Evaluating the Impact of Artificial Intelligence (AI) on Clinical Documentation Efficiency and Accuracy Across Clinical Settings: A Scoping Review.

Maximising the Opportunities of Artificial Intelligence for People Living With Cancer

Artificial Intelligence (AI) and Machine Learning (ML) have emerged as transformative technologies in healthcare, particularly in medical diagnostics. This review explores the role of AI and ML in revolutionizing disease detection, treatment planning, and personalized healthcare. AI technologies, including deep learning (DL), natural language processing (NLP), and advanced data analytics, enable the rapid and accurate processing of complex medical datasets such as imaging, genetic information, and electronic health records (EHRs). By identifying subtle patterns and abnormalities, AI systems enhance diagnostic accuracy in areas such as oncology, cardiology, neurology, dermatology, and infectious diseases. The integration of AI in cancer diagnosis, for instance, has improved early detection through advanced imaging analysis using convolutional neural networks (CNNs) and deep learning models. Similarly, in cardiovascular diseases, AI enhances electrocardiogram (ECG) analysis and risk stratification, enabling early intervention. Neurological disorders such as Alzheimer's and Parkinson's benefit from AI tools that analyze neuroimaging, speech, and motor patterns for early diagnosis and progression monitoring. In diabetes management, ML models predict disease onset, personalize treatment plans, and improve blood glucose monitoring. Dermatological and ophthalmological applications leverage AI-driven image recognition tools to diagnose skin lesions, diabetic retinopathy, and glaucoma with high precision. Despite its potential, the adoption of AI in healthcare faces challenges, including data privacy concerns, algorithmic bias, and regulatory hurdles. Addressing these issues through robust validation, transparency, and ethical frameworks is essential for wider implementation. This review highlights the future prospects of AI in healthcare, such as precision medicine, wearable technology, and AI-driven telemedicine, emphasizing its potential to enhance efficiency, reduce costs, and improve patient outcomes. As AI technologies continue to evolve, they promise a more accurate, accessible, and personalized approach to medical diagnosis and treatment.

This study investigates the evolving landscape of financial forecasting, with a specific focus on the integration of Artificial Intelligence (AI). In an era where financial markets are increasingly volatile and data-driven, traditional forecasting models fall short in delivering real-time, accurate insights. The research explores how AI technologies such as machine learning, deep learning, and natural language processing are transforming financial forecasting by enhancing accuracy, speed, and adaptability. Utilizing a mixed-methods approach, the study combines primary data collected via surveys with secondary data from extensive literature. Key findings highlight high awareness of AI among finance professionals and students, with machine learning and predictive analytics being the most recognized tools. The survey reveals concerns about data privacy, model transparency, and ethical implications, yet shows strong support for hybrid forecasting models that combine AI with human expertise. The study concludes that while AI offers significant advantages in financial forecasting, its adoption must be guided by ethical practices, regulatory frameworks, and transparency to ensure trust and responsible use. This project contributes to understanding the opportunities and challenges associated with AI in forecasting and offers actionable insights for professionals, educators, and policymakers in finance. Keywords Artificial Intelligence, Financial Forecasting, Machine Learning, Predictive Analytics, Ethics in AI, Transparency, Hybrid Models, Data Privacy, Deep Learning, Financial Modelling.This study investigates the evolving landscape of financial forecasting, with a specific focus on the integration of Artificial Intelligence (AI). In an era where financial markets are increasingly volatile and data-driven, traditional forecasting models fall short in delivering real-time, accurate insights. The research explores how AI technologies such as machine learning, deep learning, and natural language processing are transforming financial forecasting by enhancing accuracy, speed, and adaptability. Utilizing a mixed-methods approach, the study combines primary data collected via surveys with secondary data from extensive literature. Key findings highlight high awareness of AI among finance professionals and students, with machine learning and predictive analytics being the most recognized tools. The survey reveals concerns about data privacy, model transparency, and ethical implications, yet shows strong support for hybrid forecasting models that combine AI with human expertise. The study concludes that while AI offers significant advantages in financial forecasting, its adoption must be guided by ethical practices, regulatory frameworks, and transparency to ensure trust and responsible use. This project contributes to understanding the opportunities and challenges associated with AI in forecasting and offers actionable insights for professionals, educators, and policymakers in finance. Keywords Artificial Intelligence, Financial Forecasting, Machine Learning, Predictive Analytics, Ethics in AI, Transparency, Hybrid Models, Data Privacy, Deep Learning, Financial Modelling.

