Artificial Intelligence in Gastrointestinal Endoscopy.

Artificial intelligence (AI) is rapidly integrating into modern technology and clinical practice. Although in its nascency, AI has become a hot topic of investigation for applications in clinical practice. Multiple fields of medicine have embraced the possibility of a future with AI assisting in diagnosis and pathology applications. In the field of gastroenterology, AI has been studied as a tool to assist in risk stratification, diagnosis, and pathologic identification. Specifically, AI has become of great interest in endoscopy as a technology with substantial potential to revolutionize the practice of a modern gastroenterologist. From cancer screening to automated report generation, AI has touched upon all aspects of modern endoscopy. Here, we review landmark AI developments in endoscopy. Starting with broad definitions to develop understanding, we will summarize the current state of AI research and its potential applications. With innovation developing rapidly, this article touches upon the remarkable advances in AI-assisted endoscopy since its initial evaluation at the turn of the millennium, and the potential impact these AI models may have on the modern clinical practice. As with any discussion of new technology, its limitations must also be understood to apply clinical AI tools successfully

The Applications of Artificial Intelligence in Managing Project Processes and Targets: A Systematic Analysis

Artificial intelligence (AI) has emerged as the defining technology of the 21st century and has far-reaching impacts on project management (PM). This study assesses the applications of AI in managing project processes and targets through a systematic analysis of publications from 2017 to 2021. The analysis has revealed interesting insights, trends, gaps, and issues. This study informs the researchers and practitioners of the status of AI applications in the management of project processes and targets. It helps stimulate research efforts that can lead to more advances in applying AI to augment PM practices

Role of Artificial Intelligence in Human Resource Management in the Middle East Countries

The primary goal of this article is to contribute to the field of technology adoption research by providing researchers, organizations, HR leaders, service providers, and decision-makers with advanced understanding and valid inputs on the development of AI-based HR solutions and the determinants of adoption. The overall objective of this research is to determine the general attitude of HR managers toward the adoption of AI in HRM and to assess the factors that determine the adoption of AI from the perspective of HR managers. The proposed adoption factors were grouped into four constructs, innovation characteristics, trust, technology-organizational-environment (TOE) factors, and emphasized HR roles within the organization. The research was conducted among HR managers in Middle Eastern countries, specifically Jordan, Kuwait, Saudi Arabia, and Qatar. An online questionnaire was used to collect data from a total of 389 respondents. The results showed that respondents were largely positive toward AI applications in HRM. This positive attitude can be inferred from the mean values of two variables, relative advantage and attitude toward the application of AI in HRM. The research results showed that HR managers have a positive attitude and confidence that emerging AI applications can contribute to supporting the efficiency, effectiveness, and quality of HRM. In addition, the results showed a constructive perception of the relative benefits of AI. Researchers, policymakers, and service providers are also recommended to investigate the phenomenon from two perspectives, first, the impact of attitudes on actual adoption decisions and second, the factors that influence this impact. Keywords: artificial intelligence, HRM, technology adoption, HR leaders, technologicalorganizational- environmenta

3T Framework for AI Adoption in Human Resource Management: A Strategic Assessment Tool of Talent, Trust, and Technology

Artificial intelligence (AI) is steadily entering and transforming the management, work, and organizational ecosystems. We observe AI-based applications assisting employees in daily tasks, project management, decision-making, and collaboration. AI applications are increasingly assisting also Human Re-source Management (HRM) in undertaking time-critical tasks and managerial and administrative decision-making. However, more in-depth and comprehensive studies are needed to understand the specific factors affecting the full adoption of AI technology from a multi-level viewpoint and address the potential limitations of AI appropriation or its adverse outcomes in HRM.The purpose of this study is to investigate the conditions in which human talent may take advantage of the unique opportunities offered by AI. However, whereas previous studies were conducted on the individual perception of AI and technology readiness or adoption, an integrated approach aiming to combine talent management-related dimensions and managerial-related dimensions is still not avail-able. For this research gap, we build a strategic management assessment frame-work of the driving factors of Talent, Trust, and Technology (3T) in AI adoption in HRM. We investigate the impact of these trends on the human-related and technology ecosystems and provide an integrated analysis of individual micro (talent management) organizational macro (trust and technology) adoption of AI technology.The paper advances the current definition and understanding of individual human facilitators and impediments behind the ability to speed up the adoption of AI-based technology. The practical contribution can facilitate the human-centered and trustworthy design and adoption of AI

