Artificial Intelligence Research Research Articles

Artificial intelligence in myopia in children: current trends and future directions.

Myopia is one of the major causes of visual impairment globally, with myopia and its complications thus placing a heavy healthcare and economic burden. With most cases of myopia developing during childhood, interventions to slow myopia progression are most effective when implemented early. To address this public health challenge, artificial intelligence has emerged as a potential solution in childhood myopia management. The bulk of artificial intelligence research in childhood myopia was previously focused on traditional machine learning models for the identification of children at high risk for myopia progression. Recently, there has been a surge of literature with larger datasets, more computational power, and more complex computation models, leveraging artificial intelligence for novel approaches including large-scale myopia screening using big data, multimodal data, and advancing imaging technology for myopia progression, and deep learning models for precision treatment. Artificial intelligence holds significant promise in transforming the field of childhood myopia management. Novel artificial intelligence modalities including automated machine learning, large language models, and federated learning could play an important role in the future by delivering precision medicine, improving health literacy, and allowing the preservation of data privacy. However, along with these advancements in technology come practical challenges including regulation and clinical integration.

A summit on a Global Patient co-Owned Cloud (GPOC)

BackgroundDuring the last thirty years the world has benefited from the increasing adoption to cloud-based storage of personal health records. The concept of a Global Patient co-Owned Cloud (GPOC) of personal health records is presented in the GPOC Series, which contains a systematic review and meta-analysis, a global survey among 100% of the UN member states and a technical sandbox environment. GPOC contains patient co-ownership of personal health records. The global survey showed a consensus for the realisation of a GPOC.MethodsHere, we present a Delphi styled GPOC Summit. It also contains a final vote, that validates the results of the previous GPOC Survey. Hence, the Summit contains both multiple rounds of discussion and a voting.ResultsAt the GPOC Summit a unanimous consensus for patient co-ownership of the personal health records was expressed. The majority of participants underlined that GPOC may benefit global health, transfers of the personal health records globally and the dissemination of evidence-based medicine across the globe. The plausible advantages for patients, both refugees and travellers and for developing economies were underlined. GPOC, as a large substrate for global medical artificial intelligence research, was highlighted. There was unanimous positive interest for GPOC. The GPOC Summit agreed on patient co-ownership of personal health records. This collective agreement underscores the potential of GPOC to enhance global health outcomes, facilitate seamless exchange of health data, and advance medical research worldwide.ConclusionsThe summit emphasised the potential benefits of GPOC for diverse populations and economies, highlighting its potential role in improving healthcare access and infrastructure. Moving forward, coordination with relevant initiatives could enhance interoperability and standardisation in healthcare data management. In conclusion, the GPOC Summit represents one step towards realising a patient-centric, globally accessible healthcare ecosystem, with possible implications for the development and global adoption of AI-driven healthcare solutions.

Prospective Approaches for Artificial Intelligence Integration in Indian School Libraries

School libraries play a crucial role in the educational ecosystem, offering a variety of resources and services that contribute significantly to students’ academic success, personal development, and lifelong learning.The integration of Artificial Intelligence (AI) in school libraries brings about several benefits and opportunities, enhancing the overall learning experience for students and streamlining library operations.Libraries are dynamic institutions, and their tools need to evolve to keep pace with technological advancements and changing user expectations. Regular assessments, feedback from users, and collaboration with the broader library community can help identify areas for improvement and guide the development of more effective library science tools.While libraries can function without incorporating AI, certain challenges and limitations may arise in the absence of AI technologies. In this paper, we focus on AI research in the field of library science is essential for leveraging technology to its fullest potential, improving efficiency, enhancing user experiences, and ensuring that libraries remain dynamic and relevant in the digital age. It allows libraries to adapt to changing user needs and technological advancements, ultimately contributing to the overall effectiveness and impact of library services.

Artificial intelligence investments reduce risks to critical mineral supply

This paper employs insights from earth science on the financial risk of project developments to present an economic theory of critical minerals. Our theory posits that back-ended critical mineral projects that have unaddressed technical and non-technical barriers, such as those involving lithium and cobalt, exhibit an additional risk for investors which we term the “back-ended risk premium”. We show that the back-ended risk premium increases the cost of capital and, therefore, has the potential to reduce investment in the sector. We posit that the back-ended risk premium may also reduce the gains in productivity expected from artificial intelligence (AI) technologies in the mining sector. Progress in AI may, however, lessen the back-ended risk premium itself by shortening the duration of mining projects and the required rate of investment by reducing the associated risk. We conclude that the best way to reduce the costs associated with energy transition is for governments to invest heavily in AI mining technologies and research.