The following article delves into the profound impact of Artificial Intelligence (AI) and Machine Learning (ML) on enhancing business efficiencies and streamlining processes. With technological advancements continuing to accelerate, this subject has gained increasing significance. In an era where economies heavily rely on data, it has become imperative for companies to integrate AI and ML models into their business operations to maintain competitiveness, reduce expenses, and boost revenues. The research focuses on implementing various AI paradigms, including Natural Language Processing (NLP) and chatbots, across enterprises of diverse scales. It specifically explores their influence on enhancing sales, marketing, and customer service. These AI tools signify businesses' evolving strategies to navigate intricate markets. This research uses a quantitative analytical approach to evaluate the financial implications of integrating artificial intelligence (AI) within companies. To achieve this, we are utilizing both linear regression and random forest models for our analysis. This methodology enables a comprehensive analysis of the correlation between adopting advanced AI technologies and subsequent financial performance. The substantial body of research on AI and ML technologies underscores their escalating significance and the imperative for businesses to adapt to these changes. This study contributes to this extensive knowledge by furnishing empirical evidence of AI's impact on augmenting financial outcomes. The findings affirm that AI's strategic development and integration are crucial for enhancing company revenues and overall economic well-being. For this research, we selected the global Amazon investment report and the global AI/ML development growth from 2011 to 2023. This article underscores the practical implications and substantial benefits of leveraging AI and ML in real-world business scenarios. The conclusions suggest that businesses continuously refine and incorporate AI technologies into their models to achieve optimal operational efficiency. By outlines the advantages of specific AI models and provides a framework for companies aiming to utilize technology to gain a competitive edge in the market. Consequently, the practical significance of this research lies in its capacity to inform and enhance business strategies through the effective implementation of AI and ML.

Technology based on artificial intelligence (AI) is a revolutionary force that is changing economies, civilizations, and industries all over the world. AI, which has its roots in computer science and cognitive psychology, is a wide range of tools and methods designed to make robots capable of doing activities that have historically required human intellect. This abstract examines the many facets of artificial intelligence (AI) technology, including its fundamentals, uses, difficulties, and ramifications. Artificial Intelligence (AI) technology comprises several subfields such as robotics, computer vision, natural language processing, machine learning, and expert systems. Particularly, machine learning techniques have propelled incredible progress by allowing computers to learn from data and make judgments or predictions without the need for explicit programming. Natural language processing allows machines to comprehend, interpret, and produce human language, hence facilitating human-computer interaction. Machines can now see, analyze, and interpret visual data from the real world thanks to computer vision technology. Applications of AI technology may be found in a wide range of industries, including manufacturing, healthcare, finance, transportation, agriculture, education, and entertainment. AI-powered solutions help in drug discovery, medical imaging analysis, diagnosis, and customized therapy in the healthcare industry. AI algorithms are used in finance to power automated trading, fraud detection, risk assessment, and customer support. AI makes it possible for transportation to include predictive maintenance, traffic management, and driverless cars. Artificial Intelligence enhances supply chain management, quality assurance, and production processes in manufacturing. AI technology has the potential to revolutionize many industries, but it also comes with dangers and problems. These include privacy concerns, security hazards, ethical dilemmas, issues with prejudice and fairness, and effects on society and employment. Responsible AI methods, legal frameworks, multidisciplinary cooperation, and ethical standards are all necessary to meet these issues. Future prospects for AI technology development include the ability to solve challenging issues, spur creativity, increase productivity, and improve quality of life. But to fully utilize AI, one must take a comprehensive strategy that strikes a balance between the advancement of technology and ethical issues, human values, and social well-being. In summary, artificial intelligence (AI) technology is at the vanguard of innovation, presenting never-before-seen possibilities to transform whole sectors, spur economic expansion, and tackle global issues. AI has the ability to usher in a future of greater human-machine collaboration, innovation, and wealth through the promotion of collaboration, transparency, and ethical stewardship. the Ranking of the Artificial Intelligence using the TOPSIS Method . Interpretable Models is got the first rank whereas is the Ethical AI is having the Lowest rank. Keywords: Explainable AI (XAI), Interpretable Models, Ethical AI ,Responsible AI, Robustness and Adversarial Defense, Continual Learning, Federated Learning, Human-Centric AI, AI Governance and Policy

