Use Of Artificial Intelligence Research Articles

EXAMINING THE IMPACT OF ARTIFICIAL INTELLIGENCE IN DENTISTRY: A COMPREHENSIVE SYSTEMATIC REVIEW

Background: Artificial Intelligence (AI) in dentistry has the potential to revolutionize oral healthcare by solving its inherent shortcomings. Aim: To review and evaluate the body of research on artificial intelligence's use in dentistry, with a focus on how it affects treatment planning, diagnosis, and patient care in a range of dental specialties. Methodology: 30 papers encompassing oral diagnosis, surgery, endodontics, prosthodontics, orthodontics, forensic dentistry, radiography, and periodontics are thoroughly examined in this review using PRISMA guidelines. The Cochrane Handbook principles were followed in the evaluation of important variables such as randomization, blinding, withdrawal/dropout rates, sample size estimation, clarity of inclusion/exclusion criteria, examiner reliability testing, pre-specification of outcomes, and bias risk. The Newcastle-Ottawa Scale (NOS) was used in quality assessment to measure bias risk and star ratings. Results: The research highlight improvements in diagnosis, treatment planning, and procedural accuracy, illustrating the revolutionary effects of AI in dentistry. Applications of AI demonstrate its versatility and include automated designs, risk prediction, lesion recognition, and precision in dental operations. There is little chance of bias in randomization, intervention variations, and outcome assessments, according to the methodological evaluation, which shows excellent scientific rigor. Even though a few studies had minor issues including uneven blinding and missing data, these had no appreciable impact on the dependability of the results. Overall, the studies' consistent methodological quality highlights how AI may be relied upon to advance dental research and practice.

Knowledge, Attitude, and Practice (KAP) Regarding the Use of Artificial Intelligence in Hospital Settings in Mardan, Khyber Pakhtunkhwa, Pakistan.

Background Artificial intelligence (AI) is revolutionizing healthcare globally by enhancing diagnostic accuracy, predicting patient outcomes, and enabling personalized treatment plans. However, in low- and middle-income countries (LMICs) like Pakistan, the integration of AI into healthcare is limited due to challenges such as lack of funding, provider resistance, and inadequate training. Despite these barriers, there is growing interest among healthcare providers in understanding and adopting AI technologies to improve professional efficiency. Objective To determine knowledge, attitude, and practice (KAP) regarding the use of artificial intelligence (AI) among doctors in a hospital setting. Method This cross-sectional study was conducted at Mardan Medical Complex, Mardan, from January 2023 to March 2023. A total of 150 doctors from various departments participated by completing a validated questionnaire designed to assess their KAP regarding AI. The questionnaire, consisting of nine close-ended questions, was specifically designed and distributed to participants through social media applications, including WhatsApp (California, USA) and email, to maximize accessibility. It included structured questions rated on Likert scales to quantify the levels of knowledge, attitude, and practice. Participants were also asked about their exposure to AI-related training or professional work. Descriptive analysis was performed to determine the frequency and percentage of responses. Results The mean ± SD of age in this study was 36.95 ± 8.58 years. Male participants were 72.66%, while 27.33% were females. The response to the questions showed that the majority of participants (66%) knew the term AI; however, they were unsure regarding its use in healthcare. The majority of doctors (67.33%) had positive thoughts about the possibility of using AI in health management. Importantly, the majority of participants (72.66%) had never had a chance to do any AI-related work in their professional lives. The assessment of KAP showed that the majority of doctors had a medium level of knowledge (36.66%), a high level of attitude (57.55%), and a low level of practice (65.66%) regarding AI. Conclusions These results conclude that the knowledge regarding AI's use in healthcare is medium and its use in clinical practice is low; however, doctors have a high level of interest in applying AI to improve their professional efficiency.

Unveiling the power of artificial intelligence for image-based diagnosis and treatment in endodontics: An ally or adversary?

