Typical artificial intelligence algorithms and real-world applications related to handwritten number classifier

Artificial Intelligence: The Future of Maxillofacial Prognosis and Diagnosis?

Rationale and Objectives: Accurate diagnosis and staging of cervical precancers is essential for practical medicine in determining the extent of the lesion extension and determines the most correct and effective therapeutic approach. For accurate diagnosis and staging of cervical precancers, we aim to create a diagnostic method optimized by artificial intelligence (AI) algorithms and validated by achieving accurate and favorable results by conducting a clinical trial, during which we will use the diagnostic method optimized by artificial intelligence (AI) algorithms, to avoid errors, to increase the understanding on interpretation of colposcopy images and improve therapeutic planning. Materials and Methods: The optimization of the method will consist in the development and formation of artificial intelligence models, using complicated convolutional neural networks (CNN) to identify precancers and cancers on colposcopic images. We will use topologies that have performed well in similar image recognition projects, such as Visual Geometry Group Network (VGG16), Inception deep neural network with an architectural design that consists of repeating components referred to as Inception modules (Inception), deeply separable convolutions that significantly reduce the number of parameters (MobileNet) that is a class of Convolutional Neural Network (CNN), Return of investment for machine Learning (ROI), Fully Convolutional Network (U-Net) and Overcomplete Convolutional Network Kite-Net (KiU-Net). Validation of the diagnostic method, optimized by algorithm of artificial intelligence will consist of achieving accurate results on diagnosis and staging of cervical precancers by conducting a randomized, controlled clinical trial, for a period of 17 months. Results: We will validate the computer assisted diagnostic (CAD) method through a clinical study and, secondly, we use various network topologies specified above, which have produced promising results in the tasks of image model recognition and by using this mixture. By using this method in medical practice, we aim to avoid errors, provide precision in diagnosing, staging and establishing the therapeutic plan in cervical precancers using AI. Conclusion: This diagnostic method, optimized by artificial intelligence algorithms and validated by the clinical trial, which we consider “second opinion”, improves the quality standard in diagnosing, staging and establishing therapeutic conduct in cervical precancer.

The increasing complexity of urban transportation systems and the growing volume of vehicles have made traffic congestion a persistent challenge in modern cities. Efficient traffic flow prediction is essential for mitigating congestion, improving road safety, optimizing traffic signal control, and enhancing overall transportation efficiency. In recent years, artificial intelligence (AI) has emerged as a transformative tool in the field of traffic management, offering sophisticated algorithms capable of modeling, analyzing, and predicting complex traffic patterns with high accuracy. The application of AI in traffic flow prediction leverages vast amounts of real-time and historical data to generate precise forecasts, supporting data-driven decision-making by urban planners and traffic control authorities. The prediction of traffic flow involves analyzing time-series data that exhibit nonlinear, dynamic, and often stochastic behavior. Traditional statistical models, such as autoregressive integrated moving average (ARIMA), have proven to be limited in handling the high dimensionality and variability inherent in traffic systems. In contrast, AI algorithms possess the capacity to learn and adapt from complex data inputs without the need for explicit programming, making them particularly suitable for traffic-related applications. AI algorithms used in traffic flow prediction can be broadly categorized into machine learning (ML) and deep learning (DL) approaches. Machine learning algorithms such as k-nearest neighbors (KNN), support vector machines (SVM), decision trees, and random forests have demonstrated effectiveness in short-term traffic prediction tasks. These algorithms are capable of identifying hidden patterns in traffic data and adjusting to changes in traffic behavior over time. Ensemble methods, which combine the strengths of multiple learning models, further enhance prediction accuracy and robustness. Deep learning algorithms, a subfield of AI inspired by the human brain’s neural architecture, have shown exceptional performance in capturing spatial-temporal dependencies in traffic data. Recurrent neural networks (RNNs), particularly long short-term memory (LSTM) networks and gated recurrent units (GRUs), are widely used for their ability to process sequential data and retain information over extended time intervals. Convolutional neural networks (CNNs) are employed to extract spatial features from traffic sensor data or road network imagery. Hybrid models that integrate CNNs with RNNs have achieved high levels of predictive precision by simultaneously learning spatial and temporal correlations. In addition to supervised learning methods, unsupervised and reinforcement learning techniques are also applied in traffic flow prediction. Clustering algorithms, such as k-means and DBSCAN, assist in identifying traffic patterns, while reinforcement learning models optimize adaptive traffic signal control systems by learning optimal actions through environmental interaction. This study explores the different types of AI algorithms used in traffic flow prediction, examining their theoretical foundations, structural differences, and practical applications. It aims to evaluate the comparative advantages of various algorithms in addressing the challenges of real-time traffic prediction in increasingly complex transportation networks.

