Current State Of Artificial Intelligence Research Articles

Navigating the light and shadow of scientific publishing faced with machine learning and generative AI.

The public release of ChatGPT in November 2022 sparked a boom and public interest in generative artificial intelligence (AI) that has led to journals and journal families hastily releasing generative AI policies, ranging from asking authors for acknowledgement or declaration to the outright banning of use. Here, we briefly discuss the basics of machine learning, generative AI, and how it will affect scientific publishing. We focus especially on potential risks and benefits to the scientific community as a whole and journals specifically. While the concerns of editors, for example about manufactured studies, are valid, some recently implemented or suggested policies will not be sustainable in the long run. The quality of generated text and code is quickly becoming so high that it will not only be impossible to detect the use of generative AI but would also mean taking a powerful tool away from researchers that can make their life easier every day. We discuss the history and current state of AI and highlight its relevance for medical publishing and pain research. We provide guidance on how to act now to increase good scientific practice in the world of ChatGPT and call for a task force focusing on improving publishing pain research with use of generative AI.

Clinical Decision Support and Natural Language Processing in Medicine: Systematic Literature Review.

Ensuring access to accurate and verified information is essential for effective patient treatment and diagnosis. Although health workers rely on the internet for clinical data, there is a need for a more streamlined approach. This systematic review aims to assess the current state of artificial intelligence (AI) and natural language processing (NLP) techniques in health care to identify their potential use in electronic health records and automated information searches. A search was conducted in the PubMed, Embase, ScienceDirect, Scopus, and Web of Science online databases for articles published between January 2000 and April 2023. The only inclusion criteria were (1) original research articles and studies on the application of AI-based medical clinical decision support using NLP techniques and (2) publications in English. A Critical Appraisal Skills Programme tool was used to assess the quality of the studies. The search yielded 707 articles, from which 26 studies were included (24 original articles and 2 systematic reviews). Of the evaluated articles, 21 (81%) explained the use of NLP as a source of data collection, 18 (69%) used electronic health records as a data source, and a further 8 (31%) were based on clinical data. Only 5 (19%) of the articles showed the use of combined strategies for NLP to obtain clinical data. In total, 16 (62%) articles presented stand-alone data review algorithms. Other studies (n=9, 35%) showed that the clinical decision support system alternative was also a way of displaying the information obtained for immediate clinical use. The use of NLP engines can effectively improve clinical decision systems' accuracy, while biphasic tools combining AI algorithms and human criteria may optimize clinical diagnosis and treatment flows. PROSPERO CRD42022373386; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=373386.

Applications of artificial intelligence for imaging-driven diagnosis and treatment of bone and soft tissue tumors

Bone and soft tissue tumors occur in the musculoskeletal system, and malignant bone tumors of bone and soft tissue account for 0.2% of all human malignant tumors, and if not diagnosed and treated in a timely manner, patients may be at risk of a poor prognosis. Image interpretation plays an increasingly important role in the diagnosis of bone and soft tissue tumors. Artificial intelligence (AI) can be applied in clinical treatment to integrate large amounts of multidimensional data, derive models, predict outcomes, and improve treatment decisions. Among these methods, deep learning is a widely employed technique in AI that predominantly utilizes convolutional neural networks (CNN). The network is implemented through repeated training of datasets and iterative parameter adjustments. Deep learning-based AI models have successfully been applied to various aspects of bone and soft tissue tumors, encompassing but not limiting in image segmentation, tumor detection, classification, grading and staging, chemotherapy effect evaluation, recurrence and prognosis prediction. This paper provides a comprehensive review of the principles and current state of AI in the medical image diagnosis and treatment of bone and soft tissue tumors. Additionally, it explores the present challenges and future prospects in this field.