Artificial intelligence (AI), a field within computer science, uses algorithms to replicate human intelligence tasks such as pattern recognition, decision-making and problem-solving through complex datasets. In endodontics, AI is transforming diagnosis and treatment by applying deep learning algorithms, notably convolutional neural networks, which mimic human brain function to analyse two-dimensional (2D) and three-dimensional (3D) data. This article provides an overview of AI applications in endodontics, evaluating its use in 2D and 3D imaging and examining its role as a beneficial tool or potential challenge. Through a narrative review, the article explores AI's use in 2D and 3D imaging modalities, discusses their limitations and examines future directions in the field. AI significantly enhances endodontic practice by improving diagnostic accuracy, workflow efficiency, and treatment planning. In 2D imaging, AI excels at detecting periapical lesions on both periapical and panoramic radiographs, surpassing expert radiologists in accuracy, sensitivity and specificity. AI also accurately detects and classifies radiolucent lesions, such as radicular cysts and periapical granulomas, matching the precision of histopathology analysis. In 3D imaging, AI automates the segmentation of fine structures such as pulp chambers and root canals on cone-beam computed tomography scans, thereby supporting personalized treatment planning. However, a significant limitation highlighted in some studies is the reliance on in vitro or ex vivo datasets for training AI models. These datasets do not replicate the complexities of clinical environments, potentially compromising the reliability of AI applications in endodontics. Despite advancements, challenges remain in dataset variability, algorithm generalization, and ethical considerations such as data security and privacy. Addressing these is essential for integrating AI effectively into clinical practice and unlocking its transformative potential in endodontic care. Integrating radiomics with AI shows promise for enhancing diagnostic accuracy and predictive analytics, potentially enabling automated decision support systems to enhance treatment outcomes and patient care. Although AI enhances endodontic capabilities through advanced imaging analyses, addressing current limitations and fostering collaboration between AI developers and dental professionals are essential. These efforts will unlock AI's potential to achieve more predictable and personalized treatment outcomes in endodontics, ultimately benefiting both clinicians and patients alike.

Artificial intelligence in the United Arab Emirates public sector: a systematic literature review

<p><span lang="EN-US">This systematic literature review examines United Arab Emirates (UAE) public sector artificial intelligence (AI) use, impact, and challenges. Using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) protocol, 20 relevant Scopus articles were selected for the study. Data from selected articles were used to analyse AI's use, benefits, and drawbacks in the UAE's public sector. Quality assessment was done throughout the review process. The results showed that AI is being used more in the UAE's public sector to improve efficiency, cost savings, decision-making, and service delivery. The review also found data, privacy, security, technical, infrastructure, AI, and user challenges. Publication bias and the lack of AI studies in the UAE's public sector limit the study. The findings have major implications for policy and practice, emphasising the need for AI strategies and UAE-specific solutions.</span></p>

The Role of Artificial Intelligence in Nutrition Research: A Scoping Review.

Artificial intelligence (AI) refers to computer systems doing tasks that usually need human intelligence. AI is constantly changing and is revolutionizing the healthcare field, including nutrition. This review's purpose is four-fold: (i) to investigate AI's role in nutrition research; (ii) to identify areas in nutrition using AI; (iii) to understand AI's future potential impact; (iv) to investigate possible concerns about AI's use in nutrition research. Eight databases were searched: PubMed, Web of Science, EBSCO, Agricola, Scopus, IEEE Explore, Google Scholar and Cochrane. A total of 1737 articles were retrieved, of which 22 were included in the review. Article screening phases included duplicates elimination, title-abstract selection, full-text review, and quality assessment. The key findings indicated AI's role in nutrition is at a developmental stage, focusing mainly on dietary assessment and less on malnutrition prediction, lifestyle interventions, and diet-related diseases comprehension. Clinical research is needed to determine AI's intervention efficacy. The ethics of AI use, a main concern, remains unresolved and needs to be considered for collateral damage prevention to certain populations. The studies' heterogeneity in this review limited the focus on specific nutritional areas. Future research should prioritize specialized reviews in nutrition and dieting for a deeper understanding of AI's potential in human nutrition.