Application of artificial intelligence in rheumatic disease: a bibliometric analysis

The utilization of artificial intelligence (AI) in rheumatic diseases has enhanced the diagnostic accuracy of rheumatic diseases, enabled the prediction of patient outcomes, expanded treatment options, and facilitated the provision of individualized medical solutions. The research in this field has been progressively growing in recent years. Consequently, there is a need for bibliometric analysis to elucidate the current state of advancement and predominant research foci in AI applications within rheumatic diseases. Additionally, it is crucial to identify key contributors and their interrelations in this field. This study aimed to conduct a bibliometric analysis to investigate the current research hotspots and collaborative networks in the application of AI in rheumatic disease in recent years. A comprehensive search was conducted in Web of Science for articles on artificial intelligence in rheumatic diseases, published in SSCI and SCI-EXPANDED until January 1, 2024. Utilizing software tools like VOSviewers and CiteSpace, we analyzed various parameters including publication year, journal, country, institution, and authorship. This analysis extended to examining cited authors, generating reference and citation network graphs, and creating co-citation network and keyword maps. Additionally, research hotspots and trends in this domain were evaluated. As of January 1, 2024, a total of 3508 articles have been published on the application of artificial intelligence (AI) in rheumatic disease, exhibiting a steady rise in both the annual publication frequency and rate. “Scientific Reports” emerged as the leading journal in terms of relevant publications. The United States stood out as the predominant country in terms of the volume of published papers, with the University of California, San Francisco (UCSF) being the most prolific and frequently cited institution. Among authors, Young Ho Lee and Valentina Pedoia were noted for their significant contributions, with Pedoia achieving the highest average citation count per publication. Machine learning emerged as a prominent and central keyword. The trend indicates a growing interest in AI research within rheumatologic diseases, with its role expected to become increasingly pivotal in the field. This study presents a comprehensive summary of research trends and developments in the application of artificial intelligence (AI) in rheumatic diseases. It offers insights into potential collaborations and prospects for future research, clarifying the research frontiers and emerging directions in recent years. The findings of this study serve as a valuable reference for scholars studying rheumatology and immunology.

Biomedical Data Science, Artificial Intelligence, and Ethics: Navigating Challenges in the Face of Explosive Growth.

Advances in biomedical data science and artificial intelligence (AI) are profoundly changing the landscape of healthcare. This article reviews the ethical issues that arise with the development of AI technologies, including threats to privacy, data security, consent, and justice, as they relate to donors of tissue and data. It also considers broader societal obligations, including the importance of assessing the unintended consequences of AI research in biomedicine. In addition, this article highlights the challenge of rapid AI development against the backdrop of disparate regulatory frameworks, calling for a global approach to address concerns around data misuse, unintended surveillance, and the equitable distribution of AI's benefits and burdens. Finally, a number of potential solutions to these ethical quandaries are offered. Namely, the merits of advocating for a collaborative, informed, and flexible regulatory approach that balances innovation with individual rights and public welfare, fostering a trustworthy AI-driven healthcare ecosystem, are discussed.

Big Tech influence over AI research revisited: Memetic analysis of attribution of ideas to affiliation

There exists a growing discourse around the domination of Big Tech on the landscape of artificial intelligence (AI) research, yet our comprehension of this phenomenon remains cursory. This paper aims to broaden and deepen our understanding of Big Tech's reach and power within AI research. It highlights the dominance not merely in terms of sheer publication volume but rather in the propagation of new ideas or memes. Current studies often oversimplify the concept of influence to the share of affiliations in academic papers, typically sourced from limited databases such as arXiv or specific academic conferences.The main goal of this paper is to unravel the specific nuances of such influence, determining which AI ideas are predominantly driven by Big Tech entities. By employing network and memetic analysis on AI-oriented paper abstracts and their citation network, we are able to grasp a deeper insight into this phenomenon. By utilizing two databases: OpenAlex and S2ORC, we are able to perform such analysis on a much bigger scale than previous attempts.Our findings suggest that while Big Tech-affiliated papers are disproportionately more cited in some areas, the most cited papers are those affiliated with both Big Tech and Academia. Focusing on the most contagious memes, their attribution to specific affiliation groups (Big Tech, Academia, mixed affiliation) seems equally distributed between those three groups. This suggests that the notion of Big Tech domination over AI research is oversimplified in the discourse.

