Artificial Intelligence Research Research Articles

Development of an Open-Source Dataset of Flat-Mounted Images for the Murine Oxygen-Induced Retinopathy Model of Ischemic Retinopathy.

To describe an open-source dataset of flat-mounted retinal images and vessel segmentations from mice subject to the oxygen-induced retinopathy (OIR) model. Flat-mounted retinal images from mice killed at postnatal days 12 (P12), P17, and P25 used in prior OIR studies were compiled. Mice subjected to normoxic conditions were killed at P12, P17, and P25, and their retinas were flat-mounted for imaging. Major blood vessels from the OIR images were manually segmented by four graders (JSC, HKR, KBL, JM), with cross-validation performed to ensure similar grading. Overall, 1170 images were included in this dataset. Of these images, 111 were of normoxic mice retina, and 1048 were mice subject to OIR. The majority of images from OIR mice were obtained at P17. The 50 images obtained from an external dataset, OIRSeg, did not have age labels. All images were manually segmented and used in the training or testing of a previously published deep learning algorithm. This is the first open-source dataset of original and segmented flat-mounted retinal images. The dataset has potential applications for expanding the development of generalizable and larger-scale artificial intelligence and analyses for OIR. This dataset is published online and publicly available at dx.doi.org/10.6084/m9.figshare.23690973. This open access dataset serves as a source of raw data for future research involving big data and artificial intelligence research concerning oxygen-induced retinopathy.

Bibliometric Analysis of Math and Artificial Intelligence Research

This study conducts a comprehensive bibliometric analysis to explore the landscape of research in mathematics and artificial intelligence (AI). Using Scopus as the primary data source, we identify key publications and trends in these fields. Through VOSviewer, we visualize networks of keywords and collaborations among researchers and institutions. The analysis reveals the prominence of topics such as AI and mathematics in academic discourse, as well as the central role played by countries like the United States, the United Kingdom, and China in research collaboration. Limitations include potential biases in data sources and the reliance on keywords for analysis. Future research could integrate alternative metrics and qualitative analyses to provide a more nuanced understanding of research trends and impact.

Current Status and Future Directions of Research on Artificial Intelligence in Nasopharyngolaryngoscopy.

The nasopharyngolaryngoscopy (NPL) has emerged as a valuable tool for detecting early cases of head and neck cancers (HNCs). However, misdiagnoses and missed diagnoses are still common phenomena. The expertise of examining physicians often serves as the primary limiting factor, leading to issues such as incomplete visualization, imprecise identification, and unclear vision. Over recent years, the application of artificial intelligence (AI) in medical imaging, particularly in the realm of gastrointestinal endoscopy, has instigated revolutionary changes in site quality control, lesion identification, and report generation. However, there remains a lack of standardized guidelines for the proper application of NPL across various countries. While AI-related research in NPL is still in its nascent stages, it shows substantial potential for clinical application and endoscopic training. In this paper, we set our sights on reviewing the current clinical applications and summarizing the primary shortcomings of NPL. In addition, we encapsulate the progress of AI application within gastrointestinal endoscopy and NPL. Drawing from real-world clinical practice, we propose future directions and prospects for AI research in NPL. We firmly believe that the pace of clinical application of AI in NPL will accelerate significantly in the near future.

Beyond the Resource Curse: Leveraging Artificial Intelligence to Enhance Green Development Welfare

This study examines the relationship between the resource curse (RC) and green development welfare (GDW) through the influence of artificial intelligence (AI) on emerging technologies. We explore the critical question: How does resource abundance hinder green development, and how can AI mitigate this effect? Our examination of 15 years of panel data from 282 Chinese cities demonstrates a significant negative correlation: a 1% increase in RC correlates with a 0.422% reduction in GDW. AI appears as a crucial factor capable of counteracting the detrimental impact of RC, indicating that technological innovation is essential for promoting sustainable development. This study's distinctive contribution is the incorporation of AI into the GDW framework, providing novel insights into the varying impacts of RC across different levels of GDW. By differentiating resource abundance (RA) from resource dependence (RG), we illustrate that both RA and RG substantially impede GDW, with the negative effects amplifying at higher GDW levels. We discover that AI can overcome the limitations imposed by RC by reducing terms-of-trade fluctuations, mitigating Dutch disease effects, and diminishing crowding-out effects. Our research underscores the importance of AI in alleviating the adverse impacts of RC on GDW and recommends augmenting investment in AI research and application, especially in resource-rich cities, to advance sustainable development.