Background: The integration of artificial intelligence (AI) in healthcare has significantly transformed clinical practices, offering substantial improvements in diagnosis, treatment planning, and patient outcome predictions. AI technologies, including artificial neural networks, fuzzy expert systems, and hybrid intelligent systems, are advancing the field of augmented medicine by combining AI with traditional healthcare practices. Methods: This study reviews the diverse applications of AI in healthcare, focusing on its impact on clinical procedures, disease detection, and healthcare management. The analysis covers the use of AI-driven tools such as surgical navigation systems, augmented reality for pain management, and machine learning algorithms for early disease detection and clinical documentation. Results: AI technologies like AccuVein and augmented reality headsets have enhanced clinical procedures such as intravenous placements and surgical interventions. Advances in machine learning, particularly neural networks and deep learning, have improved the detection of complex patterns in imaging data, facilitating early diagnosis of diseases like cancer and pneumonia. Natural language processing (NLP) has enhanced the analysis and classification of clinical documentation, while robotic process automation (RPA) has optimized administrative tasks. AI's role in managing infectious diseases, particularly during the COVID-19 pandemic, has been critical, demonstrating its potential in screening, diagnosis, and treatment surveillance. AI applications in oncology and laboratory medicine have also shown increased accuracy and efficiency in disease diagnosis and patient care. Conclusion: AI is revolutionizing healthcare by enhancing diagnostic accuracy, treatment efficacy, and patient care quality. Despite its transformative potential, challenges such as legal accountability and data bias must be addressed for successful integration into healthcare systems. Continued research and innovation in AI applications are essential to maximizing its benefits while minimizing associated risks.

Background: The integration of artificial intelligence (AI) into healthcare represents a transformative shift in medical procedures, offering substantial benefits across various domains. With advancements in AI technologies such as machine learning (ML), deep learning (DL), and natural language processing (NLP), healthcare systems are witnessing improvements in early detection, patient treatment, and overall administration. This article traces the evolution of AI, from foundational contributions by Alan Turing during World War II to contemporary applications like ChatGPT, and examines the impact of AI in enhancing diagnostic accuracy and treatment outcomes. Methods: This comprehensive review analyzes the existing literature on AI applications in healthcare, focusing on various AI methodologies and their integration into clinical settings. It evaluates the effectiveness of AI in processing large datasets, improving diagnostic precision, and facilitating data-driven decision-making. The study also explores the ethical, legal, and technical challenges associated with AI deployment in medical environments. Results: AI technologies have demonstrated significant improvements in healthcare, particularly in early disease detection, personalized treatment plans, and resource management. The use of AI in analyzing vast medical datasets has enhanced diagnostic accuracy, reduced costs, and optimized patient care. However, challenges related to ethical considerations, patient privacy, and system reliability remain critical barriers to full-scale AI adoption. Conclusion: Despite the challenges, AI is positioned as an indispensable tool in modern medicine, capable of enhancing preventive care, personalizing treatments, and improving healthcare delivery. This review proposes a framework for evaluating the benefits, challenges, and strategies of AI integration in healthcare. Further research is essential to maximize AI's potential while addressing ethical and practical concerns, ensuring safe and effective implementation in clinical settings.