This article aims to systematically sort out the development trajectory of artificial intelligence technology from theory to application, and clarify the evolution of its core model, the current challenges and the future development trend, in order to provide a clear reference for in-depth research in this field. For this reason, this article first traces the development process and milestone events of artificial intelligence since the mid-20th century. Then, the core concepts and architectures of four representative model types (Multi-Layer Perceptrons (MLPs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and Transformer) and the successful application cases in key fields such as health care and finance are analyzed. In addition, through structured analysis, this article deeply explores the three core challenges faced by the current artificial intelligence system in the process of moving towards reliable and reliable deployment: model interpretability ("black box" problem), insufficient generalization ability and security vulnerabilities. In response to these problems, this article explores how key technical paths such as artificial intelligence, migration learning and federal learning can provide a blueprint for building the next generation of reliable artificial intelligence systems. Analysis shows that the development of artificial intelligence is shifting from the simple pursuit of performance breakthroughs to the construction of robust, safe and understandable intelligent systems. This transformation requires the coordinated development of algorithm innovation and ethical, legal and social frameworks.

Bioelectronic medicine is an emerging field that uses targeted electrical stimulation to treat chronic diseases by modulating physiological signals at the cellular and neural levels. However, the complexity and variability of human biological systems present major challenges in ensuring consistent and precise therapeutic outcomes using already available bioelectronic devices. Artificial Intelligence (AI), particularly through advanced algorithmic frameworks, has demonstrated significant potential in overcoming these limitations by enabling dynamic, adaptive, and patient-specific interventions. This narrative review explores how AI-enhanced algorithms can significantly improve the functionality of closed-loop systems in bioelectronic medicine. It discusses the integration of various deep learning models, real-time data processing techniques, and intelligent control strategies to enhance precision, adaptability, and patient outcomes. We examined the current literature and technological advances in AI models such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Support Vector Machines (SVM), and Long Short-Term Memory (LSTM) networks, evaluating their applicability in signal interpretation, electrode positioning, and treatment personalization. The review introduces both predictive and non-predictive AI-driven feedback mechanisms used to regulate therapeutic electrical stimulation in chronic conditions. It also outlines system architectures such as multi-level hierarchical control models and embedded AI systems like TinyML, which enable real-time, on-device decision-making with minimal latency. We found out that AI-powered models allow for the comprehensive mapping of biological systems (e.g., connectomes, electromes, genomes), improving targeting specificity for bioelectronic interventions. Predictive models leverage historical and real-time patient data to adjust stimulation parameters proactively, while non-predictive models enable immediate response to dynamic physiological changes. Hierarchical AI architectures offer multi-tasking capabilities essential for long-term, adaptive treatment. Embedded AI systems and edge computing facilitate real-time diagnostics and remote monitoring, enhancing telemedicine applications and patient compliance, particularly in managing complex chronic diseases such as epilepsy, diabetes, and cardiovascular disorders. In summary, our article suggests that the integration of AI into bioelectronic medicine has the potential to revolutionize chronic disease management through highly individualized, responsive, and scalable treatment modalities. This review highlights how AI-driven enhancements can transform traditional closed-loop systems into intelligent therapeutic platforms. However, ethical concerns regarding data privacy, bias, accountability, and regulatory oversight remain significant and warrant further investigation. Advancing this synergy between AI and bioelectric systems could open new frontiers in precision medicine and healthcare accessibility.

