SCIENTIFIC AND METHODOLOGICAL APPROACH TO OPTIMIZING SUPPLY CHAIN MANAGEMENT USING ARTIFICIAL INTELLIGENCE MODELS

Artificial Intelligence: The Future of Maxillofacial Prognosis and Diagnosis?

While artificial intelligence (AI) experienced an increasing interest in industry during the past decade, the true potential and applicability of AI for automotive original equipment manufacturers (OEMs) and suppliers in real-world scenarios have not been clearly understood. Most applications of AI focus on the development of connected and autonomous cars, rather than the optimisation of automotive operations and manufacturing processes. This work, therefore, bridged this gap and shed light on the topic of AI in the context of automotive manufacturing and Industry 4.0. It aimed to promote understanding and provide up-to-date insights on specific models and methods of AI, applications that have been achieved with best practices as well as the problems that were encountered, underpinned with possible future prospects. A systematic literature review approach was adopted to ensure broad and thorough coverage of current knowledge and the identification of relevant literature on the topic. The literature search was confined to papers that were published from 2015 onwards using the databases of IEEE and ScienceDirect as primary sources, with a three-keyword search phrase to narrow down the results and increase specificity. A total of 359 papers were identified and subsequently screened for eligibility, of which 84 papers were selected for quantitative and 79 papers for qualitative analysis. The results of the quantitative analysis confirmed that the topic has markedly increased in significance, with a mere 3 papers published in 2015 and 33 papers in 2021. The majority of papers dealt with solving problems in production (39.29%), quality (35.71%) and assembly (16.67%), whereas supply chain (5.95%) and business intelligence (2.38%) were inadequately represented. The results of the qualitative analysis revealed that machine learning methods dominate current research and automotive applications, with neural networks as the most used out of more than 70 identified models. The industrial applicability was confirmed by many use cases including quality inspection, robot assembly, human–robot collaboration, material demand prediction or AI-enabled manufacturing decision making. The problems of such applications were mainly attributed to data availability and quality, model development and gaps in simulation, system integration, the complexity of automotive processes, the physical conditions of the system environment and dynamic change. For industrial applications it is thus recommended to further optimise AI methods and models, enabling a wider system integration by harvesting the potential of big data and both edge and cloud computing.KeywordsArtificial intelligenceAI models and methodsAutomotive manufacturingSystematic literature review

Real-World Surveillance of FDA-Cleared Artificial Intelligence Models: Rationale and Logistics.

The rapid proliferation of artificial intelligence (AI) technologies across various industries and decision-making processes has undeniably transformed the way of approaching complex problems and tasks. AI systems have proven their prowess in areas such as healthcare, finance, and autonomous systems, revolutionizing how decisions are made. Nevertheless, this proliferation of AI has raised critical concerns regarding the transparency, accountability, and fairness of these systems, as many of the state-of-the-art AI models often resemble complex black boxes. These intricate models, particularly deep learning neural networks, harbor non-linear relationships that are difficult for human users to decipher, thereby raising concerns about bias, fairness, and overall trustworthiness in AI-driven decisions. The urgency of this issue is underscored by the realization that AI should not merely be accurate; it should also be interpretable. Explainable AI (XAI) has emerged as a vital field of research, emphasizing the development of models and techniques that render AI systems comprehensible and transparent in their decision-making processes. This paper investigates into the relevance and significance of XAI across various domains, including healthcare, finance, and autonomous systems, where the ability to understand the rationale behind AI decisions is paramount. In healthcare, where AI assists in diagnosis and treatment, the interpretability of AI models is crucial for clinicians to make informed decisions. In finance, applications like credit scoring and investment analysis demand transparent AI to ensure fairness and accountability. In the realm of autonomous systems, transparency is indispensable to guarantee safety and compliance with regulations. Moreover, government agencies in areas such as law enforcement and social services require interpretable AI to maintain ethical standards and accountability. This paper also highlights the diverse array of research efforts in the XAI domain, spanning from model-specific interpretability methods to more general approaches aimed at unveiling complex AI models. Interpretable models like decision trees and rule-based systems have gained attention for their inherent transparency, while integrating explanation layers into deep neural networks strives to balance accuracy with interpretability. The study emphasizes the significance of this burgeoning field in bridging the gap between AI's advanced capabilities and human users' need for comprehensible AI systems. It seeks to contribute to this field by exploring the design, development, and practical applications of interpretable AI models and techniques, with the ultimate goal of enhancing the trust and understanding of AI-driven decisions.

