Integration Of Artificial Intelligence Research Articles

Diagnostic accuracy of contrast-enhanced computed tomography in assessing bone invasion in patients with oral squamous cell carcinoma.

This study aimed to evaluate the diagnostic accuracy of contrast-enhanced computed tomography (CT) in detecting bone invasion in oral squamous cell carcinoma (OSCC) patients and to explore clinicopathological factors associated with its reliability. 417 patients underwent preoperative contrast-enhanced CT followed by radical surgery. The presence or absence of bone invasion served as the outcome variable, with histopathologic examination of the resection specimen considered the gold standard. Statistical analyses, comprising correlation analyses and the determination of sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), were conducted. CT exhibited 76.85% sensitivity, 82.20% specificity, 47.14% PPV, and 89.67% NPV. False-positive and false-negative rates were 11.27% and 5.99%, respectively. Artifacts affected assessment in 44 patients, but not in those with bone invasion. Tumor size, depth of invasion (DOI), tumor localization at the upper jaw, lymphatic invasion, and perineural invasion correlated with incorrect identification of bone invasion (Chi-square, p < 0.05). Despite utilizing thin-section CT, notable false-positive and false-negative results persisted. Patients with T3 tumors, DOI ≥ 10mm, or upper jaw tumors are at higher risk for misidentification of bone invasion. Combining multiple methods may enhance diagnostic accuracy, and the integration of artificial intelligence or tracking electrolyte disturbances by tumor depth profiling shows promise for further assessment of bone invasion before histopathology. Surgeons should consider these insights when planning tumor resection. Supplementary imaging may be warranted in cases with high risk factors for misidentification. Further methodological advancements are crucial for enhancing diagnostic precision.

A comprehensive review on role of magnetic resonance imaging in the precise measurement of corpus callosum

Magnetic Resonance Imaging (MRI) has emerged as a powerful and non-invasive imaging technique, playing a pivotal role in neuroimaging research and clinical diagnosis. This comprehensive review explores the significance of MRI in the precise measurement of the corpus callosum, a key structure connecting the two hemispheres of the brain. The corpus callosum plays a crucial role in inter hemispheric communication and is implicated in various neurological and psychiatric disorders. The review begins by elucidating the anatomy and functions of the corpus callosum, emphasizing its importance in facilitating seamless communication between the cerebral hemispheres. Subsequently, the paper delves into the historical evolution of MRI techniques and how advancements have enhanced the visualization and measurement capabilities of the corpus callosum. A detailed examination of various MRI modalities, including structural MRI, diffusion tensor imaging (DTI), and functional MRI, is presented in the context of corpus callosum analysis. Structural MRI provides high-resolution images for accurate morphological assessments, while DTI offers insights into white matter microstructure and fiber tractography, allowing for a more comprehensive understanding of the corpus callosum's integrity. The review also highlights the role of MRI in investigating alterations in the corpus callosum associated with neurological disorders such as multiple sclerosis, epilepsy, and neurodevelopmental disorders. Additionally, the paper explores how functional MRI contributes to understanding the functional connectivity and inter hemispheric interactions mediated by the corpus callosum. Challenges and limitations in the measurement of the corpus callosum using MRI are discussed, including methodological considerations, image artifacts, and potential sources of variability. The review concludes by outlining future directions in corpus callosum imaging research, including the integration of advanced imaging techniques, artificial intelligence, and large-scale multi-center studies to enhance the precision and reliability of measurements.In summary, this comprehensive review underscores the pivotal role of MRI in advancing our understanding of the corpus callosum, providing valuable insights into both normal brain function and pathological conditions. The continual evolution of MRI techniques holds promise for further refining the assessment of the corpus callosum, ultimately contributing to improved diagnostic accuracy and therapeutic interventions in neurological and psychiatric disorders.