Enhancing ethical codes with artificial intelligence governance – a growing necessity for the adoption of generative AI in counselling

ABSTRACT The advent of generative Artificial Intelligence (AI) systems, such as large language model chatbots, is likely to have a significant impact in psychotherapy and counselling in the future. In this paper we consider the current state of AI in psychotherapy and counselling and the likely evolution of this field. We examine the ethical codes of practice for counselling in four countries in different parts of the world, namely the UK, the USA, Australia and Malaysia, and identify aspects of these codes that will need enhancement to reflect good AI governance. Using the Model Artificial Intelligence Governance Framework as an example, we have identified how the key elements of the AI framework relate to the core elements of the ethical codes, as a pointer to how such ethical codes will need to be enhanced if generative AI systems are to be adopted by the counselling profession.

AI in Perinatal Medicine and Human Reproduction: Artificial IntelligenceAI in Perinatal Medicine and Human Reproduction: Is it “The End of the Beginning” or “The Beginning of the End”?

The paper explores the evolving role of Artificial Intelligence (AI) in perinatal medicine and hu-man reproduction, highlighting its potential to transform clinical practices. AI technologies are being utilized to improve diagnostic accuracy, personalize treatment, and enhance patient care, particularly in areas like perinatal ultrasound, fetal heart rate monitoring, and fetal neurology. The Kurjak Antenatal Neurodevelopmental Test (KANET) exemplifies how AI can aid early detection of neurodevelopmental disorders. However, the integration of AI presents challenges such as data quality concerns, algorithmic bias, ethical concerns, and the need for robust regulatory frameworks. The authors argue that while AI offers significant opportunities, its implementation must be carefully managed to avoid over-reliance on technology and ensure equitable healthcare access. The paper concludes that the current state of AI in this field marks not an endpoint but a critical phase of growth and development, necessitating a balanced approach that combines innovation with ethical and practical considerations.

Bibliometric Analysis of AI Technology Developments in Education: Trends, Collaborations, and Future Impact

This study employs bibliometric analysis to explore the landscape of artificial intelligence (AI) applications in education. Through an extensive review of thematic clusters, research trends, author collaborations, and potential future research topics, the study maps the evolution and current state of AI in educational settings. Key findings indicate a robust focus on integrating AI within higher education and a significant emphasis on educational technology across various specialized domains. Temporal analysis reveals a progressive shift toward exploring advanced topics such as generative AI, reflecting the field's rapid adaptation to new technological advancements. Additionally, the author collaboration network illustrates a vibrant and interconnected academic community, while also identifying less explored areas like language education and AI in art education as promising fields for future research. The study highlights the dynamic nature of AI research in education, showcasing both the depth of current integrations and the potential for innovative applications that could further transform educational practices.

The Race of Artificial Intelligence and Challenges

In today’s era Artificial Intelligence (AI) has become an increasingly prominent field of research and development over the past few decades. In recent years, the development of AI has been accelerating, with more and more resources being devoted to the creation of increasingly sophisticated and capable AI systems. With more and more countries being in an AI arm race, companies competing and racing to be the one with the next most disruptive and transformative AI system. Artificial Intelligence (AI) is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to limited itself to procedures that are biologically observable. While no exact definition of Artificial Intelligence (AI) exists, AI is broadly characterized as the study of computations that allow for reason, perception and action. Today, the amount of data that is generated, by both humans and machines, far outpaces humans’ ability to absorb, interpret, and make complex decisions based on that data. Artificial intelligence forms the basis for all computer learning and is the future of all complex decision making. This paper focuses features of artificial Intelligence, introduction, history, applications, growth and achievements. This paper also analyzes the current state of artificial intelligence progresses, and how the current AI race with recent trends.