Lung cancer remains one of the deadliest cancers worldwide, with survival rates significantly dependent on early detection. Traditional diagnostic methods, while effective, often identify lung cancer in advanced stages, limiting treatment options. Recently, artificial intelligence (AI) and deep learning (DL) models have gained attention for their potential to assist in early lung cancer diagnosis through radiological data, particularly computed tomography (CT) and X-ray imaging. This review examines recent advancements in AI and DL models specifically applied to early lung cancer detection, offering a comprehensive look at model architecture, data preprocessing techniques, and performance metrics. AI models, particularly those using convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have shown significant promise in improving diagnostic accuracy. In studies published over the past two years, CNN-based models have achieved sensitivity rates upwards of 90% when applied to large datasets, such as the LIDC-IDRI, a publicly available database containing thousands of annotated lung scans. Furthermore, hybrid approaches combining AI models with radiologist expertise have demonstrated reduced false positives, a frequent challenge in automated image analysis. A critical factor in these advancements has been data quality and volume. Large, annotated datasets with high-resolution images have enabled more robust training and validation, essential for refining these models. However, challenges remain, particularly regarding the standardization of data across institutions, variations in scanner quality, and ethical concerns surrounding patient privacy. The review also highlights studies exploring multi-modal approaches, integrating radiological data with clinical records and genetic markers to create more personalized diagnostic tools. These multi-modal methods have the potential to improve predictive accuracy further, though they currently require more extensive validation. Overall, while AI and DL technologies offer transformative potential for early lung cancer detection, widespread implementation depends on continued research in model accuracy, data standardization, and ethical safeguards. This paper concludes by emphasizing the need for collaboration across disciplines—AI researchers, radiologists, and oncologists—to refine these models into reliable tools that can be integrated seamlessly into clinical workflows for timely and accurate lung cancer diagnosis.

Diabetes mellitus is a chronic metabolic disorder with significant global prevalence and associated healthcare burdens, necessitating early detection and effective management strategies. The integration of Machine Learning (ML) and Artificial Intelligence (AI) has revolutionized diabetes care, offering innovative approaches to prediction, monitoring, and personalized management. This study conducted a systematic review of 82 high-quality peer-reviewed articles, following the PRISMA guidelines, to provide a comprehensive evaluation of ML and AI applications in diabetes prediction and management. The review highlights the widespread adoption of supervised learning models, such as Random Forest and Support Vector Machines (SVM), which consistently demonstrate high accuracy and reliability in predicting diabetes risk. Ensemble learning methods, particularly Gradient Boosting, emerged as superior techniques for predictive performance, while deep learning models, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), proved effective in analyzing unstructured data such as medical images and time-series glucose data. The integration of AI into wearable devices and mobile health applications has further enhanced real-time monitoring and glycemic control, bridging the gap between technological advancements and practical healthcare solutions. Despite these advancements, challenges such as data imbalance, limited external validation, and the need for explainable AI frameworks persist, underscoring the necessity for methodological rigor and standardization. This review provides critical insights into the current state, limitations, and opportunities of ML and AI in diabetes care, emphasizing their transformative potential in addressing this global health challenge.