Artificial Intelligence (AI) is revolutionizing the accounting profession, offering transformative capabilities for automating tasks, enhancing decision-making, and improving financial accuracy. As AI becomes integral to accounting practices, it brings both significant opportunities and notable ethical challenges. This review examines the intersection of AI and ethical accounting, providing insights into how professionals can navigate the evolving landscape. The adoption of AI in accounting introduces opportunities for increased efficiency and accuracy. AI systems can handle repetitive tasks such as data entry, reconciliation, and transaction categorization, freeing accountants to focus on strategic activities. Advanced AI algorithms can analyze large volumes of financial data to identify patterns, detect anomalies, and provide real-time insights, enhancing decision-making and forecasting accuracy. Moreover, AI-driven predictive analytics can aid in risk assessment and management, helping organizations to anticipate and mitigate potential financial threats. However, the integration of AI in accounting also raises significant ethical concerns. One of the primary challenges is ensuring transparency and accountability in AI decision-making processes. As AI systems often operate as "black boxes," understanding and explaining their outputs can be difficult, potentially leading to issues of trust and compliance. Ethical accounting necessitates that AI systems be designed with transparency in mind, providing clear explanations for their decisions and actions. Data privacy and security represent another critical ethical consideration. The extensive use of financial data by AI systems necessitates robust measures to protect sensitive information from breaches and unauthorized access. Accountants must ensure that AI systems comply with data protection regulations and ethical standards, safeguarding the confidentiality and integrity of financial data. Bias and fairness in AI algorithms are also pressing ethical issues. If not properly addressed, biases in AI systems can lead to unfair outcomes, such as biased financial recommendations or discriminatory practices. Ensuring fairness requires ongoing monitoring and evaluation of AI systems to identify and mitigate biases. In conclusion, while AI offers substantial benefits for the accounting profession, it also presents ethical challenges that must be carefully managed. Accountants must navigate these challenges by promoting transparency, ensuring data privacy and security, and addressing biases in AI systems. By doing so, the accounting profession can harness the potential of AI while upholding ethical standards and maintaining public trust. Keywords: AI, Ethical Accounting, Navigating, Challenges, Opportunities.

The integration of AI is anticipated to bring a major transformation to the quality, value, and impact of radiology on patient care and population health, as well as radiologists' workflows, over the next decade.AI technologies are already being applied across various dental specialties, including orthodontics.There are various applications of AI in Oral and Maxillofacial Radiology.AI is expected to ease the daily workload of physicians and reduce the incidence of misdiagnosis or underdiagnosis in dental practice.Although AI systems are a valuable resource in both dentistry and dental education, it's crucial to recognize that these technological advancements stem from human innovation and discovery.The ability of a machine to mimic intelligent human behaviors to perform complicated tasks like problem-solving, object and word recognition, and decision-making is known as artificial intelligence or AI [1,2].AI has gained popularity recently in several areas, including dentistry, where its uses in oral and maxillofacial imaging show significant promise.Because radiology creates digitally coded images, integrating AI into medical procedures is much simpler [3].Convolutional neural networks, which are capable of handling tasks including image categorization, detection, segmentation, registration, creation, and refinement, are the main focus of recent studies in oral and maxillofacial radiology [4].In this field, artificial intelligence systems have been developed for image analysis, forensic dentistry, radiography diagnosis, and picture quality enhancement [4].However, obtaining useful results necessitates large data sets, and oral and maxillofacial radiologists play a critical role in creating reliable and consistent datasets, which can take a lot of time.The use of AI is expected to bring about a dramatic revolution in the next ten years regarding the workflows of radiologists and the impact of radiology on population health and patient care.Dentists need to use their skills to evaluate patients, choose the best course of treatment, and make clinically sound prognostic judgments.They might, however, occasionally lack the expertise needed to act swiftly and decisively in clinical decisions.Applications of AI can be useful tools for improving performance and decision-making.AI is already being used in several dental specializations, such as orthodontics.The majority of current research focuses on artificial intelligence (AI) models that use artificial neural networks (ANNs) and convolutional neural networks (CNNs) [5].AI developments are anticipated to lessen the everyday burden on healthcare professionals and lower the number of dental misdiagnoses and underdiagnoses.Radiological image research encompasses tasks such as segmenting and classifying teeth, automatically labeling anatomical structures, performing cephalometric analysis, detecting early dental diseases and periodontal conditions, and using jaw radiographs to diagnose specific diseases like osteoporosis [6].

Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive, and heterogeneous interstitial lung disease with a median survival of 2–5 years. Though the diagnosis has been improved due to newly published guidelines, the recognition of the prognosis of IPF remains a challenge. Recently, several studies attempted to build prognostic models by extracting predictive variates from pulmonary function data, basic information, or chest computed tomography (CT) and CT-derived parameters with clinical characteristics. Artificial intelligence (AI) algorithms, including principal component analysis, support vector machine, random survival forest, and convolutional neural network, could be applied to the procedure of IPF prognostic model, that is, region of interest extraction, image feature selection, clinical feature selection, and model construction. Compared to human visualization, AI algorithms show a higher efficiency in calculating and extracting deep features and a lower inter-observer variation. Thus, this review provides a comprehensive CT evaluation of IPF prognostic models and discusses the role of AI in constructing IPF prognostic models. The potential improvements of AI in CT assessments, including time-series CT analysis, optimization of AI algorithms, utilization of multi-modal data, and discovery of new biomarkers through unsupervised algorithms, could be introduced to make a more accurate and convenient assessment for the prognosis of IPF patients. This review describes the status quo and future direction of AI applications in CT analysis for prognostic models of IPF.Take home messageThe review summarizes the applications of CT and AI algorithms for prognostic models in IPF and procedures of model construction. It reveals the current limitations and prospects of AI-aid models, and helps clinicians to recognize the AI algorithms and apply them to more clinical work.

Purpose This study aims to investigate ethical consideration into the data quality management and biases for fairness in artificial intelligence (AI) algorithms. It looks at how biases in AI systems could make it difficult to maintain data quality control in medical travel and tourism. Design/methodology/approach The systematic review of the literature from January 2019 to March 2025 (72 out of 925 articles) considers a variety of keyword combinations used for data quality control in AI systems. The process of systematic literature review aids in identifying studies on data quality management for medical tourism and travel. Findings The results of the study show that data quality visibility should be increased, data rectification should be done consistently and data momentum should be managed. It indicates that audience behavior in medical travel and tourism is significantly impacted by AI-powered digital branding and marketing. Research limitations/implications The research is limited to the single source of methodology, and it is limited in terms of articles collected from Jan 2019 to March 2025. It can be extended to the last decade for more insights and issues in AI use for medical travel and tourism. Practical implications Practical applications include efficient patient processing for international medical travel and tourism, as well as automated AI machine learning. Future research investigations should consistently segment data evaluation to overcome the limited regulatory compliance of data quality management. Social implications Patients (medical tourism) are primary elements in healthy social settings that can be facilitated through AI’s efficient use and maintaining the quality of the data management. Originality/value This study aims to address the main ethical concerns about the quality of AI data, data use monitoring and ethical support for data decisions and implementations inside the AI framework. The usage of digital branding, mobile app development, digital audience behavior, digital marketing principles and artificial intelligence in marketing are some of the important concepts used to accomplish the research goals.

