Emergence Of Artificial Intelligence Research Articles

Embedded Low Power Blockchain Traceability Solution for University Classroom Attendance

The emergence of artificial intelligence and decentralized applications has made it possible to set up efficient systems in terms of traceability. The combined technologies are used in several fields such as fintech, industry 4.0, smart agriculture, etc. Their application can also impact educational and academic fields. There is growing interest in leveraging blockchain technology to securely store and retrieve student records. This paper proposes a new smart system that uses deep learning and blockchain technologies to store and manage student attendance. It relies on an embedded platform based on a camera and a Raspberry PI platform that uses artificial intelligence for face recognition and on the blockchain for secure data storage. Evaluating the resulting model on the Labeled Faces in the Wild (LFW) benchmark yields an impressive accuracy rate of 0.9938 with a standard deviation of 0.00272. Moreover, the proposed system provides a complete and accurate record of the entire student learning process, and, thus, reduces the risk of falsified educational records. It also provides potential benefits to future employers by giving them access to large amounts of verified and systematically accumulated data, allowing them to identify and hire qualified students.

Variational Auto encoders for Improved Breast Cancer Classification

Among women breast cancer is the second leading cause of cancer. Emergence of Artificial Intelligence(AI) in the medical care leads to good survival rate by diagnosing and effective prognosis of the breast cancer patients. Scientific findings show that supervised deep learning model is highly dependent on the size of the training set, which must be manually labeled by experienced radiologists. The freely available biomedical imaging dataset are small in size mostly. In addition, obtaining large medical image files is difficult due to privacy and legal reasons. Thus the deep learning models tend to overfit and fail to provide a general result. Also the recent studies indicate that early detection of breast cancer (BC) is crucial to achieve favorable treatment results and reduce associated mortality. Data augmentation is the most widely used approach to address the aforementioned problem. Our proposed architecture consist of the pectoral muscle removal of the dataset, then the variational auto encoders used for data augmentation and then the U-Net and its varients used for breast cancer classification. The dice similarity score obtained for the model after the pectoral muscle segmentation is 97.53% and the classification rate accuracy is 98.5% by use of the Attention Residual U-Net than the other two models.

Comparison Between Threshold Method and Artificial Intelligence Approaches for Early Warning of Respiratory Infectious Diseases - Weifang City, Shandong Province, China, 2020-2023.

Respiratory infectious diseases, such as influenza and coronavirus disease 2019 (COVID-19), present significant global public health challenges. The emergence of artificial intelligence (AI) and big data offers opportunities to improve traditional disease surveillance and early warning systems. The study analyzed data from January 2020 to May 2023, comprising influenza-like illness (ILI) statistics, Baidu index, and clinical data from Weifang. Three methodologies were evaluated: the adaptive dynamic threshold method (ADTM) for dynamic threshold adjustments, the machine learning supervised method (MLSM), and the machine learning unsupervised method (MLUM) utilizing anomaly detection. The comparison focused on sensitivity, specificity, timeliness, and warning consistency. ADTM issued 37 warnings with a sensitivity of 71% and a specificity of 85%. MLSM generated 35 warnings, with a sensitivity of 82% and a specificity of 87%. MLUM produced 63 warnings with a sensitivity of 100% and specificity of 80%. The initial warnings from ADTM and MLUM preceded those from MLSM by five days. The Kappa coefficient indicated moderate agreement between the methods, with values ranging from 0.52 to 0.62 (P<0.05). The study explores the comparison between traditional methods and two machine learning approaches for early warning systems. It emphasizes the validation of machine learning's reliability and underscores the unique advantages of each method. Furthermore, it stresses the significance of integrating machine learning models with various data sources to enhance public health preparedness and response, alongside acknowledging limitations and the need for broader validation.

ChatGPT, Metaverses and the Future of Transdisciplinary Communication

The emergence of artificial intelligence (AI) and virtual reality (VR) technologies has propelled human-machine interaction and immersive experiences to unprecedented levels. Current discussions revolve around Trans-disciplinarity, transdisciplinary communication, metaverses2, and a paradigm shift ushered in by ChatGPT, an intelligent conversational agent powered by OpenAI’s GPT-3.5 architecture. Its integration into VR and metaverses for education and transdisciplinary communication is also being explored. The objective is to discuss and reframe ethical issues arising within the global discourse surrounding technology adaptation, and the innate promises, potential, and pitfalls. By examining the technical and ethical implications of such a potential integration, the paper aims to uncover the broader contexts of these advancements. A deeper understanding of the challenges ahead can be achieved by critically examining the risks and opportunities presented by ChatGPT, virtual realities, and metaverses. The paper emphasizes the need for initiative-taking measures to guide the development and deployment of these transformative technologies. Furthermore, the intersection of AI, VR, metaverses, education, transdisciplinarity, and transdisciplinary communication will be investigated and examined.