AI-based predictive approach via FFB propagation in a driven-cavity of Ostwald de-Waele fluid using CFD-ANN and Levenberg–Marquardt

The integration of Artificial Intelligence (AI) and Machine Learning (ML) techniques into computational science has ushered in a new era of innovation and efficiency in various fields, with particular significance in computational fluid dynamics (CFD). Several methods based on AI and Machine Learning (ML) have been standardized in many fields of computational science, including computational fluid dynamics (CFD). This study aims to couple CFD with artificial neural networks (ANNs) to predict the fluid forces that arise when a flowing fluid interacts with obstacles installed in the flow domain. The momentum equation elucidating the flow has been simulated by adopting the finite element method (FEM) for a range of rheological and kinematic conditions. Hydrodynamic forces, including pressure drop between the back and front of the obstacle, surface drag, and lift variations, are measured on the outer surface of the cylinder via CFD simulations. This data has subsequently been fed into a Feed-Forward Back (FFB) propagation neural network for the prediction of such forces with completely unknown data. For all cases, higher predictivity is achieved for the drag coefficient (CD) and lift coefficient (CL) since the mean square error (MSE) is within ± 2% and the coefficient of determination (R) is approximately 99% for all the cases. The influence of pertinent parameters like the power law index (n) and Reynolds number (Re) on velocity, pressure, and drag and lift coefficients is also presented for limited cases. Moreover, a significant reduction in computing time has been noticed while applying hybrid CFD-ANN approach as compared with CFD simulations only.

Artificial Intelligence in Medicine: A New Frontier

Artificial intelligence (AI) refers to the engineering and science of making intelligent machines through algorithms or rules, mimicking human cognitive functions, such as learning and problem-solving. AI has several branches, such as machine learning and deep learning, which can add intelligence to applications. Machine learning is the study of algorithms that allow computer programs to improve automatically through experience. Deep learning algorithms learn from an extensive, multi-layered collection of interconnected processes and expose these processors to many examples. In the coming years, the integration of AI in routine medical care is expected to revolutionize Medicine, potentially improving patient care and quality of life. The time required for a diagnosis can be greatly reduced, and diagnostic efficiency can be significantly enhanced when AI assists clinicians. Large language model chatbots are capable of clinical expert-level medical note-taking, consultation, and questionanswering. Chatbotscan generate human-like text, may help diagnose diseases based on medical records, and may suggest treatment options or plans. Artificial intelligence algorithms, particularly deep learning, have demonstrated remarkable progress in radiological image analysis and diagnosis and may improve radiologists’ efficiency. These algorithms may also improve diagnostic accuracy in dermatology, histopathology, fundoscopy, endoscopy, and other medical images. Natural language processing and ambient clinical intelligence automate administrative duties like recording patient visits in electronic health records, streamlining clinical workflow, and freeing up doctors to spend more time with patients. AI may also help with new drug discoveries, precision medicine, and clinical research. AI developments can revolutionize several healthcare-related fields and pave the way for a more individualized, accurate, predictive, and portable future. Bangladesh J Medicine 2024; 35: 54-60

Opportunities and Challenges of Integrating Generative Artificial Intelligence in Education

This paper thoroughly examines both the opportunities and obstacles associated with integrating Generative Artificial Intelligence (AI) into educational settings. It explores how Generative AI has the potential to enrich learning experiences, customize education for individuals, and foster creativity. However, it also confronts several challenges including ethical dilemmas, safeguarding data privacy, mitigating algorithmic biases, and reshaping the role of educators. Through a synthesis of theoretical frameworks and empirical research, the paper offers valuable insights into effective strategies for navigating these challenges. It emphasizes the importance of establishing ethical guidelines, ensuring transparency in algorithms, and adopting inclusive design principles during AI integration. Furthermore, the paper underscores the importance of providing educators with adequate training and professional development opportunities to effectively utilize AI tools. Additionally, it advocates for ongoing dialogue among stakeholders—such as educators, policymakers, technologists, and students—to steer responsible AI integration in education. Ultimately, the paper advocates for a collaborative approach that prioritizes human-centric values, equity, and diversity. While Generative AI holds promise for revolutionizing educational practices, its integration requires thoughtful consideration of ethical, social, and pedagogical implications. Through proactive collaboration and partnership, educators can leverage AI's potential to create more immersive, tailored, and equitable learning environments.

Artificial intelligence in surgery.

