Guowei Zhang

In the current context of China's higher education, work stress is a frequent challenge for young university teachers, often co-occurring with job burnout. This is a significant issue that is associated with both their teaching effectiveness and their career development. The purpose of this study is to explore the mediating roles of AI literacy and psychological resilience in the relationship between work stress and job burnout among young university teachers, as well as how AI literacy moderates the associations among work stress, psychological resilience, and job burnout of these young teachers. A nationwide survey was conducted, involving 411 university teachers. The main findings are as follows: (1) Both AI literacy and psychological resilience play mediating roles in the relationship between work stress and job burnout. When acting as multiple mediating variables, their model fit significantly outperforms the case where they serve as single mediating variables respectively. (2) The study finds that AI literacy moderates the associations between work stress and job burnout, as well as between psychological resilience and job burnout. In essence, the intensity of these relationships varies with the level of AI literacy. (3) The mediating effect of psychological resilience is also associated with AI literacy, suggesting that AI literacy can moderate the way psychological resilience mediates the relationship between work stress and job burnout. These research findings provide practical evidence and recommendations for alleviating job burnout through improvements in AI literacy and psychological resilience among young university teachers.

Nouf Khalid Al-Kahtani, Arun Vijay Subbarayalu, Vinoth Raman et al.

The increasing universal acceptance of artificial intelligence (AI) in healthcare systems is driving advancements, with clinical documentation at the forefront. This research aimed to gain insight into the views, perspectives, and influencing factors on AI implementation in clinical documentation among healthcare professionals [HCPs] and students, with special consideration for the obstacles and driving factors that influence the adoption of AI. A cross-sectional survey design was employed, involving 437 participants, comprising HCPs (n = 173) and health science students (n = 264). Statistical analysis, including descriptive and inferential methods, was applied to interpret the gathered data. Most participants (68.3%) had previously learned about AI for application in clinical documentation, but fewer (41.2%) were actively using it. HCPs, as well as students, demonstrated a positive perception of AI performance (76.5%), but expressed concerns about accuracy (53.8%) and the need for data privacy (61.4%). Reliability and accuracy (92.7%) emerged as key factors, followed by efficiency (87.3%), maintaining data privacy (84.9%), and peer adoption (72.1%), which influenced adoption. AI benefits were viewed differently by HCPs and students, with the students being more optimistic (p < 0.05). The successful implementation of AI in clinical documentation was considered to rely on training requirements (89.6%), the presence of technical support (76.2%), and the development of guidelines (81.5%). Although there is widespread acceptance of AI for clinical documentation among the participants, the success of implementation can only be realized by addressing areas such as accuracy, data privacy concerns, and providing adequate training and support to relevant stakeholders involved.