Cancer remains a leading cause of morbidity and mortality worldwide, necessitating innovative approaches for early detection and effective management. Predictive analytics, powered by machine learning (ML) and artificial intelligence (AI), offers a transformative potential in cancer care by integrating multi-source health data for early diagnosis, risk assessment, and personalized treatment. This presents a predictive analytics framework that leverages diverse data sources, including electronic health records (EHRs), genomic and biomarker data, medical imaging, wearable sensors, and lifestyle/environmental factors. By combining these heterogeneous data streams, AI models can identify high-risk individuals, detect cancer at its earliest stages, and optimize treatment strategies based on patient-specific insights. The framework employs advanced ML techniques, such as supervised learning models (Random Forests, Support Vector Machines), deep learning architectures (Convolutional Neural Networks, Recurrent Neural Networks), and Natural Language Processing (NLP) for analyzing clinical notes. Federated learning is also explored as a privacy-preserving approach to facilitate secure data sharing across institutions while maintaining compliance with regulatory standards such as HIPAA and GDPR. The integration of predictive analytics with telemedicine platforms and AI-driven clinical decision support systems further enhances real-time monitoring and personalized patient care. Despite its promise, challenges such as data privacy concerns, heterogeneity in multi-source data integration, model interpretability, and computational scalability must be addressed to ensure the effective implementation of AI-driven cancer diagnostics. Future advancements in Explainable AI (XAI), blockchain for secure data exchange, and multi-modal data fusion will further strengthen predictive analytics in oncology. This study underscores the critical role of big data and AI in revolutionizing cancer detection and management, paving the way for improved patient outcomes and more efficient healthcare delivery.

PurposeIn India, artificial intelligence (AI) application in supply chain management (SCM) is still in a stage of infancy. Therefore, this article aims to study the factors affecting artificial intelligence adoption and validate AI’s influence on supply chain risk mitigation (SCRM).Design/methodology/approachThis study explores the effect of factors based on the technology, organization and environment (TOE) framework and three other factors, including supply chain integration (SCI), information sharing (IS) and process factors (PF) on AI adoption. Data for the survey were collected from 297 respondents from Indian agro-industries, and structural equation modeling (SEM) was used for testing the proposed hypotheses.FindingsThis study’s findings show that process factors, information sharing, and supply chain integration (SCI) play an essential role in influencing AI adoption, and AI positively influences SCRM. The technological, organizational and environmental factors have a nonsignificant negative relation with artificial intelligence.Originality/valueThis study provides an insight to researchers, academicians, policymakers, innovative project handlers, technology service providers, and managers to better understand the role of AI adoption and the importance of AI in mitigating supply chain risks caused by disruptions like the COVID-19 pandemic.

With advancements in science and technology, there has been phenomenal developments in the application of neural networks in dentistry. This systematic review aimed to report on the effectiveness of artificial intelligence (AI) applications designed for endodontic diagnosis, decision-making, and prediction of prognosis. Studies reporting on AI applications in endodontics were identified from the electronic databases such as PubMed, Medline, Embase, Cochrane, Google Scholar, Scopus, and Web of Science, for original research articles published from January 1, 2000, to June 1, 2020. A total of 10 studies that met our eligibility criteria were further analyzed for qualitative data. QUADAS-2 was applied for synthesis of the quality of the studies included. A wide range of AI applications have been implemented in endodontics. The neural networks employed were mostly based on convolutional neural networks (CNNs) and artificial neural networks (ANNs) in their neural architectures. These AI models have been used for locating apical foramen, retreatment predictions, prediction of periapical pathologies, detection and diagnosis of vertical root fractures, and assessment of root morphologies. These studies suggest that the neural networks performed similar to the experienced professionals in terms of accuracy and precision. In some studies, these models have even outperformed the specialists. These models can be of greater assistance as an expert opinion for less experienced and nonspecialists.

Statement of problem: Artificial intelligence (AI) applications are increasingly used in restorative process. But existing dentistry restoration effectiveness as well as growth and IA applications have not yet been systematically analyzed or documented. Purpose: The goal of such comprehensive evaluation is to discover & assess the abilities associated with artificial intelligence models in restorative dentistry for the analysis of caries as well as vertical tooth fracture, evaluate margins in preparing tooth, and analyze reconstructive failures. Methods: A systematic electronic review of 5 databases was carried out: PubMed/ MEDLINE, World of Science, EMBASE, Scopus and Cochrane. The investigation was carried out manually as well. Research using AI models was chosen on the basis of 4 criterion: dental caries diagnostics, diagnostics, vertical tooth fracture, tooth preparation recognition, & cause of failure of restoration. Both researchers rated the quality of the study for Critical Appraisal Checklist for Quasi-Experimental Studies (nonrandomized experimental studies). The 3rd author was asked for resolving the dispute. Results: 34 researches were made the part of this analysis: from which 29 contains artificial intelligence techniques including the diagnostics or treatment related to caries and its causes according to sensitivity models, 2 to diagnose vertical tooth fractures, 1 to prepare the teeth. Among the studied analysis, the accuracy of caries diagnosis in the AI models was tested from 76-88.3%, sensitivity from 73-90%, as well as specificity from 61.5-93%. In the study, the accuracy of predicted caries ranges from 83.6-97.1%. The performed research showed the accuracy of the analysis of a vertical tooth fracture from 88.3-95.7%. The study, which uses AI models to find a destination, had details with the range of 90.6-97.4%. Conclusions: AI models are a powerful tool for diagnosing caries as well as vertical tooth fractures, recognizing preparation margins and predicting restoration failures. But, the dental use of AI models continues to evolve. More research is needed to evaluate the clinical effectiveness of artificial intelligence models in restorative dentistry. Keyword: Artificial intelligence, restorative dentistry, vertical tooth fractures.