Distributed algorithm for parallel computation of the n queens solutions

The n-queen problem represents a classic challenge in artificial intelligence (AI) research. It involves the placement of n queens on an n x n chessboard, with the objective of ensuring that no queen threatens another. This problem has long been a source of fascination for mathematicians and computer scientists alike due to its inherent complexity. It is a well-known fact that as the value of ‘n’ increases, the problem becomes more challenging and falls into the NP problem class. Given the computational demands of the problem, parallel methods are of critical importance. It is noteworthy that scalable and parallel approaches for the n-queen problem remain to be developed. The majority of existing methods working on graphs attempt to parallelize a recursive sequential algorithm. However, the unpredictable nature of these algorithms makes it challenging to parallelize them on modern computer architectures. Consequently, we have selected an iterative algorithm from the literature in order to facilitate parallelization. This paper presents an innovative approach to parallelization that differs from traditional matrix-based strategies. The n-queen graph is distributed among a network of nodes, ensuring effective load balancing through dynamic partitioning and real-time computation. Our bespoke distributed algorithm, designed for the maximum clique problem on the n-queen graph, operates with true concurrency, thus obviating the necessity for resource or data sharing. The results of our assessment demonstrate that the parallel algorithm outperforms a cutting-edge sequential algorithm in terms of task completion time. The findings demonstrate that the speedups are almost perfect and that the workloads are distributed evenly across the network nodes. Furthermore, the results demonstrate high scalability, with task completion times decreasing as the number of nodes increases.

Models for the marrow: A comprehensive review of AI-based cell classification methods and malignancy detection in bone marrow aspirate smears.

Given the high prevalence of artificial intelligence (AI) research in medicine, the development of deep learning (DL) algorithms based on image recognition, such as the analysis of bone marrow aspirate (BMA) smears, is rapidly increasing in the field of hematology and oncology. The models are trained to identify the optimal regions of the BMA smear for differential cell count and subsequently detect and classify a number of cell types, which can ultimately be utilized for diagnostic purposes. Moreover, AI is capable of identifying genetic mutations phenotypically. This pipeline has the potential to offer an accurate and rapid preliminary analysis of the bone marrow in the clinical routine. However, the intrinsic complexity of hematological diseases presents several challenges for the automatic morphological assessment. To ensure general applicability across multiple medical centers and to deliver high accuracy on prospective clinical data, AI models would require highly heterogeneous training datasets. This review presents a systematic analysis of models for cell classification and detection of hematological malignancies published in the last 5 years (2019-2024). It provides insight into the challenges and opportunities of these DL-assisted tasks.

Emerging trends in the evolution of neuropsychology and artificial intelligence: A comprehensive analysis

Neuropsychological evaluations are valuable in neurosurgery because they comprehensively evaluate cognitive, affective, and behavioral functioning to optimize patient outcomes. Incorporating artificial intelligence (AI) into neuropsychology offers optimistic advances, with machine learning models assisting in classifying behavioral, cognitive, and functional impairments while minimizing the number of tests. AI-based approaches have demonstrated accurate classification outcomes, providing potential alternatives to time-consuming and non-ecological conventional evaluations. This research uses data from the Scopus database to examine the trends of neuropsychological-based AI in cognitive neuroscience, mental health, and neurodegenerative disorders. The study emphasized the potential of artificial intelligence in neuropsychology research and identified several research themes. The analysis of bibliometrics may efficiently assess the developments and impact of neuropsychology research, providing insights into academic output and predicting future trends. Future research should consider utilizing alternative databases, employing a multisource strategy, incorporating additional keywords, and building upon the foundational knowledge provided by this study. Despite its limitations, this study provides significant insights and paves the way for future neuropsychology-based artificial intelligence research. Furthermore, investigating significant topics and key issues in the neuropsychology and artificial intelligence debate adds new perspectives to the corpus of literature. This analysis can help identify gaps, controversies, and areas of future exploration within the field. The study also highlights the importance of learning and intelligent computation in neuropsychology, providing a conceptual methodology based on a comprehensive review of the most recent research.

