Progress of Artificial Intelligence in Gynecological Malignant Tumors

Artificial intelligence (AI) technology has made leaps and bounds since its invention. AI technology can be subdivided into many technologies such as machine learning and deep learning. The application scope and prospect of different technologies are also totally different. Currently, AI technologies play a pivotal role in the highly complex and wide-ranging medical field, such as medical image recognition, biotechnology, auxiliary diagnosis, drug research and development, and nutrition. Colorectal cancer (CRC) is a common gastrointestinal cancer that has a high mortality, posing a serious threat to human health. Many CRCs are caused by the malignant transformation of colorectal polyps. Therefore, early diagnosis and treatment are crucial to CRC prognosis. The methods of diagnosing CRC are divided into imaging diagnosis, endoscopy, and pathology diagnosis. Treatment methods are divided into endoscopic treatment, surgical treatment, and drug treatment. AI technology is in the weak era and does not have communication capabilities. Therefore, the current AI technology is mainly used for image recognition and auxiliary analysis without in-depth communication with patients. This article reviews the application of AI in the diagnosis, treatment, and prognosis of CRC and provides the prospects for the broader application of AI in CRC.

Artificial intelligence (AI), a field of computer science, aims at simulating human intelligence with computers. Though AI has surpassed dermatologists in skin cancer detection, it still lags behind various other specialties like radiologists in broader adoption. Newer AI applications are becoming increasingly accessible. AI plays a role in various areas, such as medical image recognition, auxiliary diagnosis, and drug research and development. Dermatology has a prime position in implementation of AI in medical research due to its larger clinical, dermoscopic, and histopathological image database. Hence, it is crucial to consider the potential and emerging role of AI in dermatology clinical practice. There are already studies focusing on various skin disorders like cancer, psoriasis, atopic dermatitis, etc. This article provides an overview of AI and its applications in hair and nail disorders at present and its future potential. J Drugs Dermatol. 2022;21(10):1049-1052. doi:10.36849/JDD.6519.

Focus issue: Artificial intelligence in medical physics.

We are glad to introduce you that the 2021 3rd International Conference on Artificial Intelligence Technologies and Applications (ICAITA 2021) was successfully held on September 10-12, 2021. In light of worldwide travel restriction and the impact of COVID-19, ICAITA 2021 was carried out in the form of virtual conference to avoid personnel gatherings. Because most participants were still highly enthusiastic about participating in this conference, we chose to carry out ICAITA 2021 via online platform according to the original schedule instead of postponing it.ICAITA 2021 is to bring together innovative academics and industrial experts in the field of Artificial Intelligence Technologies and Applications to a common forum. The primary goal of the conference is to promote research and developmental activities in Artificial Intelligence Technologies and Applications and another goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working all around the world. The conference will be held every year to make it an ideal platform for people to share views and experiences in Artificial Intelligence Technologies and Applications and related areas.This scientific event brings together more than 100 national and international researchers in artificial intelligence technologies and applications. During the conference, the conference model was divided into three sessions, including oral presentations, keynote speeches, and online Q&A discussion. In the first part, some scholars, whose submissions were selected as the excellent papers, were given about 5-10 minutes to perform their oral presentations one by one. Then in the second part, keynote speakers were each allocated 30-45 minutes to hold their speeches.We were pleased to invite three distinguished experts to present their insightful speeches. Our first keynote speaker, Prof. Yau Kok Lim, from Sunway University, Malaysia. His research interests include Applied artificial intelligence, 5G networks, Cognitiveradio networks, Routing and clustering, Trust and reputation, Intelligent transportation system. And then we had Prof. Peter Sincak, from Technical University of Kosice, Slovakia. His research includes Artificial Intelligence and Intelligent Systems. Lastly, we were glad to invite Chinthaka Premachandra, from Shibaura Institute of Technology, Sri Lanka. His research interests include Artificial Intelligence, image processing and robotics. In the last part of the conference, all participants were invited to join in a WeChat group to discuss and explore the academic issues after the presentations. The online discussion was lasted for about 30-60 minutes. The first two parts were conducted via online collaboration tool, Zoom, while the online discussion was carried out through instant communication tool, WeChat. The online platform enabled all participants to join this grand academic event from their own home.We are glad to share with you that we still received lots of submissions from the conference during this special period. Hence, we selected a bunch of high-quality papers and compiled them into the proceedings after rigorously reviewed them. These papers feature following topics but are not limited to: Artificial Intelligence Applications & Technologies, Computing and the Mind, Foundations of Artificial Intelligence and other related topics. All the papers have been through rigorous review and process to meet the requirements of international publication standard.Lastly, we would like to express our sincere gratitude to the Chairman, the distinguished keynote speakers, as well as all the participants. We also want to thank the publisher for publishing the proceedings. May the readers could enjoy the gain some valuable knowledge from the proceedings. We are expecting more and more experts and scholars from all over the world to join this international event next year.The Committee of ICAITA 2021List of titles Committee member, General Conference Chair, Technical Program Committee Chair, Academic Committee Chair, Technical Program Committee Member, Academic Committee Member are available in this Pdf.

