Exploring the Diverse Applications of Deep Learning Across Multiple Domains

Artificial Intelligence: The Future of Maxillofacial Prognosis and Diagnosis?

Artificial Intelligence in the American Healthcare Industry: Looking Forward to 2030

Introduction: the Generative Pre-trained Transformer (GPT) is a deep learning language model architecture developed by OpenAI. Aim: to describe the knowledge networks (both at the theoretical and country levels) of the Generative Pre-trained Transformer (GPT) as an emerging technology. Results: 222 Documents were identified, of which 69 were articles, 50 were conference papers, 36 were editorials, 29 were notes, 19 were letters, 14 were reviews, 3 were conference reviews, and 2 were short surveys. In terms of the number of documents per year, 2 were found in 2019, 10 in 2020, 22 in 2021, 44 in 2022, and 144 in 2023. The year-on-year growth rate was over 100 % in all years. The subject area with the highest number of documents was Computer Science with 90 documents. The most productive countries in relation to GPT were the United States with 60 documents, followed by China with 19, the United Kingdom with 18, India with 15, and Australia with 12. Co-occurrence illustrated the centrality of Artificial Intelligence, Natural Language Processing, Deep Learning, and the term Human around ChatGPT and GPT. Conclusions: this bibliometric study aimed to describe the knowledge networks of the Generative Pre-trained Transformer (GPT) as an emerging technology. Although only 222 documents were found, this study revealed a high level of international scientific collaboration in the field. The results suggest that GPT is a highly relevant technology with a wide range of potential applications in natural language processing, artificial intelligence, and deep learning. Moreover, the study was able to qualitatively characterize the main thematic areas surrounding GPT, including its applications in chatbots, text generation, machine translation, sentiment analysis, and more.

Artificial Intelligence (AI) is playing an increasingly vital role in the field of financial statement analysis. AI methodologies, such as Deep Learning, Machine Learning, and Natural Language Processing, are being employed to enhance the analysis of financial data. This study explores the applications of AI in financial statement analysis, including Predictive Analytics, Anomaly Detection, Trend Analysis, Financial Report Automation, Risk Evaluation and Control, Comparative Analysis and Benchmarking, Portfolio Optimization, Risk Assessment and Management, Fraud Detection and Forensic Analysis, and Regulatory Reporting and Compliance. The chapter includes case studies, industrial insights, and practical applications of AI in financial statement analysis, highlighting the tangible benefits and opportunities for businesses in the finance sector. Additionally, the chapter addresses the challenges and potential future applications of AI in this domain.

Pharmacovigilance, the science of monitoring drug safety, plays a crucial role in identifying and mitigating adverse drug reactions (ADRs). However, underreporting in pharmacovigilance systems—estimated to have a median rate of 94 %—poses a significant threat to patient safety by hindering the detection of safety signals. The need to address these gaps is paramount, especially with the rising complexity of healthcare data. The advent of artificial intelligence (AI) and big data technologies offers promising solutions to overcome the limitations of traditional pharmacovigilance methods.The application of AI and machine learning (ML) technologies, including natural language processing (NLP) and deep learning, has the potential to revolutionize drug safety monitoring by automating the detection of ADRs from diverse data sources, such as electronic health records (EHRs), spontaneous reporting systems, and social media. These tools can process unstructured data and uncover patterns not easily identifiable through conventional approaches. Additionally, AI can enable real-time pharmacovigilance, which is especially critical in an era of increasing polypharmacy and diverse patient populations. AI-driven models are being utilized to detect drug-drug interactions (DDIs), predict ADRs, and enhance the overall efficiency of pharmacovigilance processes.Despite these advancements, several challenges remain. The performance of AI models is heavily dependent on the quality and quantity of data available. Inadequate or poorly curated datasets can lead to inaccurate ADR detection, particularly in resource-limited settings. Moreover, the heterogeneity of data sources necessitates robust AI models capable of integrating various types of data while ensuring accurate and reliable outputs. There is also a pressing need to address the transparency and explainability of AI models, as the opaque decision-making processes of current algorithms often impede their acceptance among pharmacovigilance professionals.Future directions must focus on improving the quality and standardization of datasets, advancing NLP techniques for better interpretation of clinical narratives, and developing explainable AI models. Regulatory frameworks should evolve to support AI deployment in pharmacovigilance, ensuring the establishment of best practices for AI implementation and the creation of large-scale, publicly available training datasets.Additionally, AI models should go beyond correlation-based approaches by integrating causal inference techniques, which will allow for a more accurate understanding of the relationship between drugs and ADRs. Human oversight will still be required to validate AI findings, but ongoing efforts to improve the robustness of AI systems will reduce dependency on manual interventions and scale the use of AI in pharmacovigilance.The integration of AI and big data in pharmacovigilance has the potential to transform drug safety monitoring, addressing many of the challenges posed by increasing data complexity and the need for real-time analysis. As these technologies continue to evolve, they promise to make pharmacovigilance more efficient, accurate, and comprehensive, thereby improving patient safety and rational drug use.