The early detection of diseases plays a crucial role in improving patient outcomes, reducing healthcare costs, and enabling timely interventions. In recent years, the integration of Artificial Intelligence (AI) and Predictive Analytics (PA) has emerged as a transformative approach in healthcare, offering significant advancements in detecting diseases at their earliest stages. This paper provides a comprehensive review of the application of AI-driven predictive analytics in early disease detection, focusing on various AI techniques such as machine learning (ML), deep learning (DL), natural language processing (NLP), and neural networks. These techniques have shown exceptional promise in identifying patterns and correlations within medical data—including electronic health records (EHRs), medical imaging, genetic data, and wearable devices—that can signal the onset of diseases before they become clinically evident. The paper discusses the effectiveness of AI-based predictive models in detecting a wide range of diseases, including cancer, cardiovascular diseases, diabetes, neurological disorders, neonatal conditions, and infectious diseases. Special attention is given to AI applications in neonatal healthcare, where early detection of conditions such as neonatal sepsis, respiratory distress syndrome, and congenital anomalies can significantly improve survival rates and long-term health outcomes. By leveraging large datasets and advanced algorithms, AI systems can provide accurate predictions, risk assessments, and personalized treatment plans, leading to improved early diagnosis and targeted interventions. However, the integration of AI in disease detection also presents challenges such as data privacy concerns, model interpretability, ethical issues, and the need for robust regulatory frameworks. Furthermore, the paper highlights key advancements in AI technologies that have contributed to the success of predictive analytics in healthcare, along with real-world applications, case studies, and examples of AI models that have been implemented in clinical settings. The limitations and potential solutions to these challenges are also examined, with an emphasis on the importance of high-quality, representative datasets and continuous collaboration between AI researchers, clinicians, and regulatory bodies. This review aims to provide a thorough understanding of the current landscape of AI-powered predictive analytics for early disease detection and to highlight future directions in the field. As AI technologies continue to evolve, their role in enhancing early disease detection, particularly in neonatal care, improving patient outcomes, and enabling preventive healthcare will become increasingly significant, ultimately leading to a more efficient, effective, and equitable healthcare system.

The main purpose of the paper is to evaluate and compare different business valuation models that incorporate artificial intelligence (AI) technologies. The paper seeks to understand the capabilities, advantages, disadvantages, and limitations of these AI-based models in valuing businesses accurately. Additionally, the paper aims to provide insights into how AI can be utilized effectively in the field of business valuation to enhance accuracy and efficiency. We used qualitative research methods which involve reviewing and analyzing existing literature, case studies, and expert opinions on business valuation models and artificial intelligence. The main contribution of the paper is the integration of artificial intelligence (AI) techniques into traditional business valuation models. The authors propose using AI algorithms such as machine learning and natural language processing to improve the accuracy and efficiency of valuing businesses. By leveraging AI technology, the paper aims to provide more reliable and data-driven valuations, ultimately enhancing decision-making processes for investors, managers, and other stakeholders. The initial segment of the analysis outlines conventional business valuation approaches, such as discounted cash flow (DCF), comparable company analysis (CCA), and asset-based valuation. These methods utilize historical financial data, market comparisons, and asset valuations to estimate a company’s value. Although they are effective, these traditional models have limitations in terms of capturing intricate market dynamics and accurately forecasting future performance. The following section of the analysis delves into specific AI-driven valuation strategies, such as sentiment analysis, predictive analytics, and algorithmic trading techniques. It also explores how AI technologies, like machine learning algorithms, natural language processing (NLP), and deep learning, are revolutionizing business valuation practices. AI enables the analysis of vast datasets, including unstructured data from platforms like social media, news articles, and industry reports, to extract valuable insights. Machine learning models can detect patterns, correlations, and predictive indicators that traditional models may miss, leading to more accurate and agile valuations. The analysis then addresses the benefits, obstacles, and considerations associated with integrating AI into business valuation. This includes data quality and accessibility, model interpretability and transparency, regulatory compliance, and ethical concerns related to AI bias and fairness. In addition, a comparative evaluation of AI-based models is presented. In conclusion, integrating AI into business valuation models presents significant potential to enhance the accuracy, efficiency, and dependability of valuation assessments. Using AI-driven methodologies, investors and analysts can gain deeper insights into the intrinsic value of businesses, enabling them to make more informed investment decisions in dynamic and competitive markets. However, it is crucial to pay careful attention to data integrity, model transparency, and ethical implications to ensure the responsible and effective use of AI in business valuation. Finally, future directions and recommendations are provided.