Artificial intelligence (AI) is rapidly emerging in healthcare, yet applications in surgery remain relatively nascent. Here we review the integration of AI in the field of surgery, centering our discussion on multifaceted improvements in surgical care in the preoperative, intraoperative and postoperative space. The emergence of foundation model architectures, wearable technologies and improving surgical data infrastructures is enabling rapid advances in AI interventions and utility. We discuss how maturing AI methods hold the potential to improve patient outcomes, facilitate surgical education and optimize surgical care. We review the current applications of deep learning approaches and outline a vision for future advances through multimodal foundation models.

Enhancing manufacturing productivity: A review of AI-Driven supply chain management optimization and ERP systems integration

This abstract delves into the realm of manufacturing productivity enhancement through the review of AI-driven supply chain management (SCM) optimization and Enterprise Resource Planning (ERP) systems integration. As industries strive for operational excellence, the convergence of artificial intelligence (AI) and supply chain management emerges as a transformative force in driving efficiency, agility, and competitiveness. Through a comprehensive analysis, this abstract examines the synergistic relationship between AI-driven SCM optimization and the integration of ERP systems, elucidating their collective impact on manufacturing productivity. AI-driven SCM optimization encompasses a spectrum of technologies and methodologies, including predictive analytics, machine learning, and autonomous decision-making systems, aimed at optimizing various facets of the supply chain, from demand forecasting and inventory management to production planning and logistics optimization. By harnessing the power of AI, manufacturers can enhance forecasting accuracy, reduce lead times, optimize inventory levels, and mitigate supply chain disruptions, thereby improving overall productivity and customer satisfaction. Integration of ERP systems plays a complementary role in manufacturing productivity enhancement by providing a centralized platform for data management, process automation, and cross-functional collaboration. Through seamless integration with AI-driven SCM optimization tools, ERP systems enable real-time data exchange, actionable insights, and end-to-end visibility across the supply chain, facilitating informed decision-making and agile response to dynamic market conditions. Drawing insights from case studies and industry examples, this abstract highlights best practices, challenges, and emerging trends in AI-driven SCM optimization and ERP systems integration. Strategies for successful implementation, including organizational readiness assessment, change management, and stakeholder engagement, are discussed to guide manufacturers in unlocking the full potential of these transformative technologies. In conclusion, the convergence of AI-driven SCM optimization and ERP systems integration offers a compelling pathway for enhancing manufacturing productivity, driving operational excellence, and sustaining competitive advantage in the digital era.. Keywords: Artificial Intelligence, Supply Chain Management, Enterprise Resource Planning, Manufacturing Productivity, AI Integration, Predictive Analytics.

Navigating the ethical landscape of artificial intelligence in radiography: a cross-sectional study of radiographers’ perspectives

BackgroundThe integration of artificial intelligence (AI) in radiography presents transformative opportunities for diagnostic imaging and introduces complex ethical considerations. The aim of this cross-sectional study was to explore radiographers’ perspectives on the ethical implications of AI in their field and identify key concerns and potential strategies for addressing them.MethodsA structured questionnaire was distributed to a diverse group of radiographers in Saudi Arabia. The questionnaire included items on ethical concerns related to AI, the perceived impact on clinical practice, and suggestions for ethical AI integration in radiography. The data were analyzed using quantitative and qualitative methods to capture a broad range of perspectives.ResultsThree hundred eighty-eight radiographers responded and had varying levels of experience and specializations. Most (44.8%) participants were unfamiliar with the integration of AI into radiography. Approximately 32.9% of radiographers expressed uncertainty regarding the importance of transparency and explanatory capabilities in the AI systems used in radiology. Many (36.9%) participants indicated that they believed that AI systems used in radiology should be transparent and provide justifications for their decision-making procedures. A significant preponderance (44%) of respondents agreed that implementing AI in radiology may increase ethical dilemmas. However, 27.8%expressed uncertainty in recognizing and understanding the potential ethical issues that could arise from integrating AI in radiology. Of the respondents, 41.5% stated that the use of AI in radiology required establishing specific ethical guidelines. However, a significant percentage (28.9%) expressed the opposite opinion, arguing that utilizing AI in radiology does not require adherence to ethical standards. In contrast to the 46.6% of respondents voicing concerns about patient privacy over AI implementation, 41.5% of respondents did not have any such apprehensions.ConclusionsThis study revealed a complex ethical landscape in the integration of AI in radiography, characterized by enthusiasm and apprehension among professionals. It underscores the necessity for ethical frameworks, education, and policy development to guide the implementation of AI in radiography. These findings contribute to the ongoing discourse on AI in medical imaging and provide insights that can inform policymakers, educators, and practitioners in navigating the ethical challenges of AI adoption in healthcare.