Generative artificial intelligence: Educational reflections from an analysis of scientific production

Generative Artificial Intelligence (AI-Gen) in education is a challenge for educational administrations due to the benefits and risks of its use. This research seeks to analyse the scientific production of AI-Gen in the educational field. The method used was a bibliometric analysis of descriptive and quantitative documents from the Web of Science database. The VOSviewer statistical programme was used to identify the keyword clusters and to create the network map. The results of this study ascertain that 2023 is the birth of AI-Gen research in education and has since produced a total of 178 papers, where Australia heads the ranking of countries with studies on this subject matter. The ChatGPT phenomenon materialises as a focus of study in the majority of cited research. The study includes seven keyword clusters, where the IA-Gen cluster which is related to terms such as challenges, risks and opportunities are highlighted. Following the discussion, the main conclusion of the study denotes that further research is required on the use of AI-Gen during the teaching-learning process in several educational stages to prevent plagiarism and to obtain a comprehensive understanding of this technology as an educational resource.

The Ethics of Using Artificial Intelligence in Qualitative Research.

Artificial Intelligence (AI) and other large language models are rapidly infiltrating the world of education and educational research. These new technological developments raise questions about use and ethics throughout the world of educational research, particularly for qualitative methods given the philosophical and structural foundations of its associated designs. This paper seeks to interrogate the perceived ethics around the use of AI in qualitative research and draws on survey data from qualitative researchers (n = 101) collected from April-May 2023. Findings indicate that researchers were more apt to embrace the use of AI for transcription purposes, and to a lesser extent for preliminary coding. Researchers from high research productivity (R1) universities were generally less accepting of AI's use in the research process than other researchers.

Patients' perspectives on the use of artificial intelligence and robots in healthcare.

We aimed to evaluate the opinions of individuals aged 18 and above in our country regarding the use of artificial intelligence (AI) and robots in the field of healthcare. The growing population and patient load, coupled with increasing data, can expedite the diagnosis and treatment process for patients through faster, easier, and more accurate interpretation of information. The study encompasses voluntary participants aged 18 and above, who have either undergone surgery in ahospital or have accompanied afamily member during asurgical procedure and possess internet access as well as the capability to participate in online surveys. Atotal of 725 individuals participated in our study 61% (n=442) of respondents expressed trust in the operation of AI and robots in the hospital setting. 64.1% (n=465) of participants expressed trust in AI's contribution to disease diagnosis and laboratory tests. The confidence in AI's use in radiological examinations and its contribution reached 71.6% (n=519). This study demonstrates that the use of AI and robots in healthcare services is accepted by our society and would be appropriate in our society (Tab. 5, Fig. 1, Ref. 24).

Assessing the Efficacy of an AI-Powered Chatbot (ChatGPT) in Providing Information on Orthopedic Surgeries: A Comparative Study With Expert Opinion.

Background The use of artificialintelligence (AI) as a tool for patient care has continued to rapidly expand. The technology has proven its utility in various applications across several specialties in a variety of applications. However, its practicality in orthopedics remains widely unknown. This study seeks to determine if the open-access software Chat Generative Pre-Trained Transformer (ChatGPT) can be a reliable source of data for patients. Questions/purposes This study aims to determine: (1) Is the open-access AIsoftware ChatGPT capable of accurately answering commonly posed patient questions? (2) Will there be a significant difference in agreement among the study experts in the answers generated by ChatGPT? Methods A standard list of questions for six different procedures across six subspecialties is posed to ChatGPT. The procedures chosen were anterior cruciate ligament (ACL) reconstruction, microdiscectomy, total hip arthroplasty (THA), rotator cuff repair, carpal tunnel release, and ankle fracture open reduction and internal fixation. The generated answers are then compared to expert opinion using a Likert scale based on the agreement of the aforementioned experts. Results On a three-point Likert scale with 1 being disagree and 3 being agree, the mean score across all subspecialties is 2.43, indicating at least partial agreement with expert opinion.There was no significant difference in the Likert scale mean across the six subspecialties surveyed (p = 0.177). Conclusions This study shows promise in using ChatGPT as an aid in answering patient questions regarding their surgical procedures. This opens doors for the use of the software by patients for understanding and increased shared decision-making with their surgeons. However, studies with larger participation groups are necessary to ensure accuracy on a larger and broader scale as well as studies involving specific application of AI within surgeon's practice.