Multi-dimensional Path Exploration of Artificial Intelligence Empowering Postgraduate Research Ability Improvement

The application of artificial intelligence (AI) technology in the field of graduate research is becoming more and more common, which has brought unprecedented opportunities and challenges for the enhancement of graduate research ability. The purpose of this study is to deeply analyze the multiple mechanisms of artificial intelligence promoting the improvement of graduate students' scientific research ability. Firstly, this paper systematically analyzed the theoretical foundation of artificial intelligence in graduate research, covering the application of core algorithms such as machine learning and deep learning in scientific research practice, as well as the mechanism of the integration of artificial intelligence and scientific research, including the potential of interdisciplinary cooperation, the challenges encountered in the integration process and their coping strategies. Then the application examples of artificial intelligence in graduate research practice are expounded, including but not limited to the key links of literature search and analysis, experiment design and data analysis. Then, from the two dimensions of cultivating innovative thinking and improving learning autonomy, how artificial intelligence can help improve graduate students' scientific research ability is discussed. It is believed that artificial intelligence plays an important role in generating new hypotheses, stimulating creativity, breaking through traditional thinking patterns, generating personalized learning resources, and assisting group discussion. In summary, artificial intelligence plays a vital role in improving the scientific research ability of graduate students, and provides strong technical support and innovation motivation for graduate students' scientific research work.

Benchmark Dataset Applied to Quantitative Precipitation Estimation and Forecasting of Qinghai

We collected short-duration heavy precipitation events around weather radar since 2020 and get radar base data of these events. Then, we extracted radar products like Composite Reflectivity (CR), echo top height (ET) and Vertically Integrated Liquid Water (VIL) from radar base data after quality control as the input of dataset, and merge the surface precipitation of 6 minutes with background field using multi-grid variation to generate precipitation label by 6 minutes. Thus, we constructed two datasets artificial intelligence application QpefQH for short-duration heavy precipitation. QpefQH dataset has a scale of more than 15,000 images available for radar echo extrapolation and quantitative precipitation estimation tasks based on deep learning. QpefQH dataset also includes multi-source data such as satellite, numerical model, sounding, lightning, land surface temperature, pressure, wind and other observation data. QpefQH can be shared as a benchmark dataset for artificial intelligence research on short-duration heavy precipitation in Qinghai Province.

Current Situation and Prospect of Geospatial AI in Air Pollution Prediction

Faced with increasingly serious environmental problems, scientists have conducted extensive research, among which the importance of air quality prediction is becoming increasingly prominent. This article briefly reviews the utilization of geographic artificial intelligence (AI) in air pollution. Firstly, this paper conducts a literature metrology analysis on the research of geographical AI used in air pollution. That is, 607 documents are retrieved from the Web of Science (WOS) using appropriate keywords, and literature metrology analysis is conducted using Citespace to summarize research hotspots and frontier countries in this field. Among them, China plays a constructive role in the fields of geographic AI and air quality research. The data characteristics of Earth science and the direction of AI utilization in the field of Earth science were proposed. It then quickly expanded to investigate and research air pollution. In addition, based on summarizing the current status of Artificial Neural Network (ANN), Recurrent Neural Network (RNN), and hybrid neural network models in predicting air quality (mainly PM2.5), this article also proposes areas for improvement. Finally, this article proposes prospects for future research in this field. This study aims to summarize the development trends and research hotspots of the utilization of geographic AI in the prediction of air quality, as well as prediction methods, to provide direction for future research.

Artificial intelligence and microbiome research: Evolution of hotspots, research trends, and thematic-based narrative review.

