DIAGNOSIS OF ACUTE DISEASES USING AI

Deep fakes, synthetic media generated through artificial intelligence (AI) techniques, have become a growing concern due to their potential for misinformation, manipulation, and security threats. As the sophistication of deep fake technology continues to evolve, traditional methods of detection are becoming less effective, thus prompting the need for advanced AI-driven solutions. This article explores the various AI techniques employed in deep fake detection, such as deep learning, neural networks, and computer vision algorithms. A review of the latest advancements in AI technologies, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs), is presented to highlight their roles in identifying deep fakes. Additionally, we explore challenges related to data availability, model robustness, and ethical implications. The paper concludes by proposing a hybrid detection framework that integrates multiple AI techniques to enhance the accuracy and efficiency of deep fake identification.

Thyroid dysfunction is one of the most common endocrine diseases. About 4.7% of the United States population has undiagnosed thyroid disease (Garmendia Madariaga, Santos Palacios, Guillén-Grima, & Galofré, 2014). Thyroid nodules could be present in up to 67% of the population (Ezzat, Sarti, Cain, & Braunstein, 1994). Increased incidence of thyroid nodules has been attributed to increased use of imaging modalities and improvement in imaging technology (Singh, Singh, & Khanna, 2012). Technological improvements in ultrasound including elastography, 3D ultrasound (Liang et al., 2019) 274and quantitative ultrasound (Goundan et al., 2019) have been used to improve the diagnostic accuracy. Computer-aided diagnosis software that can automatically detect different features in the thyroid nodules and generate a report has been cleared by the FDA (Lu, Shi, Zhao, Song, & Li, 2019). Similarly, artificial intelligence (AI) algorithms have been used in the diagnosis and management of thyroid diseases. One of the first papers using AI in the diagnosis of thyroid disease was by Sharpe et al. in 1993. They used a multilayer perceptron trained by back-propagation and a learning vector quantization network to investigate the robustness of these models on noisy diagnostic data. In recent years, most of the AI research in thyroidology has been focused on the diagnosis and management of thyroid nodules. Initial approaches used texture analysis to classify thyroid ultrasound images. Later, AI techniques such as machine learning (ML) and an advanced form of ML, deep learning (DL) algorithms were used. In this chapter, we discuss the use of AI in thyroid imaging, cytopathological diagnosis of thyroid nodules and molecular markers. The use of wearable devices and generative adversarial networks in thyroidology is also discussed in this chapter.

Background: The field of sports science has been fundamentally transformed by the integration of artificial intelligence (AI) technologies. AI has enabled advances in areas such as performance optimization, training personalization, injury risk assessment and prevention, talent recognition, rehabilitation, athlete monitoring, and wellness optimization. Objective: This review aims to explore the diverse impact of AI in sports science, highlighting advances in AI techniques, the challenges and limitations of integrating AI tools, and the emerging role of AI chatbots in shaping the future of sports research and applications. Methods: The review examines the existing literature on the application of machine learning, deep learning and other AI techniques in various aspects of sports science research and practice. It provides an overview of how these technologies have been used to improve personalized training programs, video analysis, injury risk prediction, talent identification, and rehabilitation. Results: The article describes how AI-powered tools and techniques have revolutionized sports science, enabling personalized, data-driven and efficient approaches to performance optimization. Sophisticated machine learning algorithms such as artificial neural networks, decision trees and support vector machines have been used to develop predictive models for injury risk assessment and prevention, leading to improvements in athletes' wellbeing and long-term performance. AI-driven talent identification and selection processes have also shown promise in recognizing exceptional athletes with greater accuracy. In addition, the integration of AI into athlete monitoring and rehabilitation has led to greater personalization, better decision making and faster return to play. Conclusion: The integration of AI and machine learning techniques into sports science has the potential to transform the field and lead to improved athlete performance, reduced risk of injury, improved well-being, and more personalized and effective interventions. The emergence of AI chatbots expands the applications of these technologies in sports science and offers new opportunities to streamline research processes, provide personalized advice to athletes and support sports medicine and rehabilitation