Deep Learning, a buzz in the artificial intelligence field, is the subset of machine learning. It teaches computers to learn from examples in order to perform a task that is intuitive to humans. It is also known as a deep neural network or deep neural learning. In deep learning, neural networks have a significant role. These are a set of algorithms that we implement to identify relevant relationships in datasets, and they follow the process that imitates the human brain. Neural networks depict the behavior of the human brain and enable computer algorithms to identify trends. It also solves complex problems in the domain of machine learning, AI, and data science.Deep learning deploys artificial neural networks to recognize the hidden patterns of data in the dataset provided. These algorithms are trained over an adequate amount of time and applied to a data set.

Deep Learning (DL) is an area of artificial intelligence (AI), wherein machine learning (ML) using convolutional neural networks to detect patterns in data and forecast its worth. It excels in tough settings like climate change and can handle sophisticated jobs such as image and speech recognition, natural language processing, and others. Deep learning models have found applications in a variety of fields due to their ability to reflect complicated patterns in data. Deep learning applications have the capacity to analyze and automatically classify large volumes of Internet traffic. Deep learning-based solutions that automate the detection of attacks and address complicated cybersecurity issues are gaining popularity. This study extensively describes the promising uses of deep learning, which is based on many layers of artificial neural networks, in a wide range of security challenges. Before critically and comparatively reviewing state-of-the-art solutions from the literature, we will highlight the main properties of typical deep learning architectures used in cybersecurity applications. Deep learning highlights developing concepts and provides an overview of required resources, such as a general framework and appropriate datasets. The limits of the evaluated works are highlighted, as well as a vision of the present challenges in the field, providing useful insights and best practices for researchers and developers working on related problems.

Artificial intelligence-based suicide prevention and prediction: A systematic review (2019–2023)

Artificial Intelligence (AI) in learning and education is not new. However, the rise of the Covid-19 pandemic led to a focus on its role in Learning Management Systems (LMS). This study also examined the role of AI in the LMS with features including Natural Language Processing and Reasoning. The researchers used a self-proposed model and gathered data from the mass communication students in two public sector universities in Pakistan. Results revealed that Artificial Intelligence is integral to Learning Management Systems (LMS) in the selected institutions. Further, the effect of Natural Language Processing (NLP) on Reasoning indicated these two factors as interlinked, improving the quality of LMS. Additionally, Natural Language Processing significantly mediated the effect of AI on the LMS, showing that language processing facilitates LMS usage among students. Finally, the results also indicated the significant mediation effect of Reasoning on the relationship s between AI and the LMS. Overall, the results also remained supported towards the incorporation of Artificial Intelligence (AI) along with Natural Language Processing (NLP) and Reasoning in Learning Management Systems (LMS). The researchers conclude that AI in Learning Management Systems is widely applied in Pakistani institutions. The young students also acknowledge the benefits of AI-enabled LMS that have enhanced their learning experiences and provided them with logical solutions and answers to their complex queries. Further, the study limitations are discussed and highlighted accordingly.