The role of artificial intelligence in building an adaptive educational environment

Purpose: of the article is to study the influence of artificial intelligence on the construction of an adaptive educational environment that takes into account the positive and negative impact of artificial intelligence, as the core of digital technologies, on the well-being of participants in the educational process. Methods: the work uses a complex of theoretical methods, including axiomatic, formalization, abstraction, logical analysis, historical retrospection. The most signifi cant practical research methods include statistical, operationalization and evaluation, comparative analysis. Results: the article provides a statistical substantiation of the demand for artifi cial intelligence in the education. The current educational problems are shown, in solving which it is advisable to use artifi cial intelligence. The article describes the trends in the use of artificial intelligence in the Russian education. The interpretation of the adaptive educational environment is given and the possibilities of using artificial intelligence as a tool for its construction are shown. The logical sequence for constructing an adaptive intelligent teaching system is proposed. The analysis of the impact of digital technologies used in hybrid learning (including artifi cial intelligence) on the well-being of participants in the educational process was carried out. The need for a rational, balanced and cautious approach to the use of artifi cial intelligence in education is emphasized, which gives rise to numerous ethical problems, ignoring which can negatively aff ect the values of education. Conclusions and Relevance: in the context of digitalization of the economy and society, the key technology of which is artifi cial intelligence, education is facing technological challenges that force it to adapt to new operating conditions. The penetration of artifi cial intelligence into the field of education is the pattern of scientific and technological progress that cannot be resisted. The positive aspects and threats of using artificial intelligence in education should be critically assessed, and informed decisions should be made on this basis. Considering the specifics of the educational environment in which the foundation of personality is laid, and the need to preserve the values of education as a factor of cultural progress, priority in goal-setting and the meanings of education should remain with a person whose assistant can be artifi cial intelligence.

Model of Artifi cial Intelligence for Forming the Federal Reserve as a Driver for Digitalization of the Economy

The article discusses theoretical issues regarding the personnel reserve, the formation of the "Random Forest" DL model for the selection of applicants for the Russian federal reserve in modern conditions. The relevance of the study lies in the fact that in modern conditions artifi cial intelligence systems based on cognitive modeling, the use of artifi cial intelligence and the formation of digital ecosystems are increasingly being used. The scientific novelty lies in the fact that in the study conducted, a deep learning model DL-model "Random Forest" was formed, which allows the selection of applicants when forming the federal reserve. The practical significance of the study is that the results obtained can be recommended for implementation in practice to provide support for management decisions in the formation of the federal reserve. The cognitive model was developed in the GraphViz environment using a semantic frame network in the form of graphs in the DOT programming language. A questionnaire was developed to identify the qualitative characteristics of applicants, which made it possible to create a "Random Forest" DLmodel dataset. The criterion for the success of the predictive properties of the DL model was the value of the average forecast error (MAE). The proposed DL model uses the best decision tree, which has optimal hyperparameter settings, for example, the depth of the tree is six layers, the number of estimators (trees) in the ensemble is ten. In the experiment, the hyperparameters of the neural network did not change; the input parameters to various trees were selected randomly by the algorithm.

Here, There and Everywhere: On the Responsible Use of Artificial Intelligence (AI) in Management Research and the Peer‐Review Process

AbstractThis editorial introduces and explains the Journal of Management Studies’ (JMS) new policy on artificial intelligence (AI). We reflect on the use of AI in conducting research and generating journal submissions and what this means for the wider JMS community, including our authors, reviewers, editors, and readers. Specifically, we consider how AI‐generated research and text could both assist and augment the publication process, as well as harm it. Consequentially, our policy acknowledges the need for careful oversight regarding the use of AI to assist in the authoring of texts and in data analyses, while also noting the importance of requiring authors to be transparent about how, when and where they have utilized AI in their submissions or underlying research. Additionally, we examine how and in what ways AI's use may be antithetical to the spirit of a quality journal like JMS that values both human voice and research transparency. Our editorial explains why we require author teams to oversee all aspects of AI use within their projects, and to take personal responsibility for accuracy in all aspects of their research. We also explain our prohibition of AI's use in peer‐reviewers’ evaluations of submissions, and regarding editors’ handling of manuscripts.