Gynecological malignant tumors seriously threaten the women's lives and also the health, with cervical cancer, ovarian cancer and endometrial cancer being the most common. Traditional treatment modalities (surgery, chemotherapy, radiotherapy) remain the cornerstone, but their efficacy is often limited by drug resistance and recurrence. New treatment methods need to be explored to save patients' lives. Immune checkpoint inhibitors (ICI) have made breakthrough progress in gynecological malignant solid tumors. By blocking the binding of inhibitory receptors on the surface of T cells (such as PD-1 and CTLA-4) to their ligands, they have reactivated the immune response of T cells to tumors, completely changing the treatment landscape. This article aims to systematically review the current clinical application status, key clinical trial evidence and biomarker roles of ICI in cervical cancer, endometrial cancer and ovarian cancer, deeply analyze the challenges it faces, including the limitations of biomarker prediction, drug resistance, management of immune-related adverse reactions and exploration of combination treatment strategies. In order to provide references for its clinical application and future research.

PurposeThis study explored the different artificial intelligence (AI) applications used in academic libraries and the key factors and impediments related to their implementation.Design/methodology/approachThe author applied quantitative research methods in the form of a questionnaire, using both open and closed questions. A total of 472 valid questionnaires were received from academic librarians.FindingsThe author sought responses from librarians who had implemented AI applications and those who had not, identifying the types of AI applications implemented, key factors relating to their implementation, and impediments to promoting AI. Gaps were identified between the level of support for AI applications and the negative effect of the impediments. Furthermore, the more extensive the individual and organizational knowledge activities performed by the librarians and libraries held, the more positive the attitude was librarians' attitude toward AI applications in their libraries. However, librarians recognized that AI applications are inevitable, but indicated that the difficulties of in execution have hampered the adoption of AI.Research limitations/implicationsThe sample data were collected in Taiwan; therefore, the data may only represent the views of Taiwanese academic librarians on AI applications. The results of this study may not apply to librarians worldwide; however, they may provide a useful reference.Practical implicationsThe results revealed the top four AI applications that libraries would most likely implement in the near future. Therefore, AI application developers and suppliers can prioritize the promotion of these products for to academic libraries. This study revealed that funding and costs related to AI implementation were discovered to be key factors relating to implementing AI applications. Some impediments to the implementation of AI applications relate to technological problems. Several librarians suggested that managers should invest more resources at an early stage rather than reducing cutting back on human resources initially. Although worries regarding privacy and ethics were mentioned expressed by some respondents, most academic librarians did not regard these to be major concerns.Originality/valueThis study provides the perspectives of librarians who have implemented AI applications and of those who have not. In addition, it explores the advantages and disadvantages of AI applications, and the level of support for and impact of AI applications and promotions. This study also included a gap analysis. Moreover, individual and organizational knowledge activity scales were adopted to examine AI awareness and the perceptions of academic librarians.

BackgroundThe increasing development of artificial intelligence (AI) systems in medicine driven by researchers and entrepreneurs goes along with enormous expectations for medical care advancement. AI might change the clinical practice of physicians from almost all medical disciplines and in most areas of health care. While expectations for AI in medicine are high, practical implementations of AI for clinical practice are still scarce in Germany. Moreover, physicians’ requirements and expectations of AI in medicine and their opinion on the usage of anonymized patient data for clinical and biomedical research have not been investigated widely in German university hospitals.ObjectiveThis study aimed to evaluate physicians’ requirements and expectations of AI in medicine and their opinion on the secondary usage of patient data for (bio)medical research (eg, for the development of machine learning algorithms) in university hospitals in Germany.MethodsA web-based survey was conducted addressing physicians of all medical disciplines in 8 German university hospitals. Answers were given using Likert scales and general demographic responses. Physicians were asked to participate locally via email in the respective hospitals.ResultsThe online survey was completed by 303 physicians (female: 121/303, 39.9%; male: 173/303, 57.1%; no response: 9/303, 3.0%) from a wide range of medical disciplines and work experience levels. Most respondents either had a positive (130/303, 42.9%) or a very positive attitude (82/303, 27.1%) towards AI in medicine. There was a significant association between the personal rating of AI in medicine and the self-reported technical affinity level (H4=48.3, P<.001). A vast majority of physicians expected the future of medicine to be a mix of human and artificial intelligence (273/303, 90.1%) but also requested a scientific evaluation before the routine implementation of AI-based systems (276/303, 91.1%). Physicians were most optimistic that AI applications would identify drug interactions (280/303, 92.4%) to improve patient care substantially but were quite reserved regarding AI-supported diagnosis of psychiatric diseases (62/303, 20.5%). Of the respondents, 82.5% (250/303) agreed that there should be open access to anonymized patient databases for medical and biomedical research.ConclusionsPhysicians in stationary patient care in German university hospitals show a generally positive attitude towards using most AI applications in medicine. Along with this optimism comes several expectations and hopes that AI will assist physicians in clinical decision making. Especially in fields of medicine where huge amounts of data are processed (eg, imaging procedures in radiology and pathology) or data are collected continuously (eg, cardiology and intensive care medicine), physicians’ expectations of AI to substantially improve future patient care are high. In the study, the greatest potential was seen in the application of AI for the identification of drug interactions, assumedly due to the rising complexity of drug administration to polymorbid, polypharmacy patients. However, for the practical usage of AI in health care, regulatory and organizational challenges still have to be mastered.

This chapter investigates the transformative role of Artificial Intelligence (AI) applications in diverse domains, specifically focusing on healthcare, agriculture, defence and medicine. The integration of AI technologies in these sectors has led to significant advancements, improving efficiency, decision-making, and outcomes. AI applications in healthcare range from diagnostic tools and predictive analytics to personalized treatment plans. This chapter explores how AI aids in early disease detection, medical imaging analysis, drug discovery, and patient care optimization. Real cases illustrate the successful implementation of AI-driven solutions, showcasing improved diagnostic accuracy and enhanced patient outcomes. In agriculture, AI technologies contribute to precision farming, crop monitoring and yield prediction. The chapter delves into how AI-driven solutions analyse data from various sources, such as satellite imagery and IoT devices, to optimize resource allocation, pest control, and crop management. Real cases demonstrate increased agricultural productivity and sustainability through the application of AI. AI plays a crucial role in modern defence systems, including surveillance, threat detection, and decision support. This chapter examines how AI enhances military operations, cybersecurity, and strategic planning. Real cases highlight instances where AI technologies have bolstered national security by improving threat identification and response capabilities. The field of medicine benefits extensively from AI applications, ranging from patient care to administrative tasks. Real cases showcase instances where AI-driven technologies have accelerated drug discovery, streamlined healthcare workflows, and personalized patient treatment plans. This chapter provides a comprehensive overview of the impact of AI applications in healthcare, agriculture, defence and medicine. By exploring real cases, it underscores the tangible benefits and transformative potential of AI technologies across these diverse domains. The insights presented in this analysis contribute to a deeper understanding of the evolving landscape shaped by the integration of AI in critical sectors, paving the way for further innovations and advancements. The chapter outlines key aspects of AI applications in each domain and highlights real-world cases demonstrating their impact. This chapter explores how AI facilitates medical research, automates administrative processes, and supports clinical decision-making.

With the rapid development of modern information electronic technology, artificial intelligence information technology has gradually penetrated into all fields of human society. This paper aims to improve the clinical assistant nurses’ correct understanding of medical artificial intelligence, and to provide an important reference for further promoting the wide application and development of clinical artificial machine intelligence in the field of clinical nursing in China. In this paper, through the comparison of intelligent nursing and traditional nursing effect monitoring, as well as the analysis of medical staff and patients’ satisfaction with artificial intelligence treatment time, treatment effect and treatment scheme, and the results were discussed and analyzed. The problems that should be paid attention to in the application of artificial intelligence in clinical nursing were put forward, which provided guarantee for the development of clinical nursing. The research in this paper has important practical significance for the further development of the two.KeywordsIn the information ageArtificial intelligenceClinical nursingSmart devices

BackgroundResearch on technologies based on artificial intelligence in healthcare has increased during the last decade, with applications showing great potential in assisting and improving care. However, introducing these technologies into nursing can raise concerns related to data bias in the context of training algorithms and potential implications for certain populations. Little evidence exists in the extant literature regarding the efficacious application of many artificial intelligence -based health technologies used in healthcare. ObjectivesTo synthesize currently available state-of the-art research in artificial intelligence -based technologies applied in nursing practice. DesignScoping review MethodsPubMed, CINAHL, Web of Science and IEEE Xplore were searched for relevant articles with queries that combine names and terms related to nursing, artificial intelligence and machine learning methods. Included studies focused on developing or validating artificial intelligence -based technologies with a clear description of their impacts on nursing. We excluded non-experimental studies and research targeted at robotics, nursing management and technologies used in nursing research and education. ResultsA total of 7610 articles published between January 2010 and March 2021 were revealed, with 93 articles included in this review. Most studies explored the technology development (n = 55, 59.1%) and formation (testing) (n = 28, 30.1%) phases, followed by implementation (n = 9, 9.7%) and operational (n = 1, 1.1%) phases. The vast majority (73.1%) of studies provided evidence with a descriptive design (level VI) while only a small portion (4.3%) were randomised controlled trials (level II). The study aims, settings and methods were poorly described in the articles, and discussion of ethical considerations were lacking in 36.6% of studies. Additionally, one-third of papers (33.3%) were reported without the involvement of nurses. ConclusionsContemporary research on applications of artificial intelligence -based technologies in nursing mainly cover the earlier stages of technology development, leaving scarce evidence of the impact of these technologies and implementation aspects into practice. The content of research reported is varied. Therefore, guidelines on research reporting and implementing artificial intelligence -based technologies in nursing are needed. Furthermore, integrating basic knowledge of artificial intelligence -related technologies and their applications in nursing education is imperative, and interventions to increase the inclusion of nurses throughout the technology research and development process is needed.

This study evaluates the impact of Artificial Intelligence (AI) applications in power systems on energy security and to determine relevant policy implications. We use a mixed methods approach to analyze the benefits and risks associated with AI implementation on the European power grid, focusing on four key dimensions of energy security: availability, affordability, accessibility, and acceptability. We investigated the benefits of AI using PyPSA, a Python-based model of the European electricity system. Three AI applications were parametrized: load reduction, load shifting, and wind wake steering. We compared scenarios in which these AI applications are widely deployed against a baseline scenario without these applications to determine if AI improves energy security. The study also analyzes risks associated with AI deployment in the power grid. We developed a risk taxonomy centered around six high-level categories: cybersecurity, jurisdictional or sovereignty issues, unexplained or unexpected actions by the model, unethical or illegal decision-making, reliance and trust in decision-making, and supplier dependency and vendor lock-in. Additionally, we conducted a back-casting exercise with subject-matter experts to determine positive and negative future outcomes of AI deployment and identify actions to create positive outcomes and avoid negative ones. The paper presents a set of policy implications for AI on the European synchronous grid. We find that AI applications can improve energy security in power systems. In the scenarios we tested, behind-the-meter applications have a greater impact on energy security than front-of-meter applications. The results suggest that AI applications targeting energy consumption may significantly improve energy security metrics. Keywords: Energy Security, AI, Power Grid, European Electricity System

Objective In order to provide the basis for nutritional therapy to patients with gynecological malignant tumor, this article investigates application of nutritional risk, nutritional status and support of patients with gynecologic malignant tumor during the perioperative period. Methods Using the fixed-point consecutive sampling method, 138 cases of inpatients with gynecologic malignant tumor accepting operative treatment were selected as research object, taking nutritional risk screening on these inpatients by NRS 2002, and investigating the types and modes of nutritional support and postoperative complications. Results Incidences of malnutrition and nutritional risk were 5.07% (7/138) and 65.22% (90/138) respectively. Among 138 patients with gynaecological malignant tumors, total nutritional support rate was 58.70% (81/138); primary support mode was parenteral nutrition implemented by single bottle transfusion and all in one transfusion, the former was 72 cases (88.89%), and the latter was 9 cases (11.11%). Enteral nutrition support was 2 cases (1.45%). Among 90 patients with nutritional risk, 77.78% (70/90) had accepted perioperative nutritional support. Among 48 patients with no nutritional risk, 22.92%(11/48) had accepted nutritional support. Conclusions Patients with gynaecological malignant tumors have a higher incidence of nutritional risk post-operation, and clinical nutrition support intervention is unreasonable. Accordingly, nutritional support for patients with gynaecological malignant tumors in perioperative period needs to be more normative. Key words: Nutritional support; Gynecologic malignant tumor; Nutritional screening

ObjectivesDifferent stakeholders may hold varying attitudes towards artificial intelligence (AI) applications in healthcare, which may constrain their acceptance if AI developers fail to take them into account. We set out...

Technological progress, especially in the field of information, has led to reducing the gap between science, information and knowledge. This accelerating change has forced all educational institutions to comply with this new mission by using information technology in the teaching process, as artificial intelligence has become an integral part of the educational and learning process, and a means of Effective in the teaching process, in addition to being research tools in obtaining information and knowledge, the research problem focused on (what are the attitudes of the kindergarten teacher towards using artificial intelligence applications in the effective teaching process in the early education stage in the third Rusafa Education Directorate?), where the importance of The research is considered one of the researches that deals with an important and vital topic (artificial intelligence), as it gains its importance in that it represents the role that kindergarten teachers play in the early education stage in developing the skills and abilities and refining the information of the child through their use of artificial intelligence methods and applications, as well as The positive role of such research in stimulating kindergarten teachers’ motivation to benefit from artificial intelligence applications, which increases the efficiency and effectiveness of their teaching. The aim of the research is to reveal kindergarten teachers’ attitudes towards using artificial intelligence in the effective teaching process in the early education stage, as well as to reveal the most important The challenges facing kindergarten teachers using artificial intelligence in the effective teaching process, and are there any statistically significant differences between the average kindergarten teacher’s attitudes to using artificial intelligence in the effective teaching process in the early education stage according to the research variables? The research reached several conclusions, perhaps the most important of which is the existence of differences. There is statistical significance between the average kindergarten teacher’s attitudes to using artificial intelligence in the effective teaching process in the early education stage according to the research variables. The research recommends holding many workshops and training courses for kindergarten teachers on how to use artificial intelligence in effective teaching, and including programs and rehabilitation plans for kindergarten teachers. About the use of artificial intelligence according to the plans of the Ministry of Education, which are consistent with its vision, and encouraging teachers to proceed and benefit from applications and websites for safe and available artificial intelligence applications in the teaching process

Outline for a German Strategy for Artificial Intelligence