ArcaNN: automated enhanced sampling generation of training sets for chemically reactive machine learning interatomic potentials

The emergence of artificial intelligence is profoundly impacting computational chemistry, particularly through machine-learning interatomic potentials (MLIPs). Unlike traditional potential energy surface representations, MLIPs surpass the conventional computational scaling limitations by...

ИССЛЕДОВАНИЕ РАЗВИТИЯ ИННОВАЦИОННЫХ И ТВОРЧЕСКИХ СПОСОБНОСТЕЙ СТУДЕНТОВ С ТОЧКИ ЗРЕНИЯ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА И ОБРАЗОВАНИЯ

The ability to innovate and create is a requirement for the basic abilities of talents in the 21st century, which has also become an important content of teaching students in the framework of modern education. The emergence of artificial intelligence, whether in terms of innovation and creation or potential intelligence, has shown us a lot of the impossible. It provided several new ideas for the development of innovation and creativity of students. In the new conditions for the development of talents in the 21st century, it is necessary to fully use the role of artificial intelligence. This article briefly discusses the advantages and characteristics of combining artificial intelligence and education, as well as how to use artificial intelligence and play its role in education to improve students’ innovative and creative abilities.

Ethical Dilemmas in Using AI for Academic Writing and an Example Framework for Peer Review in Nephrology Academia: A Narrative Review.

The emergence of artificial intelligence (AI) has greatly propelled progress across various sectors including the field of nephrology academia. However, this advancement has also given rise to ethical challenges, notably in scholarly writing. AI's capacity to automate labor-intensive tasks like literature reviews and data analysis has created opportunities for unethical practices, with scholars incorporating AI-generated text into their manuscripts, potentially undermining academic integrity. This situation gives rise to a range of ethical dilemmas that not only question the authenticity of contemporary academic endeavors but also challenge the credibility of the peer-review process and the integrity of editorial oversight. Instances of this misconduct are highlighted, spanning from lesser-known journals to reputable ones, and even infiltrating graduate theses and grant applications. This subtle AI intrusion hints at a systemic vulnerability within the academic publishing domain, exacerbated by the publish-or-perish mentality. The solutions aimed at mitigating the unethical employment of AI in academia include the adoption of sophisticated AI-driven plagiarism detection systems, a robust augmentation of the peer-review process with an "AI scrutiny" phase, comprehensive training for academics on ethical AI usage, and the promotion of a culture of transparency that acknowledges AI's role in research. This review underscores the pressing need for collaborative efforts among academic nephrology institutions to foster an environment of ethical AI application, thus preserving the esteemed academic integrity in the face of rapid technological advancements. It also makes a plea for rigorous research to assess the extent of AI's involvement in the academic literature, evaluate the effectiveness of AI-enhanced plagiarism detection tools, and understand the long-term consequences of AI utilization on academic integrity. An example framework has been proposed to outline a comprehensive approach to integrating AI into Nephrology academic writing and peer review. Using proactive initiatives and rigorous evaluations, a harmonious environment that harnesses AI's capabilities while upholding stringent academic standards can be envisioned.

EMPLOYEE UNDERSTANDING OF ARTIFICIAL INTELLIGENCE: A PRELIMINARY STUDY IN INDONESIA FINANCIAL SECTOR INDUSTRY

The emergence of artificial intelligence (AI) in the financial services industry is driven by increased demand for financial regulation, the need for profitability, and competition between companies. Despite the heightened importance of this new technology, there has been a gap in knowledge regarding the use of AI in financial services. This study aims to provide preliminary research to explore the level of understanding of middle level managers in the context of artificial intelligence usage in marketing activities. The sample is 11 managers from banking, fintech and other financial sectors. The findings provide guidance for organizations in terms of usage and knowledge of AI in the sector.