Artificial intelligence research in Canadian hospitals: The development of metropolitan competencies.

This study explores the deployment of Artificial Intelligence (AI) in Canadian hospitals from 2000 to 2021, focusing on metropolitan areas. We investigate how local public and private research ecosystems and links to national and international AI hubs influence the adoption of AI in healthcare. Our analysis shows that AI research outputs from public institutions have a significant impact on AI competences in hospitals. In addition, collaborations between hospitals are critical to the successful integration of AI. Metropolitan areas such as Toronto, Montreal, and Vancouver are leading the way in AI deployment. These findings highlight the importance of local AI research capabilities and international hospital collaborations and provide guidance to policy-makers and health leaders to drive the diffusion of AI technology in healthcare.

Artificial intelligence in the educational stages from kindergarten to university: A systematic review of Arab studies from 2010 to 2023

Artificial Intelligence (AI) is increasingly being used at different stages of teaching and learning, from kindergarten to university education, to enhance the learning and development of learners. Research has proven that AI can perform multiple roles aimed at effectively facilitating learning and improving performance. However, there is a scarcity of studies on how these studies are conducted and how AI is used in kindergarten compared to other levels. Studies and research published in Arabic on AI in education were reviewed. Although there are few studies in kindergarten. The current review reflects the issues and trends of research in AI in the stages of education that researchers have recently addressed in Arab countries. This can easily and efficiently draw the attention of other researchers to these issues. 207 research papers published in various Arab countries were reviewed during the period from 2010 to 2023 to explore the roles and trends of AI in education. Three main issues were identified: research, interaction, and performance according to the technology-based learning model. It turns out that studies focused more on comparing learning with AI to find more effective learning methods with traditional education methods, on the stages of university education, and on the aspects of cognition, skills and affect. Some suggestions have been presented for future research on AI in educational stages, to serve as a reference for researchers to conduct related research.

Publications on COVID-19 and artificial intelligence: trends and lessons

Purpose: This study investigates shifts in scientific research focus, particularly the decline in COVID-19-related research and the rapid growth of artificial intelligence (AI) publications.Methods: We analyzed publication data from the Web of Science, comparing yearly publication counts for COVID-19 and AI research. The study also tracked changes in the impact factors of leading journals like <i>Science</i> and <i>Nature</i>, alongside those of top AI journals over the past decade. Additionally, we reviewed the top 10 most cited articles in 2021 from <i>Science</i> and <i>Nature</i> and the most influential AI publications from the past five years according to Google Scholar. The impact trends of the top 100 AI journals in computer science were also explored.Results: The analysis reveals a noticeable decline in COVID-19 related publications as the pandemic urgency diminishes, contrasted with the continued rapid growth of AI research. Impact factors for prestigious journals have shifted, with AI journals increasingly dominating the academic landscape. The review of top-cited articles further emphasizes these trends.Conclusion: Our findings indicate a significant shift in research priorities, with AI emerging as a dominant field poised to address future challenges, reflecting the evolving focus of the scientific community.

Artificial intelligence in individualized retinal disease management.

Owing to the rapid development of modern computer technologies, artificial intelligence (AI) has emerged as an essential instrument for intelligent analysis across a range of fields. AI has been proven to be highly effective in ophthalmology, where it is frequently used for identifying, diagnosing, and typing retinal diseases. An increasing number of researchers have begun to comprehensively map patients' retinal diseases using AI, which has made individualized clinical prediction and treatment possible. These include prognostic improvement, risk prediction, progression assessment, and interventional therapies for retinal diseases. Researchers have used a range of input data methods to increase the accuracy and dependability of the results, including the use of tabular, textual, or image-based input data. They also combined the analyses of multiple types of input data. To give ophthalmologists access to precise, individualized, and high-quality treatment strategies that will further optimize treatment outcomes, this review summarizes the latest findings in AI research related to the prediction and guidance of clinical diagnosis and treatment of retinal diseases.

