Overview Of Artificial Intelligence Research Articles

A decade’s overview of artificial intelligence in diagnosing: a scoping review

A decade’s overview of artificial intelligence in diagnosing: a scoping review

Exploring How Education Can Leverage Artificial Intelligence for Social Good

Artificial intelligence (AI) is rapidly transforming society and industries, presenting both opportunities and ethical challenges. AI enables machines to perform tasks traditionally done by humans, such as natural language processing, pattern recognition, decision-making, and problem-solving (Brookings, 2023). In education, AI enhances teaching methodologies, student assessment, and administrative tasks through tools like intelligent tutoring systems, adaptive learning platforms, and educational chatbots. These tools offer customised learning experiences, immediate feedback, and data-driven insights. This research aims to investigate how AI can be leveraged within education to promote social good by identifying how familiar educators and students are with AI tools, identify how educators and students perceive the role of AI in education and what are the current applications of AI technologies in educational settings and how widely are they used. Finally, discuss the opportunities and ethical considerations of integrating AI in education. AI technologies can address critical social challenges such as inequality, accessibility, and personalised learning. According to Luckin et al. (2016), "AI can provide tailored educational experiences that adapt to individual learning needs, thus promoting equity in education." This exploratory research begins with an overview of AI's role and tools in education, followed by a discussion of the challenges, opportunities, and ethical considerations associated with AI integration. Understandings are drawn from educator’s response to a questionnaire and a focus group with first year and final-year third level students. This qualitative data, analysed using NVivo software, reveals key themes and significant findings on effectively utilising AI in education.

Artificial Intelligence in Higher Education: Enhancing Learning Systems and Transforming Educational Paradigms

This study ventures into the expanding role of artificial intelligence (AI) in higher education and its potential to revolutionize the learning experience. Through a qualitative approach, we investigate how advanced AI-driven educational tools can enhance learning systems through tailored learning experiences, AI-powered adaptive tutoring platforms, immersive digital learning environments, and AI-assisted assessment and feedback systems. From a theoretical perspective, we utilize the constructivist theory of learning as a study framework to explain the role of technology in AI-facilitated personalized and collaborative knowledge construction. This study was executed in three stages. First, we present a brief overview of AI and its recent impact on the education system, including its ethical considerations and implementation hurdles. In the second stage, we propose the potential implications for learning contexts through the constructivist lens, the process of AI-supported cognitive development, and the use of AI tools for optimizing educational processes through AI and transformation in educational paradigms and study work. The findings suggest that institutions can unlock new opportunities for efficiency, accessibility, and personalized learning by adopting AI technology, but they also highlight the need to address ethical considerations and implementation hurdles. This study also contributes to future studies by examining the impact of AI on student outcomes, teacher roles, and institutional practices in education, as well as exploring ethical and equity concerns in AI-driven educational settings.

Impact of Artificial Intelligence in Anatomy

Artificial intelligence is being used and integrate positively in anatomy, in which programs are available at all times, enhancing efficiency and decision-making, however, it also offers significant challenges by reducing human jobs, ethical concerns and high costs. This review provides an overview of artificial intelligence and the significant technology in the anatomy education.

AI-Led Healthcare Leadership: Unveiling Nursing Trends and Pathways Ahead

Background: Artificial intelligence (AI) is transforming healthcare systems by improving operational efficiency, simplifying patient care procedures, and improving diagnostic accuracy. Artificial intelligence (AI) technologies, like machine learning and natural language processing, present previously unheard-of chances to quickly and accurately evaluate enormous volumes of healthcare data, assisting with clinical decision-making and enhancing patient outcomes. Aim thorough examination and analysis of artificial intelligence's impact on healthcare leadership, with a particular emphasis on present nursing trends and their implications for the future. The study tries to uncover the advantages, difficulties, and consequences of AI integration by looking at how AI technologies including clinical decision support systems, predictive analytics, robots, natural language processing, and telehealth are being used in nursing practice. Method: A comprehensive analysis including research articles published between 2015 and 2024 was carried out. To give a thorough overview of AI's present and future uses in healthcare, major themes and trends were found and summarized. Results: By stressing AI's role in improving diagnostic accuracy and patient outcomes, the study highlights the technology's major contributions to drug discovery, virtual patient care, and medical imaging. Human-centered design concerns, the necessity of educational changes, and ethical challenges surrounding the application of AI surfaced as crucial topics needing attention. Conclusion: AI has enormous potential to transform healthcare by enhancing operational effectiveness, optimizing the delivery of care, and increasing diagnostic precision. Still, ethical issues must be resolved, interdisciplinary cooperation must be promoted, and educational frameworks must be improved in order to provide healthcare workers with the necessary AI skills.