Innovative Digital Model of the Infl uence of the Key Rate on the Profi tability of the Russian Banking System

The article considers theoretical approaches to modeling the sustainable development of the Russian banking system in the context of innovative transformations and the formation of digital ecosystems. The relevance of the study lies in the fact that in modern conditions, approaches are increasingly being used to ensure the sustainable development of the banking system based on cognitive modeling, the use of artifi cial intelligence and the formation of digital ecosystems. The scientific novelty lies in the fact that in the study, a deep learning model DL-model "Random Forest" was formed, which allows you to get a stable forecast for the net profi t of the banking system, in the conditions of innovative transformations and the formation of digital ecosystems. The practical signifi cance of the study is that the results obtained can be recommended for implementation in practice to provide support for managerial decision-making regarding the sustainable development of the banking system. The cognitive model was developed in the GraphViz environment using a semantic frame network in the form of graphs in the DOT programming language. An analysis of the dynamics of both macroeconomic indicators of the real sector of the economy and the parameters of the development of the banking sector of the Russian Federation was carried out. The criterion for the success of the predictive properties of the DL-model was the value of the average forecast error (MAE). The proposed DL model uses the best decision tree that has optimal hyperparameter settings, for example, the depth of the tree is six layers, the number of estimators (trees) in the ensemble is ten. In the experiment, the hyperparameters of the neural network did not change, the input parameters to various trees were randomly selected by the algorithm. The DL model showed high forecast accuracy.

Advancing cybersecurity and privacy with artificial intelligence: current trends and future research directions.

The rapid escalation of cyber threats necessitates innovative strategies to enhance cybersecurity and privacy measures. Artificial Intelligence (AI) has emerged as a promising tool poised to enhance the effectiveness of cybersecurity strategies by offering advanced capabilities for intrusion detection, malware classification, and privacy preservation. However, this work addresses the significant lack of a comprehensive synthesis of AI's use in cybersecurity and privacy across the vast literature, aiming to identify existing gaps and guide further progress. This study employs the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework for a comprehensive literature review, analyzing over 9,350 publications from 2004 to 2023. Utilizing BERTopic modeling, 14 key themes in AI-driven cybersecurity were identified. Topics were clustered and validated through a combination of algorithmic and expert-driven evaluations, focusing on semantic relationships and coherence scores. AI applications in cybersecurity are concentrated around intrusion detection, malware classification, federated learning in privacy, IoT security, UAV systems and DDoS mitigation. Emerging fields such as adversarial machine learning, blockchain and deep learning are gaining traction. Analysis reveals that AI's adaptability and scalability are critical for addressing evolving threats. Global trends indicate significant contributions from the US, India, UK, and China, highlighting geographical diversity in research priorities. While AI enhances cybersecurity efficacy, challenges such as computational resource demands, adversarial vulnerabilities, and ethical concerns persist. More research in trustworthy AI, standardizing AI-driven methods, legislations for robust privacy protection amongst others is emphasized. The study also highlights key current and future areas of focus, including quantum machine learning, explainable AI, integrating humanized AI and deepfakes.

ИСКУССТВЕННЫЙ ИНТЕЛЛЕКТ: ДРАЙВЕР ЭКОНОМИЧЕСКОГО РАЗВИТИЯ ИЛИ ГЕНЕРАТОР ПРОБЛЕМ И УГРОЗ?