Wearable biosensors and artificial intelligence (AI) are radically improving healthcare by facilitating continuous, non-intrusive, real-time monitoring of physiological and biochemical signals. Wearable biosensors are small, personal devices that can be worn on the body and gather metrics including heart rate, body temperature, blood glucose, and respiratory rate. Wearable biosensors lay the foundation for proactive, personalized healthcare. Chronic disease rates are on the rise, populations are aging across the globe, and there is need for smart systems that support remote care. Smart monitoring systems that utilize biosensors are being adopted for these reasons, especially AI-based monitoring. AI allows for data generated by biosensors to be processed, and used reliably. Machine learning and deep learning models are particularly useful in processing biosensor data. Example machine learning algorithms include convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Deep learning methods are able to remove background noise, feature extraction, data interpretation, and anomaly detection, along with retrospective analysis to predict health events including arrhythmia, stress episodes, hypoglycemia. AI and ML algorithms can use biosensor data for on-time feedback and decision support to clinicians with high amounts of specificity and sensitivity and low connectivity latency. Using Internet of Medical Things (IoMT) based infrastructure, wearable biosensors can securely transmit data privately to discrete clinical or edge cloud providers, to facilitate either clinician oversight of patients in real-time as they do their monitoring, or clinicians being forewarned to support early intervention when monitoring for acute patient events. While these systems can be considered game-changing advancements which include many advantages, there still exists hurdles to adopting systems to improve healthcare. There must be work done to address issues with data privacy, algorithmic biases, device interoperability, and sensor accuracy. Compliance with some privacy laws, such as HIPAA and GDPR is needed and the use of ethical and transparent AI models is necessary. Technical issues that must be addressed more generally include motion-induced signal noise, battery lifespan, and hardware-software integration. In conclusion, wearable biosensors empowered by AI may represent a new frontier in personalized, real-time health monitoring that enables early recognition of health issues, remote supervision, and chronic disease monitoring while alleviating the need for traditional healthcare capacity. To realize the full potential of this functionality, subsequent research should strive to improve data security, model transparency, and system scalability, and to keep patients as the focus of innovation.

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.

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.

The study presents the results of the development and testing of deep learning neural network architectures, which demonstrate high accuracy rates in classifying neurophysiological data, in particular hemodynamic brain activation patterns obtained by functional near-infrared spectroscopy, during solving mathematical problems on spatial-numerical associations. The analyzed signal represents a multidimensional time series of oxyhemoglobin and deoxyhemoglobin dynamics. Taking the specificity of the fNIRS signal into account, a comparative analysis of 2 types of neural network architectures was carried out: (1) architectures based on recurrent neural networks: recurrent neural network with long short-term memory, recurrent neural network with long short-term memory with fully connected layers, bidirectional recurrent neural network with long short-term memory, convolutional recurrent neural network with long short-term memory; (2) architectures based on convolutional neural networks with 1D convolutions: convolutional neural network, fully convolutional neural network, residual neural network. Trained long short-term memory recurrent neural network architectures showed worse results in accuracy in comparison with 1D convolutional neural network architectures. Residual neural network (model_Resnet) demonstrated the highest accuracy rates in three experimental conditions more than 88% in detecting age-related differences in brain activation during spatial-numerical association tasks considering the individual characteristics of the respondents’ signal.

Continual learning in medical devices: FDA's action plan and beyond

Artificial intelligence (AI) systems are increasingly being used in consumer as well and business applications. Companies are using them, Governments are using them, public security, medical researchers, you and I – everybody seems to be somehow touched. As it becomes more advanced and ubiquitous in our lives, it is critical that AI developers and users understand the ethical implications and challenges posed by this powerful technology. This paper explores the key ethical skills and considerations that are essential for navigating the new world of AI. Artificial Intelligence (AI) presents significant ethical dimensions across various sectors, including research, business, and daily life. It is important to appreciate the major ethical dimensions of AI and how they impact AI systems. Policies and guidelines needs to be laid out for development and usage of such systems. Automated Decisions/Responsibility: No matter how intelligent a AI system might perceived to be, it is always prone to making errors. So, AI system that make automated decisions will always be dangerous. Therefore, it is important to ensure that there is always "human intervention" or "human review" for outcomes/decisions made by AI systems. Determining accountability for AI-generated actions or content remains a significant challenge, emphasizing the need for ethical governance frameworks. There has to be human accountability.Fairness/Bias: AI systems can perpetuate existing biases present in training data, leading to discriminatory outcomes.Transparency: The opaque nature of many AI algorithms complicates accountability, making it difficult to understand decision-making processes.Privacy: AI's capability to process vast datasets can infringe on individual privacy rights, necessitating stringent data protection measuresHuman Dignity: The risk of AI-generated content resembling existing works raises ethical issues regarding originality and authorship.