Artificial intelligence (AI) and microbiome have emerged in recent years as transformative fields with far-reaching implications for various biomedical domains. This paper presents a comprehensive bibliometric analysis examining the intersection of AI and the microbiome (AIM). The study aims to provide information on this interdisciplinary field's research landscape, trends, and emerging topics. Using a systematic approach, data-driven studies were extracted from the Scopus database on 23 November 2023 and analyzed using the VOSviewer and Bibliometrix applications. The regression coefficient of 0.94 and the yearly growth rate of 7.46% in AIM production indicate a consistent increase over time. Identification of essential contributors, organizations, and nations illuminated cooperative networks and research hotspots. The trend themes are the gut microbiome, disease prediction, machine learning, transfer learning, categorization, big data, artificial neural networks, chronic rhinosinusitis, epidemiology, COPD, and bronchoalveolar lavage. These hot issues in AIM reflect the present emphasis on research and developments in our knowledge of the microbiome's function in health, sickness, and individualized treatment. The findings give researchers, policymakers, and industry experts a thorough picture of the research environment and guide future paths in AIM's fascinating and promising subject.

Building AI-enabled capabilities for improved environmental and manufacturing performance: evidence from the US and the UK

Drawing on the emerging literature on the environmental and manufacturing roles of artificial intelligence (AI), this study proposes a research model that connects AI-enabled capabilities to environmental and manufacturing performance. We collected data from 128 managers from manufacturing companies in the United States (US) and the United Kingdom (UK) and tested the proposed model using partial least squares structural equation modelling. The findings lend strong empirical support to the research model and confirm the influence of AI-enabled capabilities on environmental and manufacturing performance. We also discovered significant differences in AI use and impact between US and UK respondents, highlighting the importance of context in AI research. These findings advance practice and theory while also contributing to the emerging literature on artificial intelligence for sustainability.

In-depth analysis of research hotspots and emerging trends in AI for retinal diseases over the past decade.

The application of Artificial Intelligence (AI) in diagnosing retinal diseases represents a significant advancement in ophthalmological research, with the potential to reshape future practices in the field. This study explores the extensive applications and emerging research frontiers of AI in retinal diseases. This study aims to uncover the developments and predict future directions of AI research in retinal disease over the past decade. This study analyzes AI utilization in retinal disease research through articles, using citation data sourced from the Web of Science (WOS) Core Collection database, covering the period from January 1, 2014, to December 31, 2023. A combination of WOS analyzer, CiteSpace 6.2 R4, and VOSviewer 1.6.19 was used for a bibliometric analysis focusing on citation frequency, collaborations, and keyword trends from an expert perspective. A total of 2,861 articles across 93 countries or regions were cataloged, with notable growth in article numbers since 2017. China leads with 926 articles, constituting 32% of the total. The United States has the highest h-index at 66, while England has the most significant network centrality at 0.24. Notably, the University of London is the leading institution with 99 articles and shares the highest h-index (25) with University College London. The National University of Singapore stands out for its central role with a score of 0.16. Research primarily spans ophthalmology and computer science, with "network," "transfer learning," and "convolutional neural networks" being prominent burst keywords from 2021 to 2023. China leads globally in article counts, while the United States has a significant research impact. The University of London and University College London have made significant contributions to the literature. Diabetic retinopathy is the retinal disease with the highest volume of research. AI applications have focused on developing algorithms for diagnosing retinal diseases and investigating abnormal physiological features of the eye. Future research should pivot toward more advanced diagnostic systems for ophthalmic diseases.

Unraveling the Mysteries of Alzheimer's Disease Using Artificial Intelligence.

Alzheimer's disease (AD) is a multidimensional, complex condition that affects individuals all over the world. Despite decades of experimental and clinical research that has revealed various processes, many concerns concerning the origin of Alzheimer's disease remain unresolved. Despite the notion that there isn't a complete set of jigsaw pieces, the growing number of public data-sharing initiatives that collect biological, clinical, and lifestyle data from those suffering from Alzheimer's disease has resulted in virtually endless volumes of knowledge about the disorder, far beyond what humans can comprehend. Furthermore, combining Big Data from multi- -omics research gives a chance to investigate the pathophysiological processes underlying the whole biological spectrum of Alzheimer's disease. To improve knowledge on the subject of Alzheimer's disease, Artificial Intelligence (AI) offers a wide variety of approaches for evaluating complex and significant data. The introduction of next-generation sequencing and microarray technologies has resulted in significant growth in genetic data research. When it comes to assessing such complex projects, AI technology beats conventional statistical techniques of data processing. This review focuses on current research and potential challenges for AI in Alzheimer's disease research. This article, in particular, examines how AI may assist healthcare practitioners with patient stratification, estimating an individual's chance of AD conversion, and diagnosing AD using computer-aided diagnostic methodologies. Ultimately, scientists want to develop individualized, efficient medicines.