Economic Evaluations and Equity in the Use of Artificial Intelligence in Imaging Exams for Medical Diagnosis in People With Skin, Neurological, and Pulmonary Diseases: Protocol for a Systematic Review.

Traditional health care systems face long-standing challenges, including patient diversity, geographical disparities, and financial constraints. The emergence of artificial intelligence (AI) in health care offers solutions to these challenges. AI, a multidisciplinary field, enhances clinical decision-making. However, imbalanced AI models may enhance health disparities. This systematic review aims to investigate the economic performance and equity impact of AI in diagnostic imaging for skin, neurological, and pulmonary diseases. The research question is "To what extent does the use of AI in imaging exams for diagnosing skin, neurological, and pulmonary diseases result in improved economic outcomes, and does it promote equity in health care systems?" The study is a systematic review of economic and equity evaluations following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and CHEERS (Consolidated Health Economic Evaluation Reporting Standards) guidelines. Eligibility criteria include articles reporting on economic evaluations or equity considerations related to AI-based diagnostic imaging for specified diseases. Data will be collected from PubMed, Embase, Scopus, Web of Science, and reference lists. Data quality and transferability will be assessed according to CHEC (Consensus on Health Economic Criteria), EPHPP (Effective Public Health Practice Project), and Welte checklists. This systematic review began in March 2023. The literature search identified 9,526 publications and, after full-text screening, 9 publications were included in the study. We plan to submit a manuscript to a peer-reviewed journal once it is finalized, with an expected completion date in January 2024. AI in diagnostic imaging offers potential benefits but also raises concerns about equity and economic impact. Bias in algorithms and disparities in access may hinder equitable outcomes. Evaluating the economic viability of AI applications is essential for resource allocation and affordability. Policy makers and health care stakeholders can benefit from this review's insights to make informed decisions. Limitations, including study variability and publication bias, will be considered in the analysis. This systematic review will provide valuable insights into the economic and equity implications of AI in diagnostic imaging. It aims to inform evidence-based decision-making and contribute to more efficient and equitable health care systems. DERR1-10.2196/48544.

Enhanced rendering-based approach for improved quality of instance segmentation in detecting green gram (Vigna Rediata) pods

The emergence of Artificial Intelligence, deep learning, and current computer vision algorithms are the main contributors to innovations in the agricultural domain. The most recent detection algorithms capable of giving real-time detections at the edge nodes tackle most agricultural problems, such as disease, pest or insect detections, and maturity level detection of crops (fruits and vegetables). Modern harvesters and fruit-picking robots rely heavily on the detection capability of the algorithms used. Various detection algorithms have been proposed and used in literature, having good performance in terms of mean average precision. Still, the current agricultural systems require not only high mean average accuracy but also algorithms should have high inference speeds. The research proposes a Detectron2-based framework with PointRend (Point-based Rendering), capable of providing enhanced, high-quality pixel-level instance segmentation in identifying and detecting green gram pods or Mung Bean (Vigna Radiata) in natural field conditions rendering crisp and smooth boundaries for accurately locating the green gram pods. The results indicate that the proposed framework outperforms the famous Mask R-CNN model to obtain higher mean average precision and improved quality of detections.

Performance analysis of conventional and AI-based variant callers using short and long reads

BackgroundThe accurate detection of variants is essential for genomics-based studies. Currently, there are various tools designed to detect genomic variants, however, it has always been a challenge to decide which tool to use, especially when various major genome projects have chosen to use different tools. Thus far, most of the existing tools were mainly developed to work on short-read data (i.e., Illumina); however, other sequencing technologies (e.g. PacBio, and Oxford Nanopore) have recently shown that they can also be used for variant calling. In addition, with the emergence of artificial intelligence (AI)-based variant calling tools, there is a pressing need to compare these tools in terms of efficiency, accuracy, computational power, and ease of use.ResultsIn this study, we evaluated five of the most widely used conventional and AI-based variant calling tools (BCFTools, GATK4, Platypus, DNAscope, and DeepVariant) in terms of accuracy and computational cost using both short-read and long-read data derived from three different sequencing technologies (Illumina, PacBio HiFi, and ONT) for the same set of samples from the Genome In A Bottle project. The analysis showed that AI-based variant calling tools supersede conventional ones for calling SNVs and INDELs using both long and short reads in most aspects. In addition, we demonstrate the advantages and drawbacks of each tool while ranking them in each aspect of these comparisons.ConclusionThis study provides best practices for variant calling using AI-based and conventional variant callers with different types of sequencing data.