Sustainability in tourism and hospitality: Artificial intelligence role in eco-friendly practices in Indian hotels

This study explores the impact of artificial intelligence (AI) on the growth and competitiveness of regional tourism and hospitality destinations. AI's application has both positive and negative effects on sustainable tourism. On the positive side, AI enhances accessibility to tourist sites, provides detailed information about attractions, simplifies understanding of costs and amenities, boosts local economies, and increases federal income. A notable downside is the replacement of human workers by machine learning technologies, which may necessitate remedial measures such as training. This review examines AI's foundational IT principles, current applications, and systems in the industry, focusing on the hotel sector. It concludes with an overview of AI’s challenges in this field, proposes a research agenda, and suggests future directions for AI development in tourism and hospitality.

An overview of artificial intelligence and its application in marketing with focus on large language models

An overview of artificial intelligence and its application in marketing with focus on large language models

APPLICATION OF ARTIFICIAL INTELLIGENCE MODELS IN 5G AND 6G COMMUNICATION NETWORKS FOR NETWORK TRAFFIC CLASSIFICATION

An overview of artificial intelligence and machine learning models that can be adapted or used for traffic classification in 5G and 6G networks is provided. Emphasis is placed on convolutional neural networks, designed to handle data with a constant network topology. Given this feature, the need to transform traffic data into formats suitable for convolutional neural networks is outlined. Models capable of traffic classification, such as LeNet-5, AlexNet, VGGNet, ResNet, 1D CNN, Inception (GoogleNet), MobileNet, and Capsule Networks are proposed. Their features, advantages, and disadvantages are summarized. The structure of 6G networks is proposed. A description of the levels of 6G networks is given, and the tasks to be solved by artificial intelligence at each level are outlined. Specific neural network architectures and existing algorithms for 6G networks are listed, including authors, year of creation, and main articles on the topic, with features. The conceptual place of artificial intelligence in 6G networks is described, considering the type of centralization. Positive and negative consequences from the implementation of artificial intelligence in 6G networks are highlighted.

Revolutionizing Healthcare with AI: Innovative Strategies in Cancer Medicine

By improving early detection, diagnosis, treatment planning, and patient management, artificial intelligence (AI) is transforming the way that cancer is treated. An overview of AI's function in cancer is given in this article, with special attention to how it advances precision medicine and improves patient outcomes. Numerous AI applications are discussed, such as predictive analytics, pathology interpretation, genetic profiling, and medical imaging analysis. Case studies highlight effective AI applications in cancer care, showcasing the technology's effectiveness in enhancing the precision of diagnoses, directing individualized treatment choices, and tracking treatment response. The paper delves into the possible advancements in early identification, therapy optimization, and patient engagement through an exploration of future directions and innovations in AI-driven oncology research. The conclusion emphasizes how AI has the ability to completely change the way cancer is treated and enhance the lives of cancer sufferers all over the world.

Learning Reimagined: AI and Mixed Reality Revolutionize Indian Education

Artificial intelligence and mixed reality are realities now, not just ideas from science fiction films. AI analyzes data and can provide interpretation, just like the human brain. The efficiency and accuracy of this are causing the globe to adopt it presently. As opposed to mixed reality (MR), which combines the actual and virtual worlds to create new locations and scenarios, where things are both digital and tangible and interact with each other in real time. With a focus on Indian education, this paper provides a thorough overview of artificial intelligence (AI) and mixed reality technologies and their prospective educational applications. Together, artificial intelligence (AI) and mixed reality can revolutionize education by enhancing learning outcomes and bringing fresh resources to the classroom that have the power to change traditional methods of instruction. Like any new technology, there are hazards involved in using it, though. Experts are even in danger because of its immense power, which has the potential to fundamentally alter several industries and cause a large number of job losses. which has the potential to fundamentally alter several industries and cause a large number of job losses. Keywords -- Mixed Reality, Artificial Intelligence, Indian Education

Examining the role of artificial intelligence to advance knowledge and address barriers to research in eating disorders.