Artificial intelligence (AI), with its technologies such as machine perception, robotics, natural language processing, expert systems, and machine learning (ML) with its subset deep learning, have transformed patient care and administration in all fields of modern medicine. For many clinicians, however, the nature, scope, and resulting possibilities of ML and deep learning might not yet be fully clear. This narrative review provides an overview of the application of ML and deep learning in musculoskeletal medicine. It first introduces the concept of AI and machine learning and its associated fields. Different machine concepts such as supervised, unsupervised and reinforcement learning will then be presented with current applications and clinical perspective. Finally deep learning applications will be discussed. With significant improvements over the last decade, ML and its subset deep learning today offer potent tools for numerous applications to implement in clinical practice. While initial setup costs are high, these investments can reduce workload and cost globally. At the same time, many challenges remain, such as standardisation in data labelling and often insufficient validity of the obtained results. In addition, legal aspects still will have to be clarified. Until good analyses and predictions are obtained by an ML tool, patience in training and suitable data sets are required. Awareness of the strengths of ML and the limitations that lie within it will help put this technique to good use.

With the explosive growth of computer science and big data, quantitative finance is undergoing a huge shift from traditional econometrics to data-driven Artificial Intelligence (AI). This study aims to review the applications of Machine Learning (ML), Deep Learning (DL), and Reinforcement Learning (RL) in vital areas such as asset pricing, algorithmic trading and risk management. Research shows that Deep Neural Networks (DNNs) capture non-linear market patterns, Natural Language Processing (NLP) analyses unstructured data while providing superior sentiment signals compared to generic alternatives. Deep Reinforcement Learning (DRL) can effectively automate execution in dynamic market environments, which optimizes trading decisions. Similarly, Temporal Fusion Transformers (TFT) have emerged as a dominant architecture for multi-horizon time series forecasting, offering superior interpretability and accuracy over standard recurrent networks. These ML models greatly outperform traditional methods like CAPM and ARIMA in prediction accuracy and handling complex data. However, there are still challenges about the lack of interpretability ("black box") and overfitting. In the future, quantitative finance lies in combining "Explainable AI" (XAI).

This study presents a Systematic Literature Review on Artificial Intelligence (AI) and Natural Language Processing (NLP) applications for customer support automation and digital service optimization. The review follows the PRISMA framework to ensure methodological rigor and transparency, focusing on literature published between 2020 and 2025 from the Scopus database. The findings reveal that AI-driven technologies, including Machine Learning, Deep Learning, and Large Language Models, have significantly improved efficiency, response time, and customer satisfaction in customer support and digital service. Common NLP applications include sentiment analysis, ticket classification, and automated response generation. Among these, hybrid and transformer-based models demonstrate superior accuracy and contextual understanding compared to traditional algorithms. However, several challenges persist, including data quality limitations, privacy and security concerns, algorithmic bias, and linguistic ambiguities such as sarcasm and negation. Moreover, issues related to trust and ethical adoption continue to influence user acceptance of AI systems. This review provides a comprehensive synthesis of current methodologies, trends, and research gaps, offering insights for future studies to develop explainable, secure, and human-centered AI systems that enhance the sustainability and transparency of digital customer support services.