A Beginner's Guide to Artificial Intelligence for Ophthalmologists.

The integration of artificial intelligence (AI) in ophthalmology has promoted the development of the discipline, offering opportunities for enhancing diagnostic accuracy, patient care, and treatment outcomes. This paper aims to provide a foundational understanding of AI applications in ophthalmology, with a focus on interpreting studies related to AI-driven diagnostics. The core of our discussion is to explore various AI methods, including deep learning (DL) frameworks for detecting and quantifying ophthalmic features in imaging data, as well as using transfer learning for effective model training in limited datasets. The paper highlights the importance of high-quality, diverse datasets for training AI models and the need for transparent reporting of methodologies to ensure reproducibility and reliability in AI studies. Furthermore, we address the clinical implications of AI diagnostics, emphasizing the balance between minimizing false negatives to avoid missed diagnoses and reducing false positives to prevent unnecessary interventions. The paper also discusses the ethical considerations and potential biases in AI models, underscoring the importance of continuous monitoring and improvement of AI systems in clinical settings. In conclusion, this paper serves as a primer for ophthalmologists seeking to understand the basics of AI in their field, guiding them through the critical aspects of interpreting AI studies and the practical considerations for integrating AI into clinical practice.

Harnessing artificial intelligence to develop strategic marketing goals

In today's rapidly evolving business landscape, harnessing artificial intelligence (AI) has become imperative for organizations striving to remain competitive. This concept paper explores the integration of AI in developing strategic marketing goals, aiming to provide a comprehensive framework for leveraging AI's capabilities effectively. The paper begins by outlining the current state of AI in marketing, highlighting its transformative potential across various facets such as customer segmentation, personalized targeting, predictive analytics, and automation. It underscores the necessity for businesses to embrace AI-driven approaches to stay ahead in an increasingly data-driven marketplace. Moreover, the concept paper delves into the strategic implications of AI adoption in marketing. It emphasizes the need for alignment between AI initiatives and overarching business objectives, emphasizing the role of AI in enhancing customer experience, optimizing resource allocation, and driving revenue growth. Furthermore, the paper addresses key considerations and challenges associated with integrating AI into marketing strategies, including data privacy concerns, ethical implications, and organizational readiness. It advocates for a structured approach to AI implementation, encompassing robust data governance, talent acquisition, and continuous monitoring to mitigate risks effectively. Drawing upon industry best practices and case studies, the concept paper elucidates practical methodologies for leveraging AI in setting strategic marketing goals. It emphasizes the importance of data-driven decision-making, iterative experimentation, and agile adaptation to capitalize on AI's potential for driving innovation and competitive advantage. In conclusion, the concept paper advocates for a proactive stance towards embracing AI in marketing strategy formulation. By strategically harnessing AI's capabilities, organizations can not only enhance their marketing effectiveness but also cultivate long-term sustainable growth in today's dynamic business environment. Keywords: AI, Strategic, Marketing, Environment, Goals, Business.

The Impact of Artificial Intelligence on Legal Systems: Challenges and Opportunities

The integration of artificial intelligence into legal systems has engendered a paradigm shift in the legal landscape, presenting a complex interplay of challenges and opportunities for the legal profession and the justice system. This Comprehensive research delves into the multifaceted impact of artificial intelligence on legal systems, focusing on its transformative potential and implications. Through an extensive analysis of the integration of artificial intelligence technologies, including natural language processing, machine learning, and predictive analytics, the study illuminates the profound improvements in legal research, decision-making processes, and case management, emphasizing the unprecedented efficiency and accessibility that artificial intelligence offers within the legal domain. Furthermore, the research critically examines the ethical and societal challenges stemming from artificial intelligence integration, including concerns related to data privacy, algorithmic bias, and the accountability of artificial intelligence-driven legal solutions. By scrutinizing the existing regulatory frameworks governing artificial intelligence implementation, the study underscores the necessity of responsible and ethical artificial intelligence integration, advocating for transparency, fairness, and equitable practices in the legal profession. The findings contribute to the ongoing discourse on the ethical implications and effective management of artificial intelligence integration in legal systems, providing valuable insights and recommendations for stakeholders and policymakers to navigate the complexities and ensure the responsible adoption of artificial intelligence technologies within the legal sphere

Artificial intelligence as a new answer to old challenges in maternal-fetal medicine and obstetrics.