China and the U.S. produce more impactful AI research when collaborating together

Artificial Intelligence (AI) has become a disruptive technology, promising to grant a significant economic and strategic advantage to nations that harness its power. China, with its recent push towards AI adoption, is challenging the U.S.’s position as the global leader in this field. Given AI’s massive potential, as well as the fierce geopolitical tensions between China and the U.S., several recent policies have been put in place to discourage AI scientists from migrating to, or collaborating with, the other nation. Nevertheless, the extent of talent migration and cross-border collaboration are not fully understood. Here, we analyze a dataset of over 350,000 AI scientists and 5,000,000 AI papers. We find that since 2000, China and the U.S. have led the field in terms of impact, novelty, productivity, and workforce. Most AI scientists who move to China come from the U.S., and most who move to the U.S. come from China, highlighting a notable bidirectional talent migration. Moreover, the vast majority of those moving in either direction have Asian ancestry. Upon moving, those scientists continue to collaborate frequently with those in the origin country. Although the number of collaborations between the two countries has increased since the dawn of the millennium, such collaborations continue to be relatively rare. A matching experiment reveals that the two countries have always been more impactful when collaborating than when each works without the other. These findings suggest that instead of suppressing cross-border migration and collaboration between the two nations, the science could benefit from promoting such activities.

A Comprehensive Survey on Requirements and Design of AI-Powered Clinical Intelligence Systems in Healthcare

With the increasing complexity of healthcare services, artificial intelligence (AI) is emerging as a vital tool to support medical practitioners by providing real-time clinical insights, automating routine documentation, and enhancing patient engagement. This survey investigates the design requirements, technical challenges, and ethical considerations for AI-powered clinical intelligence systems intended for healthcare environments. Our study is based on a survey conducted with healthcare professionals, patients, and AI researchers, capturing diverse perspectives on functionality, usability, data privacy, and ethical concerns. This paper synthesizes these insights into a detailed requirement analysis that can guide the development of clinically effective, ethically sound, and user-centric AI systems. Through this analysis, we aim to lay the groundwork for creating AIdriven tools that improve clinical workflows, foster better patient outcomes, and support healthcare professionals in delivering quality care.

Advancing clinical MRI exams with artificial intelligence: Japan's contributions and future prospects.

In this narrative review, we review the applications of artificial intelligence (AI) into clinical magnetic resonance imaging (MRI) exams, with a particular focus on Japan's contributions to this field. In the first part of the review, we introduce the various applications of AI in optimizing different aspects of the MRI process, including scan protocols, patient preparation, image acquisition, image reconstruction, and postprocessing techniques. Additionally, we examine AI's growing influence in clinical decision-making, particularly in areas such as segmentation, radiation therapy planning, and reporting assistance. By emphasizing studies conducted in Japan, we highlight the nation's contributions to the advancement of AI in MRI. In the latter part of the review, we highlight the characteristics that make Japan a unique environment for the development and implementation of AI in MRI examinations. Japan's healthcare landscape is distinguished by several key factors that collectively create a fertile ground for AI research and development. Notably, Japan boasts one of the highest densities of MRI scanners per capita globally, ensuring widespread access to the exam. Japan's national health insurance system plays a pivotal role by providing MRI scans to all citizens irrespective of socioeconomic status, which facilitates the collection of inclusive and unbiased imaging data across a diverse population. Japan's extensive health screening programs, coupled with collaborative research initiatives like the Japan Medical Imaging Database (J-MID), enable the aggregation and sharing of large, high-quality datasets. With its technological expertise and healthcare infrastructure, Japan is well-positioned to make meaningful contributions to the MRI-AI domain. The collaborative efforts of researchers, clinicians, and technology experts, including those in Japan, will continue to advance the future of AI in clinical MRI, potentially leading to improvements in patient care and healthcare efficiency.