The Science of Deep Learning emerged from courses taught by the author that have provided thousands of students with training and experience for their academic studies, and prepared them for careers in deep learning, machine learning, and artificial intelligence in top companies in industry and academia. The book begins by covering the foundations of deep learning, followed by key deep learning architectures. Subsequent parts on generative models and reinforcement learning may be used as part of a deep learning course or as part of a course on each topic. The book includes state-of-the-art topics such as Transformers, graph neural networks, variational autoencoders, and deep reinforcement learning, with a broad range of applications. The appendices provide equations for computing gradients in backpropagation and optimization, and best practices in scientific writing and reviewing. The text presents an up-to-date guide to the field built upon clear visualizations using a unified notation and equations, lowering the barrier to entry for the reader. The accompanying website provides complementary code and hundreds of exercises with solutions.

In recent years, Deep Learning (DL), such as the algorithms of Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and Generative Adversarial Networks (GAN), has been widely studied and applied in various fields including agriculture. Researchers in the fields of agriculture often use software frameworks without sufficiently examining the ideas and mechanisms of a technique. This article provides a concise summary of major DL algorithms, including concepts, limitations, implementation, training processes, and example codes, to help researchers in agriculture to gain a holistic picture of major DL techniques quickly. Research on DL applications in agriculture is summarized and analyzed, and future opportunities are discussed in this paper, which is expected to help researchers in agriculture to better understand DL algorithms and learn major DL techniques quickly, and further to facilitate data analysis, enhance related research in agriculture, and thus promote DL applications effectively. Keywords: deep learning, smart agriculture, neural network, convolutional neural networks, recurrent neural networks, generative adversarial networks, artificial intelligence, image processing, pattern recognition DOI: 10.25165/j.ijabe.20181104.4475 Citation: Zhu N Y, Liu X, Liu Z Q, Hu K, Wang Y K, Tan J L, et al. Deep learning for smart agriculture: Concepts, tools, applications, and opportunities. Int J Agric & Biol Eng, 2018; 11(4): 32-44.

Artificial intelligence has been advancing in fields including anesthesiology. This scoping review of the intersection of artificial intelligence and anesthesia research identified and summarized six themes of applications of artificial intelligence in anesthesiology: (1) depth of anesthesia monitoring, (2) control of anesthesia, (3) event and risk prediction, (4) ultrasound guidance, (5) pain management, and (6) operating room logistics. Based on papers identified in the review, several topics within artificial intelligence were described and summarized: (1) machine learning (including supervised, unsupervised, and reinforcement learning), (2) techniques in artificial intelligence (e.g., classical machine learning, neural networks and deep learning, Bayesian methods), and (3) major applied fields in artificial intelligence.The implications of artificial intelligence for the practicing anesthesiologist are discussed as are its limitations and the role of clinicians in further developing artificial intelligence for use in clinical care. Artificial intelligence has the potential to impact the practice of anesthesiology in aspects ranging from perioperative support to critical care delivery to outpatient pain management.

Artificial intelligence (AI) has profoundly altered the financial services industry by offering new paradigms for data analysis, decision-making, and operational efficiency. This chapter provides an extensive review of the core concepts, recent advancements, and emerging applications of AI in the financial sector. Based on a critical review of 162 high-impact scientific articles, the chapter identifies and evaluates key theme areas, including financial forecasting, fraud detection, explainable AI (XAI), and the nexus between AI and FinTech. The findings demonstrate the importance of state-of-the-art AI techniques such as deep learning, reinforcement learning, and hybrid models for enhancing risk assessment, raising forecast precision, and ensuring regulatory compliance. The chapter also discusses how AI could transform several financial processes, including portfolio management, credit assessment, and anti-money laundering efforts. The creation of interpretable AI frameworks, the incorporation of multimodal data sources, and the examination of AI's societal ramifications regarding sustainability and financial inclusion are just a few of the new opportunities and research gaps that are highlighted in this chapter. By bridging the gap between academic research and industry practice, this chapter provides useful insights for both researchers and practitioners. To harness AI's transformative potential while upholding moral standards and public trust, it encourages ethical, interdisciplinary collaboration. The chapter concludes with a strategic roadmap for the innovative and long-term use of AI technology in contemporary financial ecosystems.

