Artificial Intelligence Innovation Research Articles

Accelerating AI Innovation in Healthcare Through Mentorship.

The adoption of Artificial Intelligence (AI) in the Canadian healthcare system falls behind that of other countries. Socio-technological considerations such as organizational readiness and a limited understanding of the technology are a few barriers impeding its adoption. To address this need, this study implemented a five-month AI mentorship program with the primary objective of developing participants' AI toolset. The analysis of our program's effectiveness resulted in recommendations for a successful mentorship and AI development and implementation program. 12 innovators and 11 experts from diverse backgrounds were formally matched and two symposiums were integrated into the program design. 8 interviewed participants revealed positive perceptions of the program underscoring its contribution to their professional development. Recommendations for future programs include: (1) obtaining organizational commitment for each participant; (2) incorporating structural supports throughout the program; and (3) adopting a team-based mentorship approach. The findings of this study offer a foundation rooted in evidence for the formulation of policies necessary to promote the integration of AI in Canada.

A Study on Cultural Heritage Preservation in the Digital Era

In our digitally driven era, the preservation of cultural heritage stands at a critical juncture, necessitating an exploration of the challenges and opportunities associated with this dynamic process. This research paper investigates the evolving landscape of cultural heritage preservation, emphasizing the transformative impact of digitization and the vital role of marketing strategies in advocacy and support. The significance of cultural heritage preservation lies in its ability to safeguