Prospects Of Artificial Intelligence Research Articles

Possibilities and prospects of artificial intelligence in the treatment of colorectal cancer (review)

AIM: to study modern approaches to the application of machine learning and deep learning technologies for the management of patients with colorectal cancer.MATERIALS AND METHODS: after screening 398 publications, 112 articles were selected and the full text of the works was studied. After studying the full texts of the articles, the works were selected, machine learning models in which showed an accuracy of more than 80%. The results of 41 original publications were used to write this review.RESULTS: several areas have been identified that are the most promising for the use of artificial intelligence technologies in the management of patients with colorectal cancer. They are predicting the response to neoadjuvant treatment, predicting the risks of metastasis and recurrence of the disease, predicting the toxicity of chemotherapy, assessing the risks of leakage of colorectal anastomoses. As the most promising factors that can be used to train models, researchers consider clinical parameters, the immune environment of the tumor, tumor RNA signatures, as well as visual pathomorphological characteristics. The models for predicting the risk of liver metastases in patients with stage T1 (AUC = 0.9631), as well as models aimed at assessing the risk of 30-day mortality during chemotherapy (AUC = 0.924), were characterized with the greatest accuracy. Most of the technologies discussed in this paper are software products trained on data sets of different quality and quantity, which are able to suggest a treatment scenario based on predictive models, and, in fact, can be used as a doctor’s assistant with very limited functionality.CONCLUSION: the current level of digital technologies in oncology and in the treatment of colorectal cancer does not allow us to talk about a strong AI capable of making decisions about the treatment of patients without medical supervision. Personalized treatment based on the microbiotic and mutation spectrum and, for example, personal pharmacokinetics, so far look fantastic, but certainly promising for future developments.

“What Scares Me Is the Speed at Which Artificial Intelligence Is Developing”: Students' Perceptions of Artificial Intelligence in Foreign Language Teaching

As artificial intelligence (AI) becomes an integral part of our daily lives, the concern of the teaching community about the illegal use of these technologies in the educational process is increasing. In order to adapt the education system and teaching practices to new technological challenges, it is necessary to analyze the opinions of all the parties concerned. The purpose of this study is to identify the attitude of students of Kazan Federal University to the use of artificial intelligence technologies in the educational process and the practice of their application in foreign languages learning process. To achieve this goal, an online survey of students of Kazan Federal University was conducted. The survey touched upon the practical aspects of the use of artificial intelligence in language teaching, the advantages and disadvantages of AI tools from the students’ point of view, as well as their opinion regarding the prospects of AI in education. As a result of the study, we came to the conclusion that at the moment AI tools are not widespread enough in teaching foreign languages. Only one-fifth of the respondents use these tools, but the respondents’ comments suggest that the number of users will grow. The attitude of students towards the use of AI is ambiguous, with responses ranging from enthusiastic to skeptical. Students’ positive impressions are mainly related to saving time and effort, as well as to the ability of AI to present complex materials with understandable language. Among the main disadvantages, the respondents noted unreliability of data and fake content. Despite the fact that students are generally positive about the use of AI, a significant part of respondents do not trust software products such as ChatGPT, since, in their opinion, it provides answers of average quality that need to be corrected. Based on the data obtained, the authors attempted to formulate recommendations on improving the methods of teaching and control in the process of teaching foreign languages at universities.

A Review of Artificial Intelligence in Breast Imaging.

With the increasing dominance of artificial intelligence (AI) techniques, the important prospects for their application have extended to various medical fields, including domains such as in vitro diagnosis, intelligent rehabilitation, medical imaging, and prognosis. Breast cancer is a common malignancy that critically affects women's physical and mental health. Early breast cancer screening-through mammography, ultrasound, or magnetic resonance imaging (MRI)-can substantially improve the prognosis for breast cancer patients. AI applications have shown excellent performance in various image recognition tasks, and their use in breast cancer screening has been explored in numerous studies. This paper introduces relevant AI techniques and their applications in the field of medical imaging of the breast (mammography and ultrasound), specifically in terms of identifying, segmenting, and classifying lesions; assessing breast cancer risk; and improving image quality. Focusing on medical imaging for breast cancer, this paper also reviews related challenges and prospects for AI.

Challenges and Prospects of Artificial Intelligence in Nigerian Journalism Practice: A Narrative Review

The integration of artificial intelligence (AI) into Nigerian journalism practices presents both challenges and prospects for the industry. This paper explores the dynamic interplay between AI technologies and journalism in Nigeria, addressing key conceptual clarifications, theoretical frameworks, challenges, and prospects associated with AI utilization in the field. Drawing upon existing literature, the study examines the multifaceted nature of AI, ranging from its conceptual underpinnings to its practical applications in journalism. It utilizes the Technology-Organization-Environment (TOE) framework to analyze the factors influencing AI adoption within Nigerian newsrooms, considering technological, organizational, and environmental contexts. Identifying challenges such as infrastructural limitations, financial constraints, cultural resistance, and ethical considerations, the paper elucidates the complexities inherent in AI integration in Nigerian journalism. These challenges encompass issues of data quality, transparency, bias, and accountability, underscoring the need for responsible AI implementation and robust ethical frameworks. Additionally, the paper discusses the prospects offered by AI in enhancing journalistic efficiency, facilitating data-driven reporting, personalizing news delivery, and combating misinformation. By leveraging AI for fact-checking, multilingual reporting, predictive analytics, and automated content generation, Nigerian journalists can innovate and adapt to the evolving media landscape. Moreover, AI presents opportunities for improving audience engagement, storytelling, collaboration, and addressing bias and ethics concerns. Embracing AI in journalism also creates educational pathways for aspiring journalists, fostering skill development and innovation in the industry.

Exploring the Role of Artificial Intelligence in Mental Healthcare: Current Trends and Future Directions - A Narrative Review for a Comprehensive Insight.

Mental health is an essential component of the health and well-being of a person and community, and it is critical for the individual, society, and socio-economic development of any country. Mental healthcare is currently in the health sector transformation era, with emerging technologies such as artificial intelligence (AI) reshaping the screening, diagnosis, and treatment modalities of psychiatric illnesses. The present narrative review is aimed at discussing the current landscape and the role of AI in mental healthcare, including screening, diagnosis, and treatment. Furthermore, this review attempted to highlight the key challenges, limitations, and prospects of AI in providing mental healthcare based on existing works of literature. The literature search for this narrative review was obtained from PubMed, Saudi Digital Library (SDL), Google Scholar, Web of Science, and IEEE Xplore, and we included only English-language articles published in the last five years. Keywords used in combination with Boolean operators ("AND" and "OR") were the following: "Artificial intelligence", "Machine learning", Deep learning", "Early diagnosis", "Treatment", "interventions", "ethical consideration", and "mental Healthcare". Our literature review revealed that, equipped with predictive analytics capabilities, AI can improve treatment planning by predicting an individual's response to various interventions. Predictive analytics, which uses historical data to formulate preventative interventions, aligns with the move toward individualized and preventive mental healthcare. In the screening and diagnostic domains, a subset of AI, such as machine learning and deep learning, has been proven to analyze various mental health data sets and predict the patterns associated with various mental health problems. However, limited studies have evaluated the collaboration between healthcare professionals and AI in delivering mental healthcare, as these sensitive problems require empathy, human connections, and holistic, personalized, and multidisciplinary approaches. Ethical issues, cybersecurity, a lack of data analytics diversity, cultural sensitivity, and language barriers remain concerns for implementing this futuristic approach in mental healthcare. Considering these sensitive problems require empathy, human connections, and holistic, personalized, and multidisciplinary approaches, it is imperative to explore these aspects. Therefore, future comparative trials with larger sample sizes and data sets are warranted to evaluate different AI models used in mental healthcare across regions to fill the existing knowledge gaps.

리처드 파워스의 소설『갈라테아 2.2』에 기술된 인공 지능(AI) 헬렌(Helen)의 특징적인 언어양상 연구

Richard Power’s novel Galatea 2.2 presents the task and prospect of Artificial Intelligence’s language although the novel is based on story-telling and the author’s imagination. In particular, it is worth to note not only terms of address and terms of reference but also terms of body concerning Artificial Intelligence Helen. She is mostly addressed by her first name (Helen) and other terms such as machine, apparatus, bag of networks, fancy tin-can telephone, distributed process, multidimentional shape, etc. Also, Artificial Intelligence Helen is a symbol of A (a twenty-two year old woman and post-graduate student) and imitating A, some terms like ‘a gifted cockatiel’ and ‘twin’ are used for Helen in terms of reference. In comparison, Rick is addressed in various ways, that is first name, surname, affectionate names and other nicknames. In addition, Artificial Intelligence Helen has no nose, mouth or fingers and only she has partially functioning eyes and ears. Thus Helen is not able to fully understand metaphorical language expressions concerning emotion. For instance, in some expressions like ‘your stomach in knots’, ‘embarrasment shrinks you’, or ‘amazement strikes you dead’. Helen could not feel the meanings and thus she has a lack of knowledge in similar linguistic circumstances. Lastly, Artificial Intelligence Helen is Imp. H. It means that Helen has been evolved from Imp. A, Imp. B, Imp. C, and so on. So the shut down of herself in the novel suggests and hints at the revival of a new version of Artificial Intelligence may be named “Galatea 3.3”.

Current status and prospects of artificial intelligence in breast cancer pathology: convolutional neural networks to prospective Vision Transformers.

Breast cancer is the most prevalent cancer among women, and its diagnosis requires the accurate identification and classification of histological features for effective patient management. Artificial intelligence, particularly through deep learning, represents the next frontier in cancer diagnosis and management. Notably, the use of convolutional neural networks and emerging Vision Transformers (ViT) has been reported to automate pathologists' tasks, including tumor detection and classification, in addition to improving the efficiency of pathology services. Deep learning applications have also been extended to the prediction of protein expression, molecular subtype, mutation status, therapeutic efficacy, and outcome prediction directly from hematoxylin and eosin-stained slides, bypassing the need for immunohistochemistry or genetic testing. This review explores the current status and prospects of deep learning in breast cancer diagnosis with a focus on whole-slide image analysis. Artificial intelligence applications are increasingly applied to many tasks in breast pathology ranging from disease diagnosis to outcome prediction, thus serving as valuable tools for assisting pathologists and supporting breast cancer management.

Artificial intelligence and machine learning in axial spondyloarthritis.

To evaluate the current applications and prospects of artificial intelligence and machine learning in diagnosing and managing axial spondyloarthritis (axSpA), focusing on their role in medical imaging, predictive modelling, and patient monitoring. Artificial intelligence, particularly deep learning, is showing promise in diagnosing axSpA assisting with X-ray, computed tomography (CT) and MRI analyses, with some models matching or outperforming radiologists in detecting sacroiliitis and markers. Moreover, it is increasingly being used in predictive modelling of disease progression and personalized treatment, and could aid risk assessment, treatment response and clinical subtype identification. Variable study designs, sample sizes and the predominance of retrospective, single-centre studies still limit the generalizability of results. Artificial intelligence technologies have significant potential to advance the diagnosis and treatment of axSpA, providing more accurate, efficient and personalized healthcare solutions. However, their integration into clinical practice requires rigorous validation, ethical and legal considerations, and comprehensive training for healthcare professionals. Future advances in artificial intelligence could complement clinical expertise and improve patient care through improved diagnostic accuracy and tailored therapeutic strategies, but the challenge remains to ensure that these technologies are validated in prospective multicentre trials and ethically integrated into patient care.

Application and Prospects of Artificial Intelligence in Breast Cancer Early Diagnosis, Screening, and Treatment

In modern society, as artificial intelligence technology becomes increasingly mature, AI can already be utilized to assess and treat breast carcinoma. By summarizing results and data of experiments that use AI to help breast carcinoma diagnosis and cure, this article states the positive impact of AI on breast cancer screening, detection of breast cancer-related genes, and prediction of efficacy of breast cancer treatment drugs. What is more, illustrating the moral, legal, and social challenges encountered by scientists when they use artificial intelligence. Also, this article makes some suggestions for the future development of AI technology. The author believes that AI technology have the ability to contribute significantly to the diagnosis and cure of breast cancer and can enhance the chances of patients surviving to a considerable extent. However, currently, artificial intelligence technology remains in a relatively nascent stage, necessitating ongoing attention and dedicated efforts for its advancement.

What Complexity Science Predicts About the Potential of Artificial Intelligence/Machine Learning to Improve Primary Care.

Primary care physicians are likely both excited and apprehensive at the prospects for artificial intelligence (AI) and machine learning (ML). Complexity science may provide insight into which AI/ML applications will most likely affect primary care in the future. AI/ML has successfully diagnosed some diseases from digital images, helped with administrative tasks such as writing notes in the electronic record by converting voice to text, and organized information from multiple sources within a health care system. AI/ML has less successfully recommended treatments for patients with complicated single diseases such as cancer; or improved diagnosing, patient shared decision making, and treating patients with multiple comorbidities and social determinant challenges. AI/ML has magnified disparities in health equity, and almost nothing is known of the effect of AI/ML on primary care physician-patient relationships. An intervention in Victoria, Australia showed promise where an AI/ML tool was used only as an adjunct to complex medical decision making. Putting these findings in a complex adaptive system framework, AI/ML tools will likely work when its tasks are limited in scope, have clean data that are mostly linear and deterministic, and fit well into existing workflows. AI/ML has rarely improved comprehensive care, especially in primary care settings, where data have a significant number of errors and inconsistencies. Primary care should be intimately involved in AI/ML development, and its tools carefully tested before implementation; and unlike electronic health records, not just assumed that AI/ML tools will improve primary care work life, quality, safety, and person-centered clinical decision making.

State of the art and prospects for artificial intelligence in orthognathic surgery: A systematic review with meta-analysis

ObjectiveTo present a systematic review of the state of the art regarding clinical applications, main features, and outcomes of artificial intelligence (AI) in orthognathic surgery. MethodsThe PICOS strategy was performed on a systematic review (SR) to answer the following question: “What are the state of the art, characteristics and outcomes of applications with artificial intelligence for orthognathic surgery?” After registering in PROSPERO (CRD42021270789) a systematic search was performed in the databases: PubMed (including MedLine), Scopus, Embase, LILACS, MEDLINE EBSCOHOST and Cochrane Library. 195 studies were selected, after screening titles and abstracts, of which thirteen manuscripts were included in the qualitative analysis and six in the quantitative analysis. The treatment effects were plotted in a Forest-plot. JBI questionnaire for observational studies was used to asses the risk of bias. The quality of the SR evidence was assessed using the GRADE tool. ResultsAI studies on 2D cephalometry for orthognathic surgery, the Tau2 = 0.00, Chi2 = 3.78, p = 1.00 and I² of 0 %, indicating low heterogeneity, AI did not differ statistically from control (p = 0.79). AI studies in the diagnosis of the decision of whether or not to perform orthognathic surgery showed heterogeneity, and therefore meta-analysis was not peformed. ConclusionThe outcome of AI is similar to the control group, with a low degree of bias, highlighting its potential for use in various applications.

Research progress and prospects of artificial intelligence in diagnosis and treatment of colorectal cancer

Colorectal cancer is one of the most common malignant tumors worldwide. Due to the heterogeneity in patient outcomes and treatment responses to standard therapy regimens, personalized diagnostic and therapeutic strategies have remained a focus of sustained interest in research. In recent years, with the rapid progression of artificial intelligence (AI) technology in the medical field, an abundance of phased research results has emerged in the decision-making for preoperative, intraoperative, and postoperative diagnostic and therapeutic plans for colorectal cancer, demonstrating great potential for application. This new and efficient solution provides for the personalized evaluations and auxiliary diagnoses and treatments of patients with colorectal cancer. In the future, AI systems may continue to advance towards multimodal, multi-omics, and real-time directions. This paper aims to explore the current state of research on the multi-faceted auxiliary applications of AI in the diagnosis and treatment of colorectal cancer, as well as to present a prospective view of the innovations that AI technology could bring to personalized colorectal cancer treatment in the future and the challenges it may face.

Exploring the matrix: knowledge, perceptions and prospects of artificial intelligence and machine learning in Nigerian healthcare.

Artificial intelligence technology can be applied in several aspects of healthcare delivery and its integration into the Nigerian healthcare value chain is expected to bring about new opportunities. This study aimed at assessing the knowledge and perception of healthcare professionals in Nigeria regarding the application of artificial intelligence and machine learning in the health sector. A cross-sectional study was undertaken amongst healthcare professionals in Nigeria with the use of a questionnaire. Data were collected across the six geopolitical zones in the Country using a stratified multistage sampling method. Descriptive and inferential statistical analyses were undertaken for the data obtained. Female participants (55.7%) were slightly higher in proportion compared to the male respondents (44.3%). Pharmacists accounted for 27.7% of the participants, and this was closely followed by medical doctors (24.5%) and nurses (19.3%). The majority of the respondents (57.2%) reported good knowledge regarding artificial intelligence and machine learning, about a third of the participants (32.2%) were of average knowledge, and 10.6% of the sample had poor knowledge. More than half of the respondents (57.8%) disagreed with the notion that the adoption of artificial intelligence in the Nigerian healthcare sector could result in job losses. Two-thirds of the participants (66.7%) were of the view that the integration of artificial intelligence in healthcare will augment human intelligence. Three-quarters (77%) of the respondents agreed that the use of machine learning in Nigerian healthcare could facilitate efficient service delivery. This study provides novel insights regarding healthcare professionals' knowledge and perception with respect to the application of artificial intelligence and machine learning in healthcare. The emergent findings from this study can guide government and policymakers in decision-making as regards deployment of artificial intelligence and machine learning for healthcare delivery.

Application Scenarios and Forms of Artificial Intelligence in Physical Education

The application of artificial intelligence in physical education is analyzed. The manifestations of artificial intelligence in physical education are as follows: accurate diagnosis, process monitoring, personalized service and intelligent decision-making; The application forms are as follows: intelligent tutor system, automatic evaluation system and physical education robot. The prospect of artificial intelligence in physical education is put forward: facing modernization and building a learning society; Facing the world, changing the teaching concept; Facing the future, innovating physical education teaching methods.

Explore the Development Status of Artificial Intelligence and the Application Analysis of Specific Fields

In the wave of scientific and technological development in the new era, artificial intelligence technology has increasingly become one of the hot scientific and technological fields of social development. Since the concept was proposed, the development of artificial intelligence has also virtually stimulated the market and business strategies in a variety of industry fields. Starting from the actual situation, this paper now divides the four development stages of artificial intelligence from the past to the future in detail and uses the small-range questionnaire survey method to understand the current Chinese society's understanding of and interest in artificial intelligence and its products. Then, literature analysis is used to search and read relevant materials and literature, and the development status, capabilities, and prospects of artificial intelligence in the field of natural language processing and machine learning large models and image and face recognition are analyzed and summarized. In the field of face recognition selected popular processing methods and material library, simple simulation of mainstream recognition options and means, through further examples to help readers understand the development of artificial intelligence status and direction. Understanding the information and relevant viewpoints and attitudes in the specific field, detailing the logic behind the inability of artificial intelligence to replace human beings and the technical difficulties and challenges faced, is of great value to improving the popularization of the concept of artificial intelligence and its development and progress in the future society.

Application of artificial intelligence in oculoplastics

Oculoplastics is a subspecialty of ophthalmology/dermatology concerned with eyelid, orbital, and lacrimal diseases. Artificial intelligence (AI), with its powerful ability to analyze large data sets, has dramatically benefited oculoplastics. The cutting-edge AI technology is widely applied to extract ocular parameters and to use these results for further assessment, such as screening and diagnosis of blepharoptosis and predicting the progression of thyroid eye disease. AI also assists in treatment procedures, such as surgical strategy planning in blepharoptosis. High efficiency and high reliability are the most apparent advantages of AI, with promising prospects. The possibilities of AI in oculoplastics may lie in three-dimensional modeling technology and image generation. We retrospectively summarize AI applications involving eyelid, orbital, and lacrimal diseases in oculoplastics, and we also examine the strengths and weaknesses of AI technology in oculoplastics.

Application analysis and prospects of artificial intelligence in enterprise technology management

Application analysis and prospects of artificial intelligence in enterprise technology management

Utilization and Prospects of Artificial Intelligence in Art - Focusing on the Visual Content Creation Process

Utilization and Prospects of Artificial Intelligence in Art - Focusing on the Visual Content Creation Process

Prospects for Artificial Intelligence in Command and Control

Prospects for Artificial Intelligence in Command and Control

Artificial intelligence in skeletal metastasis imaging

In the field of metastatic skeletal oncology imaging, the role of artificial intelligence (AI) is becoming more prominent. Bone metastasis typically indicates the terminal stage of various malignant neoplasms. Once identified, it necessitates a comprehensive revision of the initial treatment regime, and palliative care is often the only resort. Given the gravity of the condition, the diagnosis of bone metastasis should be approached with utmost caution. AI techniques are being evaluated for their efficacy in a range of tasks within medical imaging, including object detection, disease classification, region segmentation, and prognosis prediction in medical imaging. These methods offer a standardized solution to the frequently subjective challenge of image interpretation.This subjectivity is most desirable in bone metastasis imaging. This review describes the basic imaging modalities of bone metastasis imaging, along with the recent developments and current applications of AI in the respective imaging studies. These concrete examples emphasize the importance of using computer-aided systems in the clinical setting. The review culminates with an examination of the current limitations and prospects of AI in the realm of bone metastasis imaging. To establish the credibility of AI in this domain, further research efforts are required to enhance the reproducibility and attain robust level of empirical support.