To provide a brief overview of artificial intelligence (AI) application within the field of eating disorders (EDs) and propose focused solutions for research. An overview and summary of AI application pertinent to EDs with focus on AI's ability to address issues relating to data sharing and pooling (and associated privacy concerns), data augmentation, as well as bias within datasets is provided. In addition to clinical applications, AI can utilize useful tools to help combat commonly encountered challenges in ED research, including issues relating to low prevalence of specific subpopulations of patients, small overall sample sizes, and bias within datasets. There is tremendous potential to embed and utilize various facets of artificial intelligence (AI) to help improve our understanding of EDs and further evaluate and investigate questions that ultimately seek to improve outcomes. Beyond the technology, issues relating to regulation of AI, establishing ethical guidelines for its application, and the trust of providers and patients are all needed for ultimate adoption and acceptance into ED practice. Artificial intelligence (AI) offers a promise of significant potential within the realm of eating disorders (EDs) and encompasses a broad set of techniques that offer utility in various facets of ED research and by extension delivery of clinical care. Beyond the technology, issues relating to regulation, establishing ethical guidelines for application, and the trust of providers and patients are needed for the ultimate adoption and acceptance of AI into ED practice.

Integration of AI and Machine Learning in Semiconductor Manufacturing for Defect Detection and Yield Improvement

The integration of Artificial Intelligence (AI) and Machine Learning (ML) into semiconductor manufacturing has revolutionized defect detection and yield improvement processes. AI and ML algorithms analyze vast amounts of data generated during fabrication to enhance quality control, reduce defects, and optimize production yields. This paper provides an overview of AI and ML applications in semiconductor manufacturing, focusing on their roles in defect detection methodologies, process optimization, and yield enhancement strategies. Case studies and current advancements illustrate the transformative impact of AI and ML technologies on semiconductor fabrication, highlighting their potential to drive future advancements in microelectronics.

Generative Pre-Trained Transformer-Empowered Healthcare Conversations: Current Trends, Challenges, and Future Directions in Large Language Model-Enabled Medical Chatbots

This review explores the transformative integration of artificial intelligence (AI) and healthcare through conversational AI leveraging Natural Language Processing (NLP). Focusing on Large Language Models (LLMs), this paper navigates through various sections, commencing with an overview of AI’s significance in healthcare and the role of conversational AI. It delves into fundamental NLP techniques, emphasizing their facilitation of seamless healthcare conversations. Examining the evolution of LLMs within NLP frameworks, the paper discusses key models used in healthcare, exploring their advantages and implementation challenges. Practical applications in healthcare conversations, from patient-centric utilities like diagnosis and treatment suggestions to healthcare provider support systems, are detailed. Ethical and legal considerations, including patient privacy, ethical implications, and regulatory compliance, are addressed. The review concludes by spotlighting current challenges, envisaging future trends, and highlighting the transformative potential of LLMs and NLP in reshaping healthcare interactions.

Synergizing AI and Healthcare: Pioneering Advances in Cancer Medicine for Personalized Treatment

This paper investigates how Artificial Intelligence (AI) is changing the field of cancer medicine. It is organized into nine major sections that illustrate the profound effects of AI on different aspects of cancer care. Starting from the early phases of the disease, AI shows how it can transform conventional diagnostic methods by providing quick and accurate analyses of medical imaging, pathology slides, and genetic data. The paper then goes into the era of personalized cancer therapies, highlighting the ways in which AI helps to customize treatment based on individual genetic and molecular profiles. Finally, the paper discusses the smart revolution in healthcare, which is driven by AI integration, highlighting the impact of AI on diagnosis precision, treatment optimization, and resource allocation. Moreover, the story delves into how AI is being incorporated into healthcare outside of diagnosis and treatment, including areas like predictive modeling, ongoing monitoring, and after-treatment care. AI has the capacity to revolutionize cancer medicine by improving current practices and fostering innovation in clinical research, diagnosis modalities, and treatment planning. The paper highlights the revolutionary boundaries that AI has created, including liquid biopsies, virtual tumor boards, and the speeding up of drug discovery processes. The narrative weaves a thorough overview of AI's transformative journey in cancer care, offering insights into its current impact and the promising possibilities that lie ahead.