Implications of using AI in Translation Studies: Trends, Challenges, and Future Direction

This review paper provides an overview of the use of artificial intelligence (AI) in translation studies. The paper discusses the various AI techniques that have been used in translation, including statistical machine translation, rule-based machine translation, neural machine translation, and hybrid machine translation. The paper also explores the advantages and limitations of each model, as well as their applications in translation studies. Additionally, the paper reviews the various techniques for evaluating the effectiveness of AI models in translation and their advantages and limitations. The challenges and limitations of AI in translation, such as the handling of idioms, metaphors, and cultural nuances, are also discussed, along with research directions for improving AI-based translation. The review also discusses the representation of AI in literature and the arts, delves into the academic opportunities, and investigates its impact on human lives. Furthermore, AI's ethical and social implications in translation, such as job displacement, data privacy, and bias and fairness, are examined. Finally, the paper summarizes the main findings, implications, and recommendations for future research directions in AI-based translation studies. Overall, this review paper provides some insights into the current state of AI in translation and its potential for improving the field.

Optimization of Big Data Processing and Analysis Processes in the Field of Data Analytics Through the Integration of Data Engineering and Artificial Intelligence

In this article, we provide an overview of the current state of artificial intelligence (AI) and big data analytics, which are the most needed needs of our age. This report is designed to provide an overview of the latest research and achievements in this rapidly growing field and to highlight, check and complement the many ways in which AI can be combined to improve data. The most important developments in the field of data analysis demonstrate the development of machine learning models, the importance of explaining AI, and the use of AI in computing. Methodology: for this research we have analyse the latest trend in the related field of AI and data engineering. We have short listed nine main papers for this report. Meta-analysis requires the use of descriptive and/or statistical methods to collect data from multiple studies on a particular topic. These ideas help build knowledge from many studies in both qualitative and quantitative ways. Results: The article provides an overview of the complexities of integrating large amounts of data with intelligence, exploring topics such as data integrity, social and ethical issues. Additionally, it introduces new applications across multiple industries and reveals opportunities to transform environmental monitoring, supply chain operations, cybersecurity, and health monitoring. Overall, this article provides the reader with an in-depth understanding of the latest advances and challenges in data mining and intelligence-based analysis. Conclusion: It contributes to the ongoing debate on how AI can be used to gain meaningful insights from massive data sets, and provides a forward-looking perspective on the potential impacts on multiple sectors of the economy and society in general. Another new concept that expresses the openness of AI algorithms is descriptive intelligence (XAI). Understanding and interpreting intelligence decisions is important because it plays an important role in information processing, especially in areas that require compliance with ethical and legal standards.

An Introductory Guide to Artificial Intelligence in Interventional Radiology: Part 1 Foundational Knowledge.

Artificial intelligence (AI) is rapidly evolving and has transformative potential for interventional radiology (IR) clinical practice. However, formal training in AI may be limited for many clinicians and therefore presents a challenge for initial implementation and trust in AI. An understanding of the foundational concepts in AI may help familiarize the interventional radiologist with the field of AI, thus facilitating understanding and participation in the development and deployment of AI. A pragmatic classification system of AI based on the complexity of the model may guide clinicians in the assessment of AI. Finally, the current state of AI in IR and the patterns of implementation are explored (pre-procedural, intra-procedural, and post-procedural).

(127) Is Artificial Intelligence Ready to Help Urologists?