General Cultural Competence of a Future Teacher in a Changing World

The purpose of the study. The paper analyzes the content оf the concept of “general cultural competence of a future teacher” and its dialectical development in a changing world under the influence of various factors: the new technological order; the complexity and diversity of various social environments; the risks of environmental, energy and epidemic crises; the emergence of artificial intelligence and learning neural networks; and the large consequences of small (at first glance) events in public life. The authors of this paper identified the inseparable connection of the general cultural competence of a future teacher with the general culture of human society. The work culture (professional activity) of a teacher is understood as an intellectual, socially oriented creative activity in the field of education, which has a spiritual ground and value sense. Such professional activity includes imperatives of personal and professional development, learning new things, motivation to participate in the life and upbringing of the subjects of their work – students. The trend of pragmatism in the development of educational programs of higher education, underestimation of the humanistic function of pedagogical universities and the need to form longitudinal professional and life goals of graduates is actualized. Materials and methods. The study is based on the analysis of scientific publications in the field of competence-based, culturological and learner-centered approaches in pedagogy. The content of publications was compared, and the frequency of key terms in these publications was analyzed using the online service “Semantic text analysis online, seo text analysis”.The results of the study. The results prove that the phenomenon of culture arises in the process of human coevolution. In this study, the structure and content of culture is seen as a sphere with three poles (religion, art, science). In the process of historical increase in the volume of culture, these poles could have changed mutual distances, demonstrating a prevailing influence of each pole on the volume of the culture sphere. Furthermore, the series of categories of “cult – culture – general cultural competence of a future teacher” was analyzed. In the context of the competency-based approach, an analysis of the content of the concept “general cultural competence of a future teacher” is considered. It has been suggested that leadership competence can be considered as part of the general cultural competence of a future teacher, determining the ability to independently organize his or her own professional and life trajectory from the position of a leader, creating an atmosphere of cooperation in the pedagogical, design or research process. The leader's worldview is seen to be an integral part of the general culture of a person, being realized in the normative (regulatory) and evaluative (axiological) functions of the culture, as well as in the socialization function together with a continuous desire for self-improvement in the profession and life.Conclusion. The findings of the study demonstrate that teachers, as carriers and subjects of culture, need to develop their general cultural competence to navigate in and adapt to a changing world. The considered competences are crucial in the modern conditions and should be directly or indirectly introduced into the future Federal State Educational Standards of Higher Education in the Russian Federation.

SAFETY CONTROL SYSTEMS FOR ETHYLENE PRODUCTION

Ethylene production is a cornerstone of the petrochemical industry, with a global demand that continues to rise. Ensuring efficient, safe, and environmentally responsible ethylene production processes is paramount. Ethylene production control systems, encompassing a range of technologies and strategies, play a pivotal role in achieving these objectives. This abstract provides a concise overview of key aspects of ethylene production control systems, their challenges, and their critical importance. Ethylene production control systems serve as the backbone of modern ethylene plants, orchestrating complex operations to meet production targets while maintaining product quality. These systems integrate advanced technologies such as distributed control systems (DCS), safety instrumented systems (SIS), and process optimization tools to manage variables like temperature, pressure, flow rates, and feedstock composition. Efficiency and yield optimization are central objectives in ethylene production control. Control strategies are designed to maximize product output while minimizing energy consumption and raw material wastage. Additionally, safety control systems are a crucial component, mitigating risks associated with the highly flammable nature of ethylene and ensuring the well-being of personnel and environmental protection. Challenges in ethylene production control systems include managing the variability in feedstock quality, adapting to changing market demands, and adhering to stringent environmental regulations. As feedstock composition can fluctuate, control systems must continually adjust to maintain product quality and consistency. The emergence of Artificial Intelligence (AI) has brought new dimensions to ethylene production control. AI-driven predictive maintenance, anomaly detection, and process optimization are being explored to enhance operational efficiency and reduce downtime. In summary, ethylene production control systems are pivotal in sustaining the global supply of ethylene, a vital chemical compound. Their roles extend beyond production to safety and environmental compliance, making them indispensable components in the petrochemical industry's pursuit of efficiency and sustainability. To meet growing demands and address evolving challenges, the integration of advanced technologies, such as AI, holds promise for the future of ethylene production control systems. A strong safety culture is the backbone of safety control in ethylene production. This involves fostering a work environment where safety is paramount, and all personnel are equipped with the knowledge, awareness, and commitment to safety objectives. Clear communication and continuous training ensure that safety becomes an integral part of the organizational DNA. These systems represent a synergy of hardware, software, and operational protocols meticulously designed to monitor, detect, and respond to anomalous conditions, with a primary objective of averting incidents that could culminate in equipment damage, environmental harm, or harm to personnel. Keywords: Ethylene Production, Control Systems, Petrochemical Industry, Efficiency, Safety, Environmental Responsibility.