Artificial intelligence for automatic classification of needle EMG signals: A scoping review

ObjectiveThis scoping review provides an overview of artificial intelligence (AI), including machine and deep learning techniques, in the interpretation of clinical needle electromyography (nEMG) signals. MethodsA comprehensive search of Medline, Embase and Web of Science was conducted to find peer-reviewed journal articles. All papers published after 2010 were included. The methodological quality of the included studies was assessed with CLAIM (checklist for artificial intelligence in medical imaging). Results51 studies were identified that fulfilled the inclusion criteria. 61% used open-source EMGlab data set to develop models to classify nEMG signal in healthy, amyotrophic lateral sclerosis (ALS) and myopathy (25 subjects). Only two articles developed models to classify signals recorded at rest. Most articles reported high performance accuracies, but many were subject to bias and overtraining. ConclusionsCurrent AI-models of nEMG signals are not sufficient for clinical implementation. Suggestions for future research include emphasizing the need for an optimal training and validation approach using large datasets of clinical nEMG data from a diverse patient population. SignificanceThe outcomes of this study and the suggestions made aim to contribute to developing AI-models that can effectively handle signal quality variability and are suitable for daily clinical practice in interpreting nEMG signals.

Artificial intelligence for novel fuel design

Co-optimization of fuels and engines can help decarbonize the transportation sector. Traditional methods for fuel-engine co-optimization rely on experimental or theoretical approaches for determining relationships between fuel molecular properties and engine performance. However, these methods can be expensive and time consuming to develop and are prone to errors when there are complex non-linear relationships between molecular structure and combustion properties. Data-driven approaches that rely on artificial intelligence (AI) and machine learning (ML) algorithms are gaining interest for their versatility, adaptability, and ease of use. This paper provides an overview of AI and ML methods and their applications in fuel design. ML algorithms are now routinely developed to predict quantitative structure property relationships (QSPR) for combustion applications. However, molecular representations relying on descriptors are limited to iterative screening of molecules or mixtures for fuel design; they do not enable inverse fuel design wherein a fuel's structure or composition is predicted based on desired target properties. Inverse fuel design is enabled by using AI/ML methods to train QSPR models using molecular graphs and neural networks for a fully differentiable mapping of molecular structure to fuel properties. Gradient-based search methods can then be employed on the latent space representation to generate novel fuel components or mixtures. While AI for fuel design is an evolving domain, the need for uncertainty quantification in training data and model parameters cannot be overlooked. Methods for addressing aleatoric and epistemic are discussed herein together with outlooks for their applications in fuel design.

Overview of artificial intelligence in point-of-care ultrasound. New horizons for respiratory system diagnoses.

Throughout the past decades ultrasonography did not prove to be a procedure of choice if regarded as part of the routine bedside examination. The reason was the assumption defining the lungs and the bone structures as impenetrable by ultrasound. Only during the recent several years has the approach to the use of such tool in clinical daily routines changed dramatically to offer so-called point-of-care ultrasonography (POCUS). Both vertical and horizontal artefacts became valuable sources of information about the patient's clinical condition, assisting therefore the medical practitioner in differential diagnosis and monitoring of the patient. What is important is that the information is delivered in real time, and the procedure itself is non-invasive. The next stage marking the progress made in this area of diagnostic imaging is the development of arti-ficial intelligence (AI) based on machine learning algorithms. This article is intended to present the available, innovative solutions of the ultrasound systems, including Smart B-line technology, to ensure automatic identification process, as well as interpretation of B-lines in the given lung area of the examined patient. The article sums up the state of the art in ultrasound artefacts and AI applied in POCUS.