Expert System Prototype Developments For Nasa-KSC Business And Engineering Applications

Artificial Intelligence (AI) technology, and in particular expert systems — a subset of AI which shows the strongest applicability to a wide variety of environments, has recently emerged from the realm of basic research into that of real-world applications. To further these advances, NASA-Kennedy Space Center (KSC) provided funding and other critical resources to the University of Central Florida (UCF) in support of instruction of expert systems technology. During the Fall 1987 semester, UCF\u27s Colleges of Business and Engineering concurrently offered courses in response to the increased interest in expert system applications and to satisfy the intent of this grant. This paper describes the prototype expert systems which evolved from this sponsorship and the development methods used

Analysis Into Artificial Intelligence And Its Developing Dynamic And Relationship In Agricultural Supply Chains

The thesis explores artificial intelligence (AI) in agricultural (Ag) supply chains (SCs) and presents a new typology to understand artificial intelligence-based solutions in agricultural SCs. The thesis was performed utilizing a research-based review to investigate the current uses of artificial intelligence-based solutions in agricultural SCs. The AI-based solutions were found in case studies that reviewed AI operations in different areas internationally. The typology was formed on the foundation of two dynamics, the location of AI applications in Ag SCs and the driving values to integrate the AI applications. In order to develop the typology, the AI applications were studied in a series of different analyses. The analyses helped to critique and scrutinize the AI applications to gain new perspectives. The series of analyses consists of exploring the AI applications’ location within the supply chain, the value additions to the supply chain from integrating the AI applications, and the resulting depth of the effect of AI application has on the supply chain. Each additional evaluation of the AI applications examining another parameter further exposed more insight and started to build a structured ideology of AI. The proposed typology aims to create a tool of measurement to infer AI technology’s relation in the SCs and create a new viewpoint that will lead investigation and provide insight for predictions of AI’s future in agricultural SCs. In addition, the new typology should aid agriculture firms in understanding and capturing the potential synergies stemming from the driving values of innovation. The study found that AI applications with a strong relationship in the supply chain provide the greatest beneficiary relationship between technology value creation and supply chain logistics. Furthermore, AI applications will have the strongest relationship and implementation when operating in collaboration with other supply chain locations and AI integrated firms. Concluding the thesis, relevant policy and business practice recommendations are proposed

Artificial intelligence and new business models in agriculture: a structured literature review and future research agenda

Purpose Artificial Intelligence (AI) is a growing technology impacting several business fields. The agricultural sector is facing several challenges, which may be supported by the use of such a new advanced technology. The aim of the paper is to map the state-of-the-art of AI applications in agriculture, their advantages, barriers, implications and the ability to lead to new business models, depicting a future research agenda. Design/methodology/approach A structured literature review has been conducted, and 37 contributions have been analyzed and coded using a detailed research framework. Findings Findings underline the multiple uses and advantages of AI in agriculture and the potential impacts for farmers and entrepreneurs, even from a sustainability perspective. Several applications and algorithms are being developed and tested, but many barriers arise, starting from the lack of understanding by farmers and the need for global investments. A collaboration between scholars and practitioners is advocated to share best practices and lead to practical solutions and policies. The promising topic of new business models is still under-investigated and deserves more attention from scholars and practitioners. Originality/value The paper reports the state-of-the-art of AI in agriculture and its impact on the development of new business models. Several new research avenues have been identified