The article examines the problems of the development of artificial intelligence at the pre-sent stage and emphasizes the need to develop scientific research in all areas of intelligence: human, biological and artificial. The purpose of the study is to identify the main problems and limitations of the current level of artificial intelligence development. The relevance of the problem under consid-eration lies in the fact that the work analyzes a set of problems that determine the factors of the development and use of artificial intelligence, since the idea of artificial intelligence arose among people relatively recently and this understanding is rapidly developing. The term “artificial intelli-gence” was proposed by John McCarthy in 1956 at Dortmund College at aconference on the “mecha-nization of intelligence”. Changes in society related to the development of AI have been happening and are happening constantly and, judging by the basis on which AI is developing, will never stop, since its development is completely based on the capabilities of the human mind, and we cannot determine its potential even approximately. Currently, AI is penetrating almost all areas of human activity and will dominate them. The future of AI is limitless and depends only on its creators and users. The transformations that the use of artificial intelligence brings often radically change our daily lives and relationships. The ongoing changes contain both undoubted significant positive ef-fects that greatly facilitate the solution of various problems, as well as pitfalls that manifest them-selves in various aspects: starting from imperfections of applied technical solutions and ending in ethical and social areas, which result not only in current technical, technological, and social prob-lems, but also potentially significant threats that pose the danger of human existence itself. The article shows that there is a need to clarify some concepts of AI, to determine the direction of AI development. Research in the work has shown that developments are conducted mainly in the field of “weak” artificial intelligence. They are mainly focused on commercialization and early implemen-tation of practical results. In this regard, the authors note the need to strengthen fundamental and exploratory research on the properties of intelligence and the creation of scientific foundations for the practical implementation of “strong” AI systems. In the long run, AI, as a tool for optimizing and accelerating various processes, has a future that promises its maximum possible use and develop-ment in attempts to achieve, and eventually try to surpass human intelligence. In this regard, it is necessary to provide for possible positive and negative consequences, to determine the boundaries and direction of the use of artificialintelligence so that people do not become victims of unsuccess-ful developments, and humanity can benefit from this invention.

Cognitive Modeling of the Sustainable Development of the Banking System under the Conditions of Innovative Transformations

The article considers theoretical approaches to modeling the sustainable development of the Russian banking system in the context of innovative transformations and the formation of digital ecosystems. The relevance of the study lies in the fact that in modern conditions, approaches are increasingly being used to ensure the sustainable development of the banking system based on cognitive modeling, the use of artifi cial intelligence. The scientifi c novelty lies in the fact that in the study, a deep learning model DL-model "Random Forest" was formed, which allows you to get a stable forecast for the net profi t of the banking system, in the conditions of innovative transformations. The practical signifi cance of the study is that the results obtained can be recommended for implementation in practice to provide support for managerial decision-making regarding the sustainable development of the banking system. The cognitive model was developed in the GraphViz environment using a semantic frame network in the form of graphs in the DOT programming language. An analysis of the dynamics of both macroeconomic indicators of the real sector of the economy and the parameters of the development of the banking sector of the Russian Federation was carried out. The criterion for the success of the predictive properties of the DL-model was the value of the average forecast error (MAE). The proposed DL model uses the best decision tree that has optimal hyperparameter settings, for example, the depth of the tree is six layers, the number of estimators (trees) in the ensemble is ten. In the experiment, the hyperparameters of the neural network did not change, the input parameters to various trees were randomly selected by the algorithm.

The Role and Impact of Artificial Intelligence in Addressing Sexually Transmitted Infections, Nonvenereal Genital Diseases, Sexual Health, and Wellness.

The potential of artificial intelligence (AI) in diagnosing and managing sexually transmitted infections (STIs), nonvenereal genital diseases, and overall sexual health is immense. AI shows promise in STI screening and diagnosis through image recognition and patient data analysis, potentially increasing diagnostic accuracy while ensuring inclusivity. AI can fuel the transformation of e-health and direct-to-consumer services, enhancing targeted screening and personalized interventions while improving the user-friendliness of services. There is a significant role for AI in sexual education, particularly its use in interactive, empathetic chatbots. AI's integration into health care as a decision support tool for primary health-care providers can boost real-time diagnostic accuracy. Furthermore, AI's use in big data can enhance real-time epidemiology, predictive analysis, and directed interventions at population levels. However, challenges such as real-world diagnostic accuracy, liability, privacy concerns, and ethical dilemmas persist. Future directions include an emphasis on inclusivity, language accommodation, and swift research-to-practice transitions. Collaboration among policymakers, researchers, and health-care providers is needed to leverage AI's transformative potential in sexual health.