Emotion recognition using artificial intelligence (AI) has become an increasingly vital area of research, offering transformative applications across healthcare, human-computer interaction, marketing, education, and entertainment. This paper provides a comprehensive review of the techniques and applications of AI in emotion recognition. Key methodologies, including facial expression analysis, speech emotion recognition, text-based emotion detection, physiological signal analysis, and multimodal approaches, are explored in detail. Each technique's underlying algorithms, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and natural language processing (NLP) methods, are examined for their efficacy in identifying human emotions. This paper also delves into practical applications, showcasing how emotion recognition enhances mental health monitoring, improves user experience, optimizes customer service, personalizes education, and enriches entertainment media. Moreover, this study addresses the ethical and technical challenges associated with emotion recognition, such as data privacy concerns, potential biases in AI models, and the need for accuracy and reliability in emotion detection systems. The discussion extends to future directions, emphasizing the integration of AI with wearable devices, advancements in multimodal systems, and the expansion of emotion recognition technology into new domains. By presenting a thorough analysis of current techniques and applications, this paper aims to highlight the significant impact and potential of AI-driven emotion recognition, paving the way for future innovations and ethical considerations in this dynamic field. The proliferation of Internet of Things (IoT) technology has enabled the development of smart home systems that can enhance home security, energy efficiency, and convenience. This paper presents the design and implementation of an IoT-based smart home security system that utilizes a variety of sensors and actuators to monitor and control various aspects of home security. The proposed system integrates multiple components, including motion detectors, door/window sensors, cameras, smart locks, and a central control unit, all interconnected through a wireless network. The system leverages cloud computing and machine learning techniques to process sensor data, detect potential threats, and initiate appropriate responses. A user-friendly mobile application allows homeowners to monitor and control the security system remotely. The paper discusses the system architecture, hardware and software components, communication protocols, and security considerations. The implemented system demonstrates the feasibility and potential benefits of IoT-based smart home security solutions.

Background:Brain tumors represent a diverse group of central nervous system neoplasms that require precise diagnosis and characterization for effective treatment. Advances in neuroimaging have improved the detection of these tumors; however, conventional interpretation methods remain time-intensive and subject to variability. Artificial intelligence (AI), particularly through machine learning and deep learning, has emerged as a powerful tool in neuro-oncological imaging, offering automation, consistency, and enhanced diagnostic performance. Objective:This review aims to provide a comprehensive overview of AI-assisted imaging tools used in the detection and characterization of brain tumors. It highlights key AI technologies, their clinical applications, performance compared to human experts, and the emerging trends shaping the future of AI in neuro-oncology. Methods:A narrative review was conducted of recent peer-reviewed studies focused on the application of AI in brain tumor imaging. Emphasis was placed on machine learning and deep learning models used for tumor segmentation, histopathologic and molecular subtype prediction, prognostic modeling, and treatment response monitoring. Technical challenges, ethical concerns, and regulatory considerations were also examined. Results:AI models have demonstrated high accuracy in tasks such as tumor segmentation, classification of tumor types and grades, non-invasive prediction of molecular markers (e.g., IDH mutation, MGMT methylation), and survival prediction. Emerging techniques such as federated learning, multimodal data integration, and explainable AI are addressing key limitations, including data privacy, generalizability, and clinical trust. Conclusion:AI-assisted imaging holds considerable promise in improving the accuracy, speed, and personalization of brain tumor diagnosis and management. For widespread clinical adoption, future efforts should focus on multi-institutional collaboration, prospective validation, regulatory alignment, and clinician education. With continued advancement, AI can become a valuable adjunct in the neuro-oncology diagnostic arsenal, ultimately contributing to better patient outcomes.