The rapid development of artificial intelligence (AI) and machine learning has introduced significant changes in various fields, including the Arabic language education sector. This study focuses on exploring the challenges and applications of AI in Arabic language learning, particularly in the context of machine learning technologies. The primary problem addressed in this research is the difficulty in developing effective machine learning models that accurately understand and process the Arabic language, given its complexity, diverse dialects, and intricate grammar rules. The main objective of this research is to analyze the current applications of AI in Arabic language learning, assess the challenges associated with these technologies, and identify potential improvements. The study employs a qualitative approach, reviewing existing literature, case studies, and practical applications of AI in Arabic language education. The results indicate that AI technologies such as natural language processing, automated translation, and speech recognition have significantly contributed to enhancing Arabic language learning tools. However, the study also reveals challenges related to the complexity of Arabic grammar, regional dialects, and the lack of sufficient digital resources and standardized linguistic data. This research contributes to the field by providing insights into the integration of AI in Arabic language education, highlighting both the advancements and the obstacles that need to be overcome. The study suggests that with continued investment in AI technologies and collaboration between linguistic experts and tech developers, the future of Arabic language learning can be greatly enhanced, offering more efficient and personalized learning experiences for students

This systematic literature review explores the integration of artificial intelligence (AI) technologies such as intelligent tutoring systems (ITS), machine learning, natural language processing, and adaptive learning platforms in university education. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we evaluated peer-reviewed articles, case studies, and government reports from 2015 onwards. The results demonstrate that AI technologies not only enhance personalized learning and educational outcomes but also streamline administrative functions, transforming educational practices. However, challenges such as ethical issues, data privacy, and algorithmic bias remain. The review underscores the importance of theoretical frameworks like constructivist learning theory and the TPACK framework for effective AI integration. Recommendations are provided for educators, administrators, and policymakers to ensure responsible AI use in university settings. This paper offers insights into the current capabilities and future prospects of AI in higher education, promoting ongoing research and strategic implementation.

The rapid evolution of Artificial Intelligence (AI) since its inception in the mid-20th century has significantly influenced the field of Natural Language Processing (NLP), transforming it from a rule-based system to a dynamic and adaptive model capable of understanding the complexities of human language. This paper aims to offer a comprehensive review of the various applications and methodologies of AI in NLP, serving as a detailed guide for future research and practical applications. In the early sections, the paper elucidates the indispensable role of AI in NLP, highlighting its transition from symbolic reasoning to a focus on machine learning and deep learning, and its extensive applications in sectors such as healthcare, transportation, and finance. It emphasizes the symbiotic relationship between AI and NLP, facilitated by platforms like AllenNLP, which aid in the development of advanced language understanding models. Further, the paper explores specific AI techniques employed in NLP, including machine learning, Naive Bayes, and Support Vector Machines, and identifies pressing challenges and avenues for future research. It delves into the applications of AI in NLP, showcasing its transformative potential in tasks such as machine translation, facilitated by deep learning methods, and the development of chatbots and virtual assistants that have revolutionized human-technology interaction. The paper also highlights other fields impacted by AI techniques, including text summarization, sentiment analysis, and named entity recognition, emphasizing the efficiency and accuracy brought about by the integration of AI in these areas. In conclusion, the paper summarizes the remarkable advancements and persistent challenges in NLP, such as language ambiguity and contextual understanding, and underscores the need for diverse and representative labeled data for training. Looking forward, it identifies promising research avenues including Explainable AI, Few-shot and Zero-shot Learning, and the integration of NLP with other data modalities, aiming for a holistic understanding of multimodal data. The paper calls for enhanced robustness and security in NLP systems, especially in sensitive applications like content moderation and fake news detection, to foster trust and reliability in AI technologies. It advocates for continual learning in NLP models to adapt over time without losing previously acquired knowledge, paving the way for a future where AI and NLP work synergistically to understand and generate human language more effectively and efficiently.