Following the latest trends in the development of artificial intelligence (AI), the possibility of processing an immense amount of data has created a breakthrough in the medical field. Practitioners can now utilize AI tools to advance diagnostic protocols and improve patient care. The aim of this article is to present the importance and modalities of AI in maternal-fetal medicine and obstetrics and its usefulness in daily clinical work and decision-making process. A comprehensive literature review was performed by searching PubMed for articles published from inception up until August 2023, including the search terms "artificial intelligence in obstetrics", "maternal-fetal medicine", and "machine learning" combined through Boolean operators. In addition, references lists of identified articles were further reviewed for inclusion. According to recent research, AI has demonstrated remarkable potential in improving the accuracy and timeliness of diagnoses in maternal-fetal medicine and obstetrics, e.g., advancing perinatal ultrasound technique, monitoring fetal heart rate during labor, or predicting mode of delivery. The combination of AI and obstetric ultrasound can help optimize fetal ultrasound assessment by reducing examination time and improving diagnostic accuracy while reducing physician workload. The integration of AI in maternal-fetal medicine and obstetrics has the potential to significantly improve patient outcomes, enhance healthcare efficiency, and individualized care plans. As technology evolves, AI algorithms are likely to become even more sophisticated. However, the successful implementation of AI in maternal-fetal medicine and obstetrics needs to address challenges related to interpretability and reliability.

Unlocking Tomorrow’s Landscape: How AI Will Reshape Customer Journeys (B2B &amp; B2C)

Businesses must ask themselves this question as we approach a revolutionary era: how will artificial intelligence (AI) change the B2B and B2C customer journey landscapes? This capstone study explores and covers the journey of artificial intelligence (AI), it’s impact on the business, the new opportunities along with challenges and the future predictions following the literature reviews, case studies, and expert insights. The research unveils a wide variety of possibilities waiting to be unlocked. This research focuses on the transformative potential of artificial intelligence (AI) for businesses and customers alike. It serves as a valuable resource for business leaders, marketing and sales professionals, customer service teams to managing artificial intelligence (AI) adoption plans and optimizing customer journey opportunities. Using AI-powered tools for personalized engagement and data-driven decisions. Using AI to improve productivity and customize assistance. Includes key Findings like Personalized Experiences, Data-Driven Decisions Automation &amp; Efficiency. AI-powered chatbots offer seamless B2B customer service, while B2C brands use recommendation engines to provide hyper-personalized product suggestions. AI streamlines B2B workflows, boosting productivity. Beyond these, the research identifies: Opportunities: How AI increases customer engagement to a greater extent, unlocking brand loyalty and advocacy. Areas to integration of AI in business regular process. Challenges: What could be the Ethical considerations and potential job displacement carefully.

Integration of artificial intelligence with medical diagnostic sonography

AbstractRapid changes in artificial intelligence (AI) have already impacted the medical field. While the use of AI to assist medical diagnosis has been documented, AI is continually expanding within medical applications. AI applications in sonography and their effect on ultrasound examinations and sonographers are still indeterminate. Six papers were reviewed to investigate AI applications and effects within the sonography field. These papers provided results on a range of ultrasound applications including breast, obstetric, skin lesions, carotid, blood flow and cardiac ultrasound imaging when combined with AI. In this narrative review, the application of AI demonstrated that accuracy and speed of clinical diagnosis can be improved. These six aspects of ultrasound imaging combined with AI demonstrated the potential to assist the operator and clinicians with a diagnosis in various applications and settings. Additionally, AI can be beneficial to telehealth applications for rural and remote areas where healthcare access can be limited. These changes are opportunities to assist with medical care to provide benefits to patients, sonographers and clinicians as AI transitions to a positive integration within many aspects of clinical care.