Network model with internal complexity bridges artificial intelligence and neuroscience.

Artificial intelligence (AI) researchers currently believe that the main approach to building more general model problems is the big AI model, where existing neural networks are becoming deeper, larger and wider. We term this the big model with external complexity approach. In this work we argue that there is another approach called small model with internal complexity, which can be used to find a suitable path of incorporating rich properties into neurons to construct larger and more efficient AI models. We uncover that one has to increase the scale of the network externally to stimulate the same dynamical properties. To illustrate this, we build a Hodgkin-Huxley (HH) network with rich internal complexity, where each neuron is an HH model, and prove that the dynamical properties and performance of the HH network can be equivalent to a bigger leaky integrate-and-fire (LIF) network, where each neuron is a LIF neuron with simple internal complexity.

Optical coherence tomography angiography in neovascular age-related macular degeneration: comprehensive review of advancements and future perspective.

Optical coherence tomography angiography (OCTA) holds promise in enhancing the care of various retinal vascular diseases, including neovascular age-related macular degeneration (nAMD). Given nAMD's vascular nature and the distinct vasculature of macular neovascularization (MNV), detailed analysis is expected to gain significance. Research in artificial intelligence (AI) indicates that en-face OCTA views may offer superior predictive capabilities than spectral domain optical coherence tomography (SD-OCT) images, highlighting the necessity to identify key vascular parameters. Analyzing vasculature could facilitate distinguishing MNV subtypes and refining diagnosis. Future studies correlating OCTA parameters with clinical data might prompt a revised classification system. However, the combined utilization of qualitative and quantitative OCTA biomarkers to enhance the accuracy of diagnosing disease activity remains underdeveloped. Discrepancies persist regarding the optimal biomarker for indicating an active lesion, warranting comprehensive prospective studies for validation. AI holds potential in extracting valuable insights from the vast datasets within OCTA, enabling researchers and clinicians to fully exploit its OCTA imaging capabilities. Nevertheless, challenges pertaining to data quantity and quality pose significant obstacles to AI advancement in this field. As OCTA gains traction in clinical practice and data volume increases, AI-driven analysis is expected to further augment diagnostic capabilities.

Machine Psychology: integrating operant conditioning with the non-axiomatic reasoning system for advancing artificial general intelligence research.

This paper presents an interdisciplinary framework, Machine Psychology, which integrates principles from operant learning psychology with a particular Artificial Intelligence model, the Non-Axiomatic Reasoning System (NARS), to advance Artificial General Intelligence (AGI) research. Central to this framework is the assumption that adaptation is fundamental to both biological and artificial intelligence, and can be understood using operant conditioning principles. The study evaluates this approach through three operant learning tasks using OpenNARS for Applications (ONA): simple discrimination, changing contingencies, and conditional discrimination tasks. In the simple discrimination task, NARS demonstrated rapid learning, achieving 100% correct responses during training and testing phases. The changing contingencies task illustrated NARS's adaptability, as it successfully adjusted its behavior when task conditions were reversed. In the conditional discrimination task, NARS managed complex learning scenarios, achieving high accuracy by forming and utilizing complex hypotheses based on conditional cues. These results validate the use of operant conditioning as a framework for developing adaptive AGI systems. NARS's ability to function under conditions of insufficient knowledge and resources, combined with its sensorimotor reasoning capabilities, positions it as a robust model for AGI. The Machine Psychology framework, by implementing aspects of natural intelligence such as continuous learning and goal-driven behavior, provides a scalable and flexible approach for real-world applications. Future research should explore using enhanced NARS systems, more advanced tasks and applying this framework to diverse, complex tasks to further advance the development of human-level AI.