Focus issue: Artificial intelligence in medical physics.

The integration of artificial intelligence (AI) technologies into the banking sector has revolutionized various financial processes, including bill payment. This abstract explores the application of AI in the context of bill payment within the banking industry. Bill payment is a fundamental aspect of banking, requiring significant administrative efforts and customer interaction. AI technologies, such as machine learning, natural language processing, and robotic process automation, offer potential solutions to enhance the efficiency, accuracy, and customer experience associated with bill payment.This abstract examines the key AI-driven innovations in bill payment, including intelligent chatbots, automated invoice processing, and predictive analytics. Intelligent chatbots leverage natural language processing to interact with customers, providing real-time assistance in bill inquiries, payment scheduling, and troubleshooting. Automated invoice processing utilizes machine learning algorithms to extract relevant information from invoices and automate payment processing, minimizing errors and reducing manual intervention. Predictive analytics employs AI algorithms to analyze historical data and predict payment behavior, enabling banks to identify potential delinquencies and proactively assist customers.Furthermore, it highlights the benefits and challenges associated with implementing AI in bill payment within the banking industry. The advantages include improved operational efficiency, reduced costs, enhanced customer satisfaction, and personalized payment solutions. However, challenges such as data privacy, security concerns, ethical considerations, and regulatory compliance need to be addressed to ensure responsible AI adoption.Overall, the integration of AI technologies in banking bill payment holds tremendous potential for streamlining processes, optimizing customer experience, and driving innovation within the financial industry. By harnessing the power of AI, banks can deliver more efficient and personalized bill payment services, ultimately benefiting both customers and financial institutions.

The modern stage of technological advancement is characterized by the dynamic development of artificial intelligence (AI) technologies and their integration into education. Of the several dozen artificial intelligence technologies used in various spheres of human activity, the most widely used in education are: a) machine learning, b) natural language processing, c) data science and d) intelligent tutoring system. On their basis, artificial intelligence tools are created, which have significant language teaching potential and in many ways change the traditional roles of the teacher and learners in the educational process. However, it should be noted that the integration of artificial intelligence technologies into education in general and foreign language teaching in particular is currently at the initial stage. Educators and learning designers conduct pilot studies investigating the abilities of specific artificial intelligence tools in the formation of foreign language aspects or the development of learners' foreign language communication skills. At the same time, the limited number of empirical research studies does not allow us to talk about the systematicity and comprehensiveness of foreign language teaching based on artificial intelligence technologies. One of the key differences between artificial intelligence technologies and modern information and communication technologies is their AI’s ability to provide a much wider range of feedback. It is owing to this advantage of artificial intelligence that innovative methods of teaching a foreign language will be based, creating new additional conditions for students to master a foreign language and raising the learning process to a new level in terms of the quality of solving learning tasks. However, the consideration of the types of feedback provided by AI tools has not been the subject of separate research, which determined the importance of this study. The aim of the study is to identify the types of feedback provided to learners by artificial intelligence technologies for the subsequent development of teaching methods (teaching technologies and/or typologies of tasks and assignments) based on them. The definition of the types of feedback provided to users by artificial intelligence tools was based on a review and analysis of research in the field of pedagogy and foreign language teaching methods. The sample of sources included research articles and reviews published in academic journals indexed in Scopus and Web of Science (Q1 and Q2), as well as Russian academic journals, included in the list of the Higher Attestation Commission of the Russian Federation (Categories 1 and 2) (pedagogical sciences). The following aspects of teaching methods were the subject of study in the review and analysis of academic papers: a) the artificial intelligence tool used for receiving feedback; b) the target audience of learners; c) the purpose of interaction with artificial intelligence; d) the form of activities; e) the type of feedback used. As a result, the following six types of feedback provided by artificial intelligence tools were identified in this study: a) educational and social; b) information and reference; c) methodological; d) analytical; e) evaluative; f) conditionally creative feedback.