The Impact of Artificial Intelligence on the Future of Radiographer Career in Saudi Arabia

The emergence of artificial intelligence (AI) technologies in the healthcare imaging domain has heightened discourse regarding the responsibilities and role of radiologists, thereby bringing this issue to the fore. On the other hand, the extent to which this kind of equipment will alter the radiographer's job has not been much discussed. To examine the potential effects of AI on the radiography job, this study assesses current processes and cross-maps potential areas for automation, such as procedure organizing, picture capture, and processing images. We also draw attention to the advantages that AI offers, such as improved patient care, greater technology skills, elevated cross-modality learning and teamwork, and an extension of radiographer duties into AI-supported picture auditing and reporting roles. For the purpose of incorporating AI systems into medical imaging, radiologists are crucial because they serve as a liaison between people and technological advances. Studies have looked at the knowledge, beliefs, and attitudes of Saudi Arabian radiology professionals toward AI to investigate how radiologists react toward AI in radiology and to find out their opinions about AI in the future of radiology.

Trust in AI-augmented design: Applying structural equation modeling to AI-augmented design acceptance

With the emergence of Artificial Intelligence (AI) 2.0, computers are now equipped with new creative capabilities and are playing an increasingly significant role in design. The use of AI augmentation has the potential to enhance design performance, however, there is limited research on the acceptance of AI-augmented design. The research gap under consideration in this study is addressed by presenting an acceptance model designed for AI-augmented design. This model integrates a range of variables including perceived privacy risk, enjoyment, perceived value, perceived usefulness, perceived ease of use, perceived behavioral control, social influence, and behavioral intention. The proposed model was validated through a questionnaire survey of 249 designers in China.The results reveal that enjoyment, perceived value, perceived ease of use, perceived behavioral control, and social influence have a significant positive impact on users' intention to use AI-augmented design, while perceived privacy risk has a significant negative impact. Perceived value was found to mediate the relationship between enjoyment and behavioral intention, while perceived behavioral control play a mediation role in the relationship between social influence and behavioral intention.In conclusion, this study highlights the variables that influence the acceptance of AI-augmented design and provides valuable insights into the potential benefits and drawbacks of integrating AI technologies in design. The proposed acceptance model serves as a framework for future research in this area and can guide the development of more user-friendly and effective AI-augmented design tools and technologies.

Building Resilience in the European Union in Times of Polycrisis and Challenges in the Cognitive Domain

This aim of this publication is to draw attention to the need to build resilience in the societies and Member States of the European Union in times of “polycrisis” and challenges in the cognitive domain. Following an analysis of global risks and hybrid threats, as well as the emergence of artificial intelligence in today's communication and security environment, certain recommendations and conclusions are drawn to enable the EU to exploit its potential to be a “multidimensional power”, overcoming non-kinetic challenges to vulnerabilities in democratic societies, citizens' minds and emotions. In this research, a range of political science analysis methods were employed. In addition, the “mind maps” technique of the British psychologist Tony Buzan was implemented by enabling the synthesis of ideas through visual representations. On the eve of the European elections in 2024, the publication draws key conclusions and recommendations for overcoming the challenges of desinformation, perception management and psychological manipulation, which undermine trust in democratic institutions and prevent decision-makers from countering them. These include: building resilience through education, media literacy, critical thinking, digital competencies, mental toughness; the ability to think not only fast but also slow; the application of exponential design to strategic thinking; multilateralism and close cooperation with NATO.