Artificial Intelligence: The Future of Maxillofacial Prognosis and Diagnosis?

The future of drug research is intrinsically connected to the continuous advancement and prudent integration of Artificial intelligence (AI) and mathematics. By addressing challenges and leveraging possibilities, the pharmaceutical industry may fully harness the potential of AI to develop innovative and effective treatments for diverse diseases. In our opinion, finding new drugs takes long time and funds, and in the past, it was mostly done manually. AI has changed this sector completely, making drug manufacturing faster, cheaper, and more specific. This review presents the pertinent literature on drug discovery utilizing mathematical modeling and AI tools and methodologies implemented at every stage of drug development to expedite the research process and mitigate risk and costs in clinical trials, it also presents that how mathematical modeling and AI algorithms can be used together at several stages of drug development. There is a lot of focus on how mathematical frameworks like Linear Algebra, optimization, statistical modeling, graph theory and differential equations may operate along with the techniques of AI like machine learning (ML), deep learning (DL), reinforcement learning (RL), natural language processing (NLP) and transfer learning (TL) and the problems that are now being faced, the tools and datasets that are accessible, and what the future holds for this field, which is changing quickly.

The Central Serous Retinopathy (CSR) or Central Serous Chorioretinopathy (CSC) is a significant disease that causes blindness and vision loss among in millions of people in the world. It transpires as a result of accumulation of watery fluids behind the retina. Therefore, detection of CSR at early stages allows taking preventive measures to avert any impairment to the human eye. Traditionally, several manual methods for detecting CSR have been developed in the past; however, they have shown to be imprecise and unreliable. Consequently, due to Artificial Intelligence (AI) services in the medical field, including automated CSR detection, it is now possible to detect and cure this disease. This review assessed a variety of innovative technologies and researches that contribute to the automatic detection of CSR. In this review, various CSR disease detection techniques, broadly classified into two categories: a) CSR detection based on classical imaging technologies, and b) CSR detection based on Machine/Deep Learning methods, have been reviewed after an elaborated evaluation of 29 different relevant articles. Additionally, it also goes over the advantages, drawbacks and limitations of a variety of traditional imaging techniques, such as Optical Coherence Tomography Angiography (OCTA), Fundus Imaging and more recent approaches that utilize Artificial Intelligence techniques. Finally, it is concluded that the most recent Deep Learning (DL) classifiers are found to deliver accurate, fast, and reliable CSR detection. However, there is a need of more researches on publically available datasets to improve computation complexity for the reliable detection and diagnosis of CSR disease.

Exploring new horizons: Empowering computer-assisted drug design with few-shot learning

The transition to sustainable and intelligent energy systems has intensified the development of smart microgrids, which offer decentralized, resilient, and efficient power solutions. This review critically examines the integration of Artificial Intelligence (AI) and Deep Reinforcement Learning (DRL) into smart microgrid platforms, focusing on their role in optimizing sustainable energy management. Traditional energy management systems often struggle to adapt to the dynamic nature of modern energy demands, renewable energy intermittency, and grid complexity. AI-driven solutions, particularly DRL, provide adaptive, autonomous, and data-driven mechanisms for real-time decision-making and predictive control within microgrids. DRL, by learning optimal policies through interaction with the environment, is capable of handling multi-objective problems, including demand-response optimization, energy storage control, load forecasting, and distributed generation scheduling. This paper synthesizes recent advancements and applications of DRL algorithms such as Deep Q-Networks (DQN), Deep Deterministic Policy Gradient (DDPG), and Proximal Policy Optimization (PPO) in smart microgrids. It also explores hybrid models that combine DRL with other AI techniques, such as fuzzy logic and neural networks, to improve performance under uncertainty and nonlinearity. Furthermore, the review evaluates benchmark testbeds, simulation tools, and real-time platforms used to implement and validate these intelligent systems. Challenges such as high computational costs, model generalizability, real-time implementation issues, and cybersecurity vulnerabilities are discussed. The study also highlights recent research trends emphasizing decentralized control, edge computing, and federated learning to enhance scalability and privacy in DRL-based microgrid applications. Future directions suggest the need for explainable AI (XAI), robust training environments, and standardization to facilitate the wider adoption of DRL in real-world microgrid systems. The integration of DRL into smart microgrids represents a transformative shift toward resilient, efficient, and sustainable energy ecosystems. This review offers a comprehensive understanding of how AI and DRL are revolutionizing energy management, providing a foundation for future research and practical deployment in smart energy infrastructures.