Artificial Intelligence's Use in the Diagnosis of Mouth Ulcers: A Systematic Review.

Artificial intelligence (AI) has been cited as being helpful in the diagnosis of diseases, the prediction of prognoses, and the development of patient-specific therapeutic strategies. AI can help dentists, in particular, when they need to make important judgments quickly. It can eliminate human mistakes in making decisions, resulting in superior and consistent medical treatment while lowering the workload on dentists.The existing studies relevant to the study and application of AI in the diagnosis of various forms of mouth ulcers are reviewed in this work. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) standards were followed in the preparation of the review. There were no rule violations, with the significant exception of the use of a better search method that led to more accurate findings. Using search terms mainly such as AI, oral health, oral ulcers, oral herpes simplex, oral lichen planus, pemphigus vulgaris, recurrent aphthous ulcer (RAU), oral cancer, premalignant and malignant disorders, etc., a comprehensive search was carried out in the reliable sources of literature, namely PubMed, Scopus, Embase, Web of Science, Ovid, Global Health, and PsycINFO. For all papers, exhaustive searches were done using inclusion criteria as well as exclusion criteria between June 28, 2018, and June 28, 2023. An AI framework for the automatic categorization of oral ulcers from oral clinical photographs was developed by the authors, and it performed satisfactorily. The newly designed AI model works better than the current convolutional neural network image categorization techniques and shows a fair level of precision in the classification of oral ulcers. However, despite being useful for identifying oral ulcers, the suggested technique needs a broader set of data for validation and training purposes before being used in clinical settings. Automated OCSCC identification using a deep learning-based technique is a quick, harmless, affordable, and practical approach to evaluating the effectiveness of cancer treatment. The categorization and identification of RAU lesions through the use of non-intrusive oral pictures using the previously developed ResNet50 and YOLOV algorithms demonstrated better accuracy as well as adequate potential for the future, which could be helpful in clinical practice. Moreover, the most reliable projections for the likelihood of the presence or absence of RAU were made by the optimized neural network. The authors also discovered variables associated with RAU that might be used as input information to build artificial neural networks that anticipate RAU.

A special delivery by a fork: Where does artificial intelligence come from?

AbstractIn this article, I discuss the use of artificial intelligence (AI) in evaluation and its relevance to the evolution of the field. I begin with a background on how AI models are developed, including how machine learning makes sense of data and how the algorithms it develops go on to power AI models. I go on to explain how this foundational understanding of machine learning and natural language processing informs where AI might and might not be effectively used. A critical concern is that AI models are only as strong as the data on which they are trained, and evaluators should consider important limitations when using AI, including its relevance to structural inequality. In considering the relationship between AI and evaluation, evaluators must consider both AI's use as an evaluative tool and its role as a new subject of evaluation. As AI becomes more and more relevant to a wider array of fields and disciplines, evaluators will need to develop strategies for how good the AI is (or is not), and what good the AI might (or might not) do.

Place of artificial intelligence in the detection and investigation of crime: the present state and future perspectives

The article analyses an area that has not yet received sufficient scientific attention: the useof artificial intelligence in the investigation of criminal offences. Artificial Intelligence (AI) isa widely discussed topic with rapidly developing technologies that will undoubtedly occupy an important place in criminal investigation and law enforcement activities. Artificial Intelligence (AI) is a rapidly evolving group of technologies. These technologies can provide economic and social benefits in a wide variety of industries and social activities. AI systems are widely used in the interactive world, search engines, digital assistants, computer game development, and security systems, as well as by law enforcement authorities and their techniques. In law enforcement authorities, AI systems are applied in process automation (document analysis, automatically generated messages, etc.), vehicles (automatic license plate scanning, recording of violations), facial recognition systems (in airports, when crossing the state border), robots (automatic demining robots) and internet technologies. Currently, significant investments and resources are being allocated in Lithuania and worldwide in order to improve AI algorithms and usage possibilities1 in both law enforcement authorities and ordinary people’s lives.