AI SENTRY: REINVENTING CYBERSECURITY THROUGH INTELLIGENT THREAT DETECTION

In recent years, the escalating complexity and frequency of cyber threats have presented a formidable challenge to traditional cybersecurity measures. The emergence of artificial intelligence (AI) technologies has revolutionized the landscape, offering a promising solution to fortify defenses against evolving threats. This paper introduces AI Sentry, an innovative approach to cybersecurity that leverages the power of AI for intelligent threat detection and prevention. AI Sentry embodies a paradigm shift in cybersecurity, integrating machine learning, neural networks, and advanced algorithms to proactively identify, analyze, and mitigate potential threats in real time. By continuously learning from vast datasets and adapting to new attack vectors, AI Sentry enhances its ability to recognize anomalous patterns and behaviors, thereby thwarting sophisticated cyber assaults. The core strength of AI Sentry lies in its capability to detect anomalies and predict threats with a high degree of accuracy, surpassing the limitations of traditional signature-based systems. Through anomaly detection, behavioral analysis, and contextual understanding, AI Sentry not only identifies known threats but also anticipates zero-day attacks and previously unseen malicious activities. This paper delves into the technical underpinnings of AI Sentry, elucidating its architecture, data processing techniques, machine learning models, and the orchestration of various AI components. Furthermore, it explores the ethical considerations and challenges associated with AI-powered cybersecurity, including issues of privacy, bias mitigation, and transparency in decision-making.

Navigating the Legal and Ethical Terrain of Artificial Intelligence in Enhancing Patient Safety in Nigeria

The emergence of Artificial Intelligence (AI) significantly impacts the understanding of medical errors, minimizes their occurrence, and provides contextual solutions for patient safety in Nigeria.Given the country's expanding population and constrained healthcare resources, the potential significance of AI in enhancing patient safety in Nigeria cannot be overstated. There is a rapid trend to integrate AI into Nigeria's healthcare system, however, this raises concerns about algorithm bias and privacy. This study explores the ethical and legal implications of deploying AI for patient safety in Nigeria and assesses how the existing Nigerian legal frameworks address these concerns. This research shows that although bias and discrimination are generally prohibited by the Constitution of the Federal Republic of Nigeria and other legal instruments, algorithmic bias which arises from the use of AI is not catered for by these laws. In addition, despite current privacy and confidentiality safeguards, AI in healthcare presents unique and novel challenges that the legislation does not yet address. Therefore, developing a new legal and governance structure to address both present and prospective challenges of AI in the health sector is extremely useful.

Applications of remote sensing vis-à-vis machine learning in air quality monitoring and modelling: a review.

Environmental contamination especially air pollution is an exponentially growing menace requiring immediate attention, as it lingers on with the associated risks of health, economic and ecological crisis. The special focus of this study is on the advances in Air Quality (AQ) monitoring using modern sensors, integrated monitoring systems, remote sensing and the usage of Machine Learning (ML), Deep Learning (DL) algorithms, artificial neural networks, recent computational techniques, hybridizing techniques and different platforms available for AQ modelling. The modern world is data-driven, where critical decisions are taken based on the available and accessible data. Today's data analytics is a consequence of the information explosion we have reached. The current research also tends to re-evaluate its scope with data analytics. The emergence of artificial intelligence and machine learning in the research scenario has radically changed the methodologies and approaches of modern research. The aim of this review is to assess the impact of data analytics such as ML/DL frameworks, data integration techniques, advanced statistical modelling, cloud computing platforms and constantly improving optimization algorithms on AQ research. The usage of remote sensing in AQ monitoring along with providing enormous datasets is constantly filling the spatial gaps of ground stations, as the long-term air pollutant dynamics is best captured by the panoramic view of satellites. Remote sensing coupled with the techniques of ML/DL has the most impact in shaping the modern trends in AQ research. Current standing of research in this field, emerging trends and future scope are also discussed.

Tooth Segmentation in Dentistry: A Review

The emergence of artificial intelligence (AI) has marked the past decade as a breakthrough in the advancement of technology. Every industry has embraced AI with great excitement and the field of dental science is no different. Artificial intelligence has several uses in dental and medical science, ranging from fundamental process of gathering a patient’s medical history, using machine learning algorithms to predict dental caries and finally for data processing to extracting the information from the data for diagnosis. This paper reviews the role of tooth segmentation in dentistry. Keywords: Artificial Intelligence, Deep Learning Method, Tooth Segmentation, Watershed, U Net