A systematic review of artificial intelligence techniques for collaborative learning over the past two decades

In the era of digitalization, electronic gadgets such as Google Translate, Siri, and Alexa have at least one characteristic: They are all the products of natural language processing (NLP). “Natural Language” refers to a human language used for daily communication, such as English, Hindi, Bengali, etc. Natural languages, as opposed to artificial languages such as computer languages and mathematical nomenclature, have evolved as they have been transmitted from generation to generation and are challenging to explain with clear limits in the first instance. In natural language processing, artificial intelligence (Singh et al., 2021), linguistics, information processing, and cognitive science are all related fields (NLP). NLP aims to use intelligent computer techniques to process human language. However, NLP technologies such as voice recognition, language comprehension, and machine translation exist. With such limited obvious exclusions, machine learning algorithms in NLP sometimes lacked sufficient capacity to consume massive amounts of training data. In addition, the algorithms, techniques, and infrastructural facilities lack enough strength. Humans design features in traditional machine learning, and feature engineering is a limitation that requires significant human expertise. Simultaneously, the accompanying superficial algorithms lack depiction capability and, as a result, the ability to generate layers of duplicatable concepts that would naturally separate intricate aspects in forming visible linguistic data. Deep learning overcomes the challenges mentioned earlier by using deep, layered modelling architectures, often using neural networks and the corresponding full-stack learning methods. Deep learning has recently enhanced natural language processing by using artificial neural networks based on biological brain systems and Backpropagation. Deep learning approaches that use several processing layers to develop hierarchy data representations have produced cutting-edge results in various areas. This chapter introduces natural language processing (NLP) as an AI component. The history of NLP is next. Distributed language representations are the core of NLP's profound learning revolution. After the survey, the boundaries of deep learning for NLP are investigated. The paper proposes five NLP scientific fields.

Deep Learning and Beyond: AI’s New Horizons” is an expansive monograph that delves into the dynamic and rapidly evolving field of deep learning, a key area of artificial intelligence. This comprehensive work provides an insightful exploration of the revolutionary developments, foundational concepts, and future directions of deep learning, positioning itself as a crucial resource for understanding this transformative technology. The monograph commences with an introductory chapter that lays the groundwork by elucidating the background of machine learning and chronicling the ascent of deep learning. This foundational perspective is vital for comprehending the underlying principles and methodologies that have propelled deep learning to the forefront of technological innovation. Subsequent chapters delve into the “Historical Evolution” of AI, tracing the origins of neural networks from their early inception to the Renaissance period of deep learning. This historical analysis is pivotal in understanding the field’s trajectory and significant milestones. The “Foundations of Deep Learning” chapter presents an in-depth analysis of neural networks, covering their basic architecture, activation functions, layers, and the critical process of backpropagation and gradient descent. This is followed by examining “Advanced Neural Network Architectures,” where the focus shifts to state-of-the-art models such as convolutional neural networks, recurrent neural networks, and groundbreaking transformers and attention mechanisms. In addressing the “Training Deep Models,” the monograph explores regularization techniques, optimization strategies, and the increasingly significant role of transfer learning and pre-trained models. The application-oriented chapter, “Applications of Deep Learning,” showcases real-world implementations in domains like image recognition, natural language viii processing, and autonomous systems, illustrating deep learning technologies’ vast potential and versatility. The monograph also tackles the “Challenges in Deep Learning,” including overfitting, interpretability, and the ethical considerations surrounding AI deployment. It then ventures into the “Future of Deep Learning,” contemplating emerging trends such as integrating quantum computing, neural architecture search, and the convergence of neuroscience and deep learning. “Case Studies” provide practical insights and empirical evidence of the impact of deep learning. At the same time, the concluding chapter synthesizes the current state of deep learning, offers predictions for the future, and provides reflective thoughts on this transformative field. “Deep Learning and Beyond: AI’s New Horizons” educates and informs readers about the vast potential and future possibilities of deep learning and artificial intelligence. Keywords: Deep Learning, Artificial Intelligence, Neural Networks, Machine Learning, Convolutional Neural Networks, Recurrent Neural Networks, Transformers, Quantum Computing, Ethical AI, Neural Architecture Search, Natural Language Processing, Autonomous Systems, AI in Healthcare, Future of AI, AI Applications, AI Challenges.