A comparative study of Chinese and foreign research relevant to the diffusion drift model in the last decade

Abstract. This study evaluates the differential research approaches of Chinese and international scholars regarding the drift diffusion model in mental processes through a literature review from the last decade. The application of DDM theory in cognitive research varies between Chinese and foreign study methodologies. The differences in decision-making mechanisms among cultural groups result in this outcome. Inconsistency has been identified in international academic research. They cover reward rate optimization, decision thresholds, decision time, and more. On the contrary, advancements in Chinese studies involve the development of innovative models. They are Bayesian and semi-parametric. These are designed for mixtures combining drift-diffusion processes and the inverse Gaussian distribution. In addition, they encompass elucidating the neural processes and implementing DDM techniques. This use of DDM reveals the rewarding perception and decision-making linked to depression. It also reveals the brain patterns of social anxiety. These comparisons aim to summarize the differences between Chinese and foreign studies. They also analyze them. It will find the causes of these differences. Moreover, it will determine how Chinese clinical care can use them. The DDM studies show its importance in decision-making patterns. Scholars have the opportunity to apply these tools in a variety of scientific fields, including neuroscience, psychology, artificial intelligence, and cognitive research.

Neural networks for abstraction and reasoning

For half a century, artificial intelligence research has attempted to reproduce the human qualities of abstraction and reasoning - creating computer systems that can learn new concepts from a minimal set of examples, in settings where humans find this easy. While specific neural networks are able to solve an impressive range of problems, broad generalisation to situations outside their training data has proved elusive. In this work, we look at several novel approaches for solving the ion & Reasoning Corpus (ARC). This is a dataset of abstract visual reasoning tasks introduced to test algorithms on broad generalization. Despite three international competitions with $100,000 in prizes, the best algorithms still fail to solve a majority of ARC tasks. The best solvers today rely on complex hand-crafted rules, without using machine learning at all. We revisit whether recent advances in neural networks allow progress on this task, or whether an entirely different class of models are required. First, we adapt the DreamCoder neurosymbolic reasoning solver to ARC. DreamCoder automatically writes programs in a bespoke domain-specific language to perform reasoning, using a neural network to mimic human intuition. We present the Perceptual ion and Reasoning Language (PeARL) language, which allows DreamCoder to solve ARC tasks, and propose a new recognition model that allows us to significantly improve on the previous best implementation. We also propose a new encoding and augmentation scheme that allows large language models (LLMs) to solve ARC tasks, and find that the largest models can solve some ARC tasks. LLMs are able to solve a different group of problems to state-of-the-art solvers, and provide an interesting way to complement other approaches. We perform an ensemble analysis, combining systems to achieve better results than any system alone and analysing individual strengths. However, it is sobering to see that approaches based on neural networks still lag behind existing hand-crafted solvers, and we suggest avenues for future improvements. Our findings with the ensemble model may indicate that a diversity of methods might be necessary to solve problems in ARC. Humans likely employ diverse strategies to solve ARC. Studies involving human participants to identify the strategies they employ to solve ARC could provide valuable insights for future AI approaches. Finally, we publish the arckit Python library to make future research on ARC easier.

Artificial intelligence research in radiation oncology: a practical guide for the clinician on concepts and methods.

The use of artificial intelligence (AI) holds great promise for radiation oncology, with many applications being reported in the literature, including some of which are already in clinical use. These are mainly in areas where AI provides benefits in efficiency (such as automatic segmentation and treatment planning). Prediction models that directly impact patient decision-making are far less mature in terms of their application in clinical practice. Part of the limited clinical uptake of these models may be explained by the need for broader knowledge, among practising clinicians within the medical community, about the processes of AI development. This lack of understanding could lead to low commitment to AI research, widespread scepticism, and low levels of trust. This attitude towards AI may be further negatively impacted by the perception that deep learning is a "black box" with inherently low transparency. Thus, there is an unmet need to train current and future clinicians in the development and application of AI in medicine. Improving clinicians' AI-related knowledge and skills is necessary to enhance multidisciplinary collaboration between data scientists and physicians, that is, involving a clinician in the loop during AI development. Increased knowledge may also positively affect the acceptance and trust of AI. This paper describes the necessary steps involved in AI research and development, and thus identifies the possibilities, limitations, challenges, and opportunities, as seen from the perspective of a practising radiation oncologist. It offers the clinician with limited knowledge and experience in AI valuable tools to evaluate research papers related to an AI model application.