Digital applications for diet monitoring, planning, and precision nutrition for citizens and professionals: a state of the art.

The objective of this review was to critically examine existing digital applications, tailored for use by citizens and professionals, to provide diet monitoring, diet planning, and precision nutrition. We sought to identify the strengths and weaknesses of such digital applications, while exploring their potential contributions to enhancing public health, and discussed potential developmental pathways. Nutrition is a critical aspect of maintaining good health, with an unhealthy diet being one of the primary risk factors for chronic diseases, such as obesity, diabetes, and cardiovascular disease. Tracking and monitoring one's diet has been shown to help improve health and weight management. However, this task can be complex and time-consuming, often leading to frustration and a lack of adherence to dietary recommendations. Digital applications for diet monitoring, diet generation, and precision nutrition offer the promise of better health outcomes. Data on current nutrition-based digital tools was collected from pertinent literature and software providers. These digital tools have been designed for particular user groups: citizens, nutritionists, and physicians and researchers employing genetics and epigenetics tools. The applications were evaluated in terms of their key functionalities, strengths, and limitations. The analysis primarily concentrated on artificial intelligence algorithms and devices intended to streamline the collection and organization of nutrition data. Furthermore, an exploration was conducted of potential future advancements in this field. Digital applications designed for the use of citizens allow diet self-monitoring, and they can be an effective tool for weight and diabetes management, while digital precision nutrition solutions for professionals can provide scalability, personalized recommendations for patients, and a means of providing ongoing diet support. The limitations in using these digital applications include data accuracy, accessibility, and affordability, and further research and development are required. The integration of artificial intelligence, machine learning, and blockchain technology holds promise for improving the performance, security, and privacy of digital precision nutrition interventions. Multidisciplinarity is crucial for evidence-based and accessible solutions. Digital applications for diet monitoring and precision nutrition have the potential to revolutionize nutrition and health. These tools can make it easier for individuals to control their diets, help nutritionists provide better care, and enable physicians to offer personalized treatment.

The Impact of Artificial Intelligence on Job Loss: Risks for Governments

The rapid advancement of Artificial Intelligence (AI) technologies presents both opportunities and challenges for labor markets globally. By adopting a qualitative methodology, this study analyses the impact of AI integration in jobs on the labor market and the subsequent policy responses by governments. The study specifies the industries and jobs that are highly susceptible to AI-driven automation, particularly those characterized by routine and repetitive tasks. The study also acknowledges the potential for AI to create new job categories. Key findings suggest that while AI poses a significant risk of job displacement in several sectors, it also offers opportunities for economic growth, opening of new jobs and innovation. Moreover, the study highlights various government strategies employed globally, including up-skilling and re-skilling initiatives, strengthening social protection systems, and fostering AI-human collaboration. These strategies aim to mitigate the adverse impacts of AI on employment and ensure a balanced transition to AI-integrated economies. Based on these strategies, the paper suggests some recommendations for governments to address AI-driven job losses, emphasizing the importance of multidisciplinary education, ethical AI use, inclusive growth strategies, and international cooperation.

Regulating Algorithmic Assemblages: Exploring Beyond Corporate AI Ethics

The rapid advancement of artificial intelligence (AI) systems, fueled by extensive research and development investments, has ushered in a new era where AI permeates decision-making processes across various sectors. This proliferation is largely attributed to the availability of vast digital datasets, particularly in machine learning, enabling AI systems to discern intricate correlations and furnish valuable insights from data on human behavior and other phenomena. However, the widespread integration of AI into private and public domains has raised concerns regarding the neutrality and objectivity of automated decision-making processes. Such systems, despite their technological sophistication, are not immune to biases and ethical dilemmas inherent in human judgments. Consequently, there is a growing call for regulatory oversight to ensure transparency and accountability in AI deployment, akin to traditional regulatory frameworks governing analogous processes. This paper critically examines the implications and ripple effects of incorporating AI into existing social systems from an 'AI ethics' standpoint. It questions the adequacy of self-policing mechanisms advocated by corporate entities, highlighting inherent limitations in corporate social responsibility paradigms. Additionally, it scrutinizes well-intentioned regulatory initiatives, such as the EU AI ethics initiative, which may overlook broader societal impacts while prioritizing the desirability of AI applications. The discussion underscores the necessity of adopting a holistic approach that transcends individual and group rights considerations to address the profound societal implications of AI, encapsulated by the concept of 'algorithmic assemblage'.