Abstract Introduction ChatGPT is a language model developed by OpenAI. It's a variant of the GPT (Generative Pretrained Transformer) architecture, a transformer-based neural network used for natural language processing tasks. The model has been trained on a massive corpus of text data and can generate human-like responses to topics and questions. ChatGPT can be used for chatbots, conversation agents, and language translation. The integration of AI into urology has the potential to improve patient outcomes, increase efficiency, and reduce costs, ultimately leading to a better healthcare experience for patients. Objective The objective of this study is to assess the current state of artificial intelligence (AI) when it comes to urologic problems and to determine whether it is sufficiently developed and prepared to provide useful assistance to urologists in their clinical practice. This study aims to assess the accuracy, reliability, and potential benefits of one AI language model (ChatGPT) in efficiently answering common patient questions, providing patients with accurate educational information, minimizing patient harm, and guiding patients towards appropriate management. By exhibiting ChatGPT's capabilities and limitations in the context of urology, the study aims to demonstrate how AI can be helpful in everyday urological practices. Methods ChatGPT (chat.openai.com) was presented with typical clinical scenarios and patient questions. Results AI wrote the introduction answering “What is ChatGPT?” and “How can AI help urologists?”. For the conclusion, AI was prompted with “Is artificial intelligence ready to help urologists?”. Figures summarize the answers to common patient questions given by ChatGPT. Conclusions The use of AI in the field of urology is still in its early stages and more research is needed to determine its full potential. However, there have already been some promising developments and successful applications. For example, AI algorithms have been used to accurately diagnose prostate cancer, predict patient outcomes, and improve surgical planning. While AI has the potential to revolutionize the field of urology, it is important to note that AI is not a substitute for human expertise and judgment. AI algorithms can provide valuable insights and assistance, but the ultimate decision making and treatment planning should still be done by experienced urologists. Additionally, it is important to ensure that AI systems are developed and used in a way that respects patient privacy and data security, and that the outcomes of AI-assisted decision making are transparent and explainable. In summary, AI has the potential to greatly benefit the field of urology. Still, it is important to approach its use with caution and continue evaluating its impact as more research is conducted. Disclosure No.

The future of artificial intelligence in thoracic surgery for non-small cell lung cancer treatment a narrative review.

To present a comprehensive review of the current state of artificial intelligence (AI) applications in lung cancer management, spanning the preoperative, intraoperative, and postoperative phases. A review of the literature was conducted using PubMed, EMBASE and Cochrane, including relevant studies between 2002 and 2023 to identify the latest research on artificial intelligence and lung cancer. While AI holds promise in managing lung cancer, challenges exist. In the preoperative phase, AI can improve diagnostics and predict biomarkers, particularly in cases with limited biopsy materials. During surgery, AI provides real-time guidance. Postoperatively, AI assists in pathology assessment and predictive modeling. Challenges include interpretability issues, training limitations affecting model use and AI's ineffectiveness beyond classification. Overfitting and global generalization, along with high computational costs and ethical frameworks, pose hurdles. Addressing these challenges requires a careful approach, considering ethical, technical, and regulatory factors. Rigorous analysis, external validation, and a robust regulatory framework are crucial for responsible AI implementation in lung surgery, reflecting the evolving synergy between human expertise and technology.

A Literature Survey on AI Health Care System

The integration of Artificial Intelligence (AI) in healthcare systems has witnessed remarkable progress in recent years, revolutionizing the landscape of patient care, diagnostics, and medical research. This paper provides a comprehensive survey of the diverse applications, challenges, and benefits of AI in healthcare. We explore the role of machine learning algorithms and natural language processing in enhancing diagnosis and treatment planning. The utilization of AI for predictive analytics, personalized medicine, and patient management is discussed, showcasing its potential to improve healthcare outcomes and reduce costs. Additionally, we address ethical considerations, data privacy concerns, and regulatory frameworks that accompany the implementation of AI in healthcare. Through an extensive literature review, this paper aims to offer insights into the current state of AI in healthcare, highlighting key trends and future directions for research and development in this rapidly evolving field

The Role of Artificial Intelligence in Revolutionizing Healthcare

This research paper provides a comprehensive overview of the transformative role of Artificial Intelligence (AI) in revolutionizing the healthcare sector. As AI technologies continue to advance, their integration into various facets of healthcare promises groundbreaking improvements in diagnostics, personalized medicine, predictive analytics, and Electronic Health Records (EHR) management. This paper explores the applications of AI in these critical areas, highlighting its potential to enhance patient care, optimize treatment strategies, and streamline healthcare operations. Additionally, the paper addresses the challenges and ethical considerations associated with AI adoption in healthcare. By offering insights into the current state of AI in healthcare and its future implications, this research aims to contribute to a deeper understanding of the profound impact AI is making on the healthcare landscape