This study conducts a comprehensive meta-analysis to evaluate the effectiveness of artificial intelligence (AI)-driven data engineering approaches within cloud-based integration models. Drawing from 122 peer-reviewed studies published between 2015 and 2025—with a combined citation count exceeding 25,000—this research synthesizes empirical findings on how AI techniques such as machine learning, deep learning, reinforcement learning, and natural language processing are transforming core data engineering functions. The analysis focuses on performance outcomes related to data ingestion, transformation, orchestration, and quality assurance across leading cloud platforms including AWS Glue, Azure Data Factory, and Google Cloud Dataflow. Findings reveal that AI integration significantly improves ingestion latency, schema adaptability, and throughput by automating real-time stream handling and multi-source harmonization. In data transformation workflows, AI models enhance feature extraction, reduce redundancy, and facilitate semantic alignment in high-dimensional and unstructured data. AI-enabled orchestration further supports adaptive scheduling, failure recovery, and self-healing pipelines, resulting in increased operational resilience and resource efficiency. Azure Data Factory offering robust hybrid integration and compliance support, and AWS Glue leading in data lake environments. The results affirm that AI is no longer a supplemental feature but a foundational element in building scalable, intelligent, and autonomous data engineering infrastructures. This study contributes to the growing body of literature by offering evidence-based insights and platform-level comparisons that inform strategic decisions for enterprises adopting AI-driven cloud data solutions.

Artificial Intelligence (AI) is emerging as a transformative force in both drug development and orthodontics. This paper provides an overview of key AI techniques employed in these fields. In drug development, AI is accelerating discovery, development, and delivery through generative models, predictive modeling, image analysis, and natural language processing. Orthodontics benefits from AI through image analysis, treatment planning, and patient experience enhancement. Common AI techniques in both domains include machine learning, deep learning, natural language processing, and computer vision. By leveraging these technologies, researchers and clinicians can achieve significant advancements in drug discovery, treatment efficacy, and patient care.

The research relevance is determined by the importance of a thorough study of methods, schemes and models used by artificial intelligence to mechanise creativity in modern conditions of active technological development. The study aims to analyse the main processes taking place in modern art in connection with active technologization of work processes, to identify the leading concepts regarding the possibility of creating machine art in the future, etc. The employed methods are theoretical, such as analysis, systematisation, generalisation, etc., for studying key problems and further development of creativity based on artificial intelligence. The study examines in detail the main developments of Artificial General Intelligence and Artificial Narrow Intelligence, in particular the achievements of Generative adversarial networks and Creative adversarial networks. Artificial intelligence-generated art demonstrates the remarkable capabilities of technologies. The evolving artificial intelligence in the arts introduces “digital art”. Generative Adversarial Networks are used as a foundational tool for artists who use digital methods and texture generation to create unique compositions. Furthermore, sculptors collaborate with artificial intelligence tools to convert drawings into 3D models or transform historical art databases into sculptures. Creative thinking, a hallmark of human intelligence, is determined as artificial intelligence’s ability to generate new and original ideas. The development of emotional intelligence in artificial intelligence enables empathetic responses and the identification of human emotions through voice and facial expressions. The issues of authorised internationality, awareness of the creative process, psychological foundations of artificial empathy and emotional intelligence define the prospects for the development of neuroscience. Challenges persist in defining creativity, authorship, and legal aspects of artificial intelligence-generated art. The study materials may be useful for artists, art educators, technologists, and researchers interested in the intersection of technology and art, legal professionals (especially intellectual property law), and individuals involved in artificial intelligence development may find these findings valuable