MIVES Technique for Sustainability Assessment in Architecture and Civil Engineering Industries

A subfield of computer science called artificial intelligence studies, develops, and uses intelligent computers. Massive computational resources are needed for complicated structure system modeling and optimization using traditional approaches; nevertheless, Artificial intelligence-driven solutions can often provide practical substitutes for efficiently addressing challenges in civil engineering. This paper presents a study that examines the merits and requirements of multidisciplinary collaboration between architecture and civil engineering students, focusing on the distinctions and overlaps in their aesthetic assessments and visual preferences. To illustrate the difficulties facing the Spanish Combined Value Model for Sustainability Assessment (MIVES), this research study synthesizes the different approaches for sustainability assessment for the construction industry and compares their advantages and disadvantages. The value function concept and expert seminars serve as the foundation for the Multi-Criteria Decision Making technique known as MIVES. Research indicates that the primary areas of interest for artificial intelligence studies in civil engineering are design optimization and structural maintenance and management. Finally, the primary research trends are also highlighted. An overview of artificial intelligence's advancements in civil engineering is given in this paper.

The use of artificial intelligence during the educational process for students with attention deficit and hyperactivity disorder

This research paper delves into the transformative potential of artificial intelligence (AI) in education, with a specific focus on students with Attention Deficit Hyperactivity Disorder (ADHD). It examines both the advantages and challenges associated with the integration of artificial intelligence into educational practices. Beginning with an overview of artificial intelligence's emerging role in education, the paper explores the characteristics and symptoms of ADHD, tracing its historical evolution. This sets the stage for an in-depth discussion of the signs and symptoms of ADHD and the need for tailored educational approaches. The study further deepens into the concept of artificial intelligence, elucidating its capacity to replicate human mental capabilities and its growing significance in education, particularly within the realm of special education. The legislative landscape within the European Union (EU) and Greece regarding AI integration in education is also outlined. After the literature overview, artificial intelligence's pivotal role in education is highlighted, encompassing personalized learning experiences, virtual tutors, and its positive impact on student engagement, critical thinking, and problem-solving skills. The challenges posed by artificial intelligence integration are meticulously explored, including ethical concerns, biases in automated assessments, and the importance of fostering stakeholder awareness and education. Additionally, the operational prerequisites for AI implementation are addressed, encompassing both technical infrastructure and human resources. Special emphasis is placed on the necessity for adequate training and support for students and teachers, alongside the challenge of ensuring access to essential technical resources. In conclusion, the paper underscores the substantial potential of artificial intelligence to reshape education while acknowledging the importance of addressing ethical concerns and ensuring equitable access to artificial intelligence technologies. This research underscores the pivotal role of artificial intelligence in adapting to the evolving demands of 21st-century education, with a strong emphasis on personalized learning and learner-centered approaches.

Artificial Intelligence in Battle against Antimicrobial Resistance: Opportunities and Challenges

Due to the overuse and abuse of antibiotics, antimicrobial resistance (AMR) poses a serious risk to socioeconomic development and public health. A paradigm shift is required to address this dilemma, and artificial intelligence (AI) appears as a possible remedy. AI, including machine learning (ML) and deep learning (DL), has demonstrated significant promise in several medical research fields, especially in the fight against AMR. Applications of AI in AMR use cutting-edge computational methods to analyze gene expression and whole-genome sequencing data, assisting in discovering infectious disease etiology and disease subtypes. These AI-driven systems have several advantages over more conventional ones, including less need for human involvement, more accuracy, and lower costs. However, they also encounter difficulties, such as inconsistent performance across datasets, with data volume critically influencing model efficacy. The accessibility and expense of high-throughput sequencing data, particularly next-generation sequencing data, also pose challenges to the wider application of AI models for AMR investigation. Despite these difficulties, AI has significant promise in the fight against AMR, and its advantages and disadvantages must be carefully considered in order to build successful tactics for dealing with this urgent worldwide problem. We assess research papers on AMR analysis using AI on various datasets and contrast the effectiveness of various AI models. We thoroughly reviewed the DL models used up to this point in the field of AMR, and we additionally discussed the challenges that come with deploying these approaches. This paper offers a thorough overview of AI's applications in AMR analysis, highlighting both its benefits and drawbacks.