The AI Race: Why Current Neural Network-based Architectures are a Poor Basis for Artificial General Intelligence

Artificial General Intelligence is the idea that someday an hypothetical agent will arise from artificial intelligence (AI) progresses, and will surpass by far the brightest and most gifted human minds. This idea has been around since the early development of AI. Since then, scenarios on how such AI may behave towards humans have been the subject of many fictional and research works. This paper analyzes the current state of artificial intelligence progresses, and how the current AI race with the ever faster release of impressive new AI methods (that can deceive humans, outperform them at tasks we thought impossible to tackle by AI a mere decade ago, and that disrupt the job market) have raised concerns that Artificial General Intelligence (AGI) might be coming faster that we thought. In particular, we focus on 3 specific families of modern AIs to develop the idea that deep neural networks, which are the current backbone of nearly all artificial intelligence methods, are poor candidates for any AGI to arise due to their many limitations, and therefore that any threat coming from the recent AI race does not lie in AGI but in the limitations, uses, and lack of regulations of our current models and algorithms. This article appears in the AI & Society track.

Navigating the AI frontiers in cardiovascular research: a bibliometric exploration and topic modeling.

Artificial intelligence (AI) has emerged as a promising field in cardiovascular disease (CVD) research, offering innovative approaches to enhance diagnosis, treatment, and patient outcomes. In this study, we conducted bibliometric analysis combined with topic modeling to provide a comprehensive overview of the AI research landscape in CVD. Our analysis included 23,846 studies from Web of Science and PubMed, capturing the latest advancements and trends in this rapidly evolving field. By employing LDA (Latent Dirichlet Allocation) we identified key research themes, trends, and collaborations within the AI-CVD domain. The findings revealed the exponential growth of AI-related research in CVD, underscoring its immense potential to revolutionize cardiovascular healthcare. The annual scientific publication of machine learning papers in CVD increases continuously and significantly since 2016, with an overall annual growth rate of 22.8%. Almost half (46.2%) of the growth happened in the last 5 years. USA, China, India, UK and Korea were the top five productive countries in number of publications. UK, Germany and Australia were the most collaborative countries with a multiple country publication (MCP) value of 42.8%, 40.3% and 40.0% respectively. We observed the emergence of twenty-two distinct research topics, including "stroke and robotic rehabilitation therapy," "robotic-assisted cardiac surgery," and "cardiac image analysis," which persisted as major topics throughout the years. Other topics, such as "retinal image analysis and CVD" and "biomarker and wearable signal analyses," have recently emerged as dominant areas of research in cardiovascular medicine. Convolutional neural network appears to be the most mentioned algorithm followed by LSTM (Long Short-Term Memory) and KNN (K-Nearest Neighbours). This indicates that the future direction of AI cardiovascular research is predominantly directing toward neural networks and image analysis. As AI continues to shape the landscape of CVD research, our study serves as a comprehensive guide for researchers, practitioners, and policymakers, providing valuable insights into the current state of AI in CVD research. This study offers a deep understanding of research trends and paves the way for future directions to maximiz the potential of AI to effectively combat cardiovascular diseases.

Current Status and Role of Artificial Intelligence in Anorectal Diseases and Pelvic Floor Disorders.

Anorectal diseases and pelvic floor disorders are prevalent among the general population. Patients may present with overlapping symptoms, delaying diagnosis, and lowering quality of life. Treating physicians encounter numerous challenges attributed to the complex nature of pelvic anatomy, limitations of diagnostic techniques, and lack of available resources. This article is an overview of the current state of artificial intelligence (AI) in tackling the difficulties of managing benign anorectal disorders and pelvic floor disorders. A systematic literature review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We searched the PubMed database to identify all potentially relevant studies published from January 2000 to August 2023. Search queries were built using the following terms: AI, machine learning, deep learning, benign anorectal disease, pelvic floor disorder, fecal incontinence, obstructive defecation, anal fistula, rectal prolapse, and anorectal manometry. Malignant anorectal articles and abstracts were excluded. Data from selected articles were analyzed. 139 articles were found, 15 of which met our inclusion and exclusion criteria. The most common AI module was convolutional neural network. researchers were able to develop AI modules to optimize imaging studies for pelvis, fistula, and abscess anatomy, facilitated anorectal manometry interpretation, and improved high-definition anoscope use. None of the modules were validated in an external cohort. There is potential for AI to enhance the management of pelvic floor and benign anorectal diseases. Ongoing research necessitates the use of multidisciplinary approaches and collaboration between physicians and AI programmers to tackle pressing challenges.