The growing complexity and critical importance of energy infrastructure necessitate the adoption of advanced maintenance strategies to ensure reliability, efficiency, and sustainability. Traditional maintenance approaches, such as reactive and preventive maintenance, have proven inadequate in addressing the challenges posed by modern energy systems, particularly with the integration of renewable energy sources. This research explores the potential of artificial intelligence (AI)-driven predictive maintenance (PdM) as a transformative solution for the energy sector. By leveraging historical maintenance records and real-time sensor data, AI models, including machine learning and deep learning techniques, were developed to predict equipment failures with high accuracy. The study employed a mixed-methods approach, combining quantitative analysis of data and qualitative insights from case studies conducted in wind farms, solar power plants, and thermal power plants. The results demonstrated that AI-driven PdM significantly reduces unplanned downtime, lowers maintenance costs, and extends the lifespan of critical energy assets. Deep learning models, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), outperformed traditional models in terms of predictive accuracy, with F1-scores exceeding 90%. Despite the promising results, the research also identified challenges related to data quality, system integration, and organizational adoption. These challenges highlight the need for further research in areas such as explainable AI, the integration of IoT and digital twins, and the exploration of PdM applications across different sectors. The findings underscore the potential of AI-driven PdM to revolutionize maintenance practices in the energy sector, offering a pathway to more reliable, efficient, and sustainable energy systems.

This paper presents a systematic review of studies related to Artificial Intelligence (AI) and Machine Learning (ML) applications in Supply Chain Management (SCM). Our systematic search studied 109 journal articles published between 2007 and 2025. It was presented definitions for AI, ML, and SCM and a bibliometric analysis was carried out to examine trends and patterns in the existing literature. The 109 articles were grouped into five clusters based on keywords and abstracts, identifying promising research areas and proposes directions for future investigations. The current literature on AI and ML in SCM is expanding, with an increasing number of publications and growing diversity in applications, although significant gaps remain warranting attention. Various AI and ML techniques, including supervised learning algorithms, neural networks, and optimization techniques, have been studied in scenarios such as demand forecasting, inventory management, and risk analysis. Additionally, AI and ML methods are expected to gain broader acceptance in SCM scenarios, such as, application of Generative AI (GenAI) and Large Multimodal Models (LMMs); Quantum ML techniques to handle with large SCM data; to implement SHAP (SHapley Additive exPlanations) for demand forecasting and fraud detection; and to use Generative AI models (like, GPT-4) to generate synthetic supply chain scenarios.

Solar energy-based technologies have developed rapidly in recent years, however, the inability to appropriately estimate solar energy resources is still a major drawback for these technologies. In this study, eight different artificial intelligence (AI) models namely; convolutional neural network (CNN), artificial neural network (ANN), long short-term memory recurrent model (LSTM), eXtreme gradient boost algorithm (XG Boost), multiple linear regression (MLR), polynomial regression (PLR), decision tree regression (DTR), and random forest regression (RFR) are designed and compared for solar irradiance prediction. Additionally, two hybrid deep neural network models (ANN-CNN and CNN-LSTM-ANN) are developed in this study for the same task. This study is novel as each of the AI models developed was used to estimate solar irradiance considering different timesteps (hourly, every minute, and daily average). Also, different solar irradiance datasets (from six countries in Africa) measured with various instruments were used to train/test the AI models. With the aim to check if there is a universal AI model for solar irradiance estimation in developing countries, the results of this study show that various AI models are suitable for different solar irradiance estimation tasks. However, XG boost has a consistently high performance for all the case studies and is the best model for 10 of the 13 case studies considered in this paper. The result of this study also shows that the prediction of hourly solar irradiance is more accurate for the models when compared to daily average and minutes timestep. The specific performance of each model for all the case studies is explicated in the paper.