Stress Detection through Compound Facial Expressions Using Neural Networks

Human face is the most dynamic part of the body that conveys information about the instant emotions. Facial expression analysis starts from early 1900s where later on scientists identify the six basic facial expressions as Anger, Disgust, Fear, Happiness, Sadness, Surprise and Neutral with the pioneering studies of psychologists. In the last decades, the acceleration in artificial intelligence and computer vision research makes it possible to automatically detect facial expressions through images. Furthermore, micro expressions, muscle movements and compound facial expressions; that are the combinations of the basic expressions can be also analyzed with computer vision algorithms. The main motivation in automatic facial expression analysis is to support human-computer. Furthermore, facial expression analysis can be a driver for automatic emotion analysis. In this study, we propose a novel method to detect stress indicators on the frontal face images. The detection procedure is based on compound facial expression analysis. 49 couples of 6 basic facial expressions where one is dominating, and the other is the complementary expression are employed. iCV-MEFED facial expression dataset is used in the experiments where video and image samples are provided for every compound facial expression class. The training and testing of compound facial expressions are done using a deep neural network. The robust representations of faces are achieved using a fusion method that combines deep texture features and the action units on the face. Then, through the appropriate grouping of the compound expressions, the system can detect the signs of stress. The proposed approach obtains encouraging results, and it is open to further improvements.

Understanding the Growth of Artificial Intelligence in Educational Research through Bibliometric Analysis

The purpose of this study was to investigate research trends in artificial intelligence studies related to education that were published in the Web of Science database. This review conducted a bibliometric analysis of 4673 articles published between 1975 and 2023 and explored trends in several areas, including the annual distribution of publications, frequently studied topics, top authors, top countries, top universities/departments, top journals and publishers, and top funders. The findings highlighted that the number of studies increased exponentially after 2010. The most often used terms in artificial intelligence research in education were machine learning, deep learning, and data mining. Studies in higher education have been more prevalent than studies in elementary and secondary education. The USA, mainland China, and Australia were the three most productive nations. Most productive authors were connected to academic institutions in Taiwan, Hong Kong, or mainland China. Furthermore, there was little cooperation among the most productive authors andcountries. In addition to the abundance of journals on educational technology, it is crucial to emphasize the dearth of publications on education across different disciplines. To understand how artificial intelligence can support new practices in educational research, interdisciplinary interest and support are needed.

Claude 3 Opus and ChatGPT With GPT-4 in Dermoscopic Image Analysis for Melanoma Diagnosis: Comparative Performance Analysis.

Recent advancements in artificial intelligence (AI) and large language models (LLMs) have shown potential in medical fields, including dermatology. With the introduction of image analysis capabilities in LLMs, their application in dermatological diagnostics has garnered significant interest. These capabilities are enabled by the integration of computer vision techniques into the underlying architecture of LLMs. This study aimed to compare the diagnostic performance of Claude 3 Opus and ChatGPT with GPT-4 in analyzing dermoscopic images for melanoma detection, providing insights into their strengths and limitations. We randomly selected 100 histopathology-confirmed dermoscopic images (50 malignant, 50 benign) from the International Skin Imaging Collaboration (ISIC) archive using a computer-generated randomization process. The ISIC archive was chosen due to its comprehensive and well-annotated collection of dermoscopic images, ensuring a diverse and representative sample. Images were included if they were dermoscopic images of melanocytic lesions with histopathologically confirmed diagnoses. Each model was given the same prompt, instructing it to provide the top 3 differential diagnoses for each image, ranked by likelihood. Primary diagnosis accuracy, accuracy of the top 3 differential diagnoses, and malignancy discrimination ability were assessed. The McNemar test was chosen to compare the diagnostic performance of the 2 models, as it is suitable for analyzing paired nominal data. In the primary diagnosis, Claude 3 Opus achieved 54.9% sensitivity (95% CI 44.08%-65.37%), 57.14% specificity (95% CI 46.31%-67.46%), and 56% accuracy (95% CI 46.22%-65.42%), while ChatGPT demonstrated 56.86% sensitivity (95% CI 45.99%-67.21%), 38.78% specificity (95% CI 28.77%-49.59%), and 48% accuracy (95% CI 38.37%-57.75%). The McNemar test showed no significant difference between the 2 models (P=.17). For the top 3 differential diagnoses, Claude 3 Opus and ChatGPT included the correct diagnosis in 76% (95% CI 66.33%-83.77%) and 78% (95% CI 68.46%-85.45%) of cases, respectively. The McNemar test showed no significant difference (P=.56). In malignancy discrimination, Claude 3 Opus outperformed ChatGPT with 47.06% sensitivity, 81.63% specificity, and 64% accuracy, compared to 45.1%, 42.86%, and 44%, respectively. The McNemar test showed a significant difference (P<.001). Claude 3 Opus had an odds ratio of 3.951 (95% CI 1.685-9.263) in discriminating malignancy, while ChatGPT-4 had an odds ratio of 0.616 (95% CI 0.297-1.278). Our study highlights the potential of LLMs in assisting dermatologists but also reveals their limitations. Both models made errors in diagnosing melanoma and benign lesions. These findings underscore the need for developing robust, transparent, and clinically validated AI models through collaborative efforts between AI researchers, dermatologists, and other health care professionals. While AI can provide valuable insights, it cannot yet replace the expertise of trained clinicians.