Adapting digital marketing to artificial intelligence

Methods. In recent years, marketing has reached a point of evolution where adapting to digital trends is essential. In many situations, the platforms used for online promotion include algorithms to identify the best combinations. Specific research methods were used: analysis and synthesis - when identifying new trends in digital marketing, in particular, the use of keywords for sustainable business; classifications – for grouping the most common keyword research tools in digital marketing. Results. The article provides research that highlights the current position of digital marketing. It has been demonstrated that a new sub-industry is actively growing in the modern marketing industry – automated marketing, which is characterized, among other things, by the inclusion of voice search in marketing or keyword identification methods. and the inclusion of voice search in marketing or keyword identification methods. The most common keyword research tools in digital marketing are identified, the effectiveness of their application in the practical activities of a marketer is shown. The results of the authors in this article show a change from digital marketing to intelligent marketing using artificial intelligence. Novelty. Modern trends in the development of digital marketing are identified, and the specifics of its new form – Intellectual marketing – are highlighted. Features of adapting the modern marketing model to artificial intelligence are demonstrated. Practical value. The paper reviews the current state of artificial intelligence in marketing processes and provides a consistent model using an intelligent marketing solution that can improve website visibility through keywords.

Impact of Artificial Intelligence on Colorectal Polyp Detection and Characterization

AbstractColorectal cancer (CRC) is the third most common cancer in the world. Colonoscopy has contributed significantly to reduction of incidence and mortality of CRC. Integration of artificial intelligence (AI) into colonoscopy practice has addressed the various shortcomings of screening colonoscopies. AI-assisted colonoscopy will help in real-time recognition of type of polyp with probable histology. This will not only save time but will also help to mitigate human errors. Computer-aided detection and computer-aided characterization are two applications of AI, which are being studied extensively with a goal of improvement of polyp and adenoma detection rates. Several studies are being conducted across the globe, which either involve simple decision-making algorithms or complex patterns through neural networks, which imitate the human brain. Most data are collected retrospectively and the research is limited to single-center studies, which might have bias. Therefore, the future research on AI in colonoscopy should aim to develop more sophisticated convolutional neural network and deep learning models that will help to standardize the practice and ensure the same degree of accuracy with all the colonoscopies, irrespective of experience of performing endoscopists. In this review, we will take a closer look at the current state of AI and its integration into the field of colonoscopy.

AI in Inventory Management: Applications, Challenges, and Opportunities

Abstract: This paper delves into the multifaceted role of Artificial Intelligence (AI) in inventory management, encompassing its applications, challenges, and future opportunities. AI's integration into inventory management systems has revolutionized supply chain operations, enhancing efficiency, accuracy, and decision-making processes. The paper explores various AI applications, including demand forecasting, stock optimization, and automated reordering. However, it also acknowledges the challenges in AI implementation, such as data quality, interpretability, and model transparency. The research highlights the synergy between AI and emerging technologies like the Internet of Things (IoT), pointing towards new innovative solutions that were unimaginable in the past. In conclusion, the paper presents a balanced view of AI's transformative impact on inventory management, emphasizing both its current benefits and the hurdles that need to be overcome for its successful integration. Furthermore, this comprehensive analysis not only provides insights into the current state of AI in inventory management but also sheds light on its promising future, marked by efficiency and technological advancement