Artificial intelligence in epilepsy - applications and pathways to the clinic.

Artificial intelligence (AI) is rapidly transforming health care, and its applications in epilepsy have increased exponentially over the past decade. Integration of AI into epilepsy management promises to revolutionize the diagnosis and treatment of this complex disorder. However, translation of AI into neurology clinical practice has not yet been successful, emphasizing the need to consider progress to date and assess challenges and limitations of AI. In this Review, we provide an overview of AI applications that have been developed in epilepsy using a variety of data modalities: neuroimaging, electroencephalography, electronic health records, medical devices and multimodal data integration. For each, we consider potential applications, including seizure detection and prediction, seizure lateralization, localization of the seizure-onset zone and assessment for surgical or neurostimulation interventions, and review the performance of AI tools developed to date. We also discuss methodological considerations and challenges that must be addressed to successfully integrate AI into clinical practice. Our goal is to provide an overview of the current state of the field and provide guidance for leveraging AI in future to improve management of epilepsy.

Enhancing ESL Education with AI: Innovations in Teaching Methods and Au-thentic Materials

This article explores the integration of Artificial Intelligence (AI) in English as a Sec-ond Language (ESL) education, focusing on innovative teaching methods and authen-tic materials. AI-powered gamification is discussed, highlighting the use of AI tools like ChatGPT to enhance the gamification of key terms and concepts in ESL educa-tion. The interactive and enjoyable nature of gamified learning, combined with AI-driven content curation, deepens student understanding, critical thinking skills, and retention. By personalizing the learning experience, AI tools cater to individual stu-dent needs, allowing for flexibility in difficulty and fostering a love for the subject. Reflection and self-assessment play a crucial role in solidifying knowledge and pro-moting deeper learning. The article also discusses the importance of authentic materials in ESL education, emphasizing the use of AI-driven content curation to select and organize relevant and meaningful language content. AI-powered tools can analyze vast amounts of data, un-derstand patterns, and make accurate predictions, enhancing the user experience and delivering content that is most relevant and valuable to each individual student. The integration of AI in ESL education offers numerous benefits, including increased engagement, improved retention and application of key terms and concepts, personal-ized learning experiences, and enhanced critical thinking skills. By embracing AI-powered gamification and content curation, educators can create dynamic, interactive, and engaging learning experiences that foster a love for language learning and pro-mote deeper understanding.

Gravitating towards Artificial Intelligence on Anti-Money Laundering A PRISMA Based Systematic Review

In Bangladesh, like many other countries, the implementation of Anti-Money Laundering (AML) measures plays a crucial role in the fight against financial offences and the maintenance of the financial system's integrity. The integration of Artificial Intelligence (AI) is one of the technologies for AML that has emerged as a promising approach to enhance the detection and prevention of financial crimes. The researcher aims to investigate the current state of AML, the impact of AI, the roles involved, and the legislation to achieve economic stability for the transformation of Bangladesh into a smart nation by 2041. In this systematic review study, the PRISMA framework is utilized to provide a comprehensive analysis of existing literature on the application of AI in AML, with a focus on risks, laws, and implications, particularly within the context of Bangladesh. The review synthesizes findings from diverse sources (Web of Sciences, Scopus, PubMed, and DOAJ indexing articles and reports) including academic research, industry reports, and regulatory documents, to assess the effectiveness, challenges, and ethical considerations associated with AI-driven AML solutions. Through a structured search strategy and rigorous selection criteria, the researchers discovered a total of 299 research papers, consisting of 181 latest studies and 117 previous relevant studies identified and analyzed to elucidate key trends, methodologies, and gaps in the current landscape of AI-enabled AML practices. The review also offers insights and recommendations for policymakers, practitioners, and researchers interested in leveraging AI technologies to combat money laundering effectively while navigating regulatory frameworks and safeguarding privacy rights. By critically examining the intersection of AI, AML, and legal frameworks, this study contributes to the ongoing discourse on the role of technology in addressing financial crime challenges in Bangladesh and beyond.