Ethical approaches in designing autonomous and intelligent systems: a comprehensive survey towards responsible development

AbstractOver the past decade, significant progress in artificial intelligence (AI) has spurred the adoption of its algorithms, addressing previously daunting challenges. Alongside these remarkable strides, there has been a simultaneous increase in model complexity and reliance on opaque AI models, lacking transparency. In numerous scenarios, the systems themselves may necessitate making decisions entailing ethical dimensions. Consequently, it has become imperative to devise solutions to integrate ethical considerations into AI system development practices, facilitating broader utilization of AI systems across various domains. Research endeavors should explore innovative approaches to enhance ethical principles in AI systems, fostering greater transparency, accountability, and trustworthiness. Upholding fundamental individual rights, human dignity, autonomy, privacy, equality, and fairness, while mitigating potential harm, remains paramount. Considering ethical values and ensuring compliance with ethical requirements throughout the development lifecycle of autonomous and intelligent systems nurture trust and reliability in their utilization. Ethical considerations should be ingrained within organizational procedures guiding AI research activities, establishing robust frameworks that address ethical concerns and reflect the ethical implications of AI-based systems. This paper presents an overview of ethical approaches and processes aimed at integrating ethical considerations into AI system development practices. It underscores the significance of ethical frameworks in fostering ethical AI implementation and ensuring the ethical integrity of AI technologies.

Contrastive subspace distribution learning for novel category discovery in high-dimensional visual data

Discovering novel visual categories, particularly in recognition tasks, is a prominent area of research in artificial intelligence. Visual data, such as images and videos, is inherently high-dimensional and they often exhibit low-dimensional subspace structures, making the task of uncovering novel low-dimensional subspace structures a substantial challenge. To address this challenge, we develop a novel subspace discovery method, consisting of four steps. First, we introduce a base-expressive network (BENet) designed to learn coefficients that express the relationships between samples and bases. Additionally, these coefficients are normalized as a probability distribution using the softmax function. Second, to enhance this process, we propose a Contrastive Subspace Distribution Learning (CSDL) technique, utilizing Rényi Divergence. CSDL combines self-supervised and supervised contrastive distances between the normalized coefficients, considering all samples and labeled samples, respectively. Third, the base-expressive coefficients are subsequently computed by the learned SENet, which effectively transfers subspace information from labeled samples to unlabeled ones. Finally, we employ spectral clustering on the predicted coefficients to automatically assign categories to unlabeled samples. Extensive experimental results demonstrate that our approach surpasses related methods in the realms of category discovery, unsupervised clustering, and semi-supervised (subspace) clustering.

Challenges of the Intellectual Property System in Pharmaceutical Innovations Resulting from Artificial Intelligence

The patent system has long been criticized for limiting access to medicines. Dramatic advances in artificial intelligence and machine learning technology present a revolutionary opportunity in drug discovery, formulation and testing of dosage forms. The pharmaceutical industry claims that patenting is necessary to encourage innovation in the risky, lengthy, and costly research and development (R&amp;D) process. But it still does not provide logical evidence about the actual effects of patents on innovation. The increasing use of artificial intelligence in research is intensifying the debate about pharmaceutical patents. Inventions created or enabled by artificial intelligence raise questions about patentability and patent policy in general. Faster and more efficient research and development weakens the justification for pharmaceutical patents. Research findings suggest that despite the necessity of continuing incentives for drug research and development, lawmakers should consider alternative systems that prioritize access alongside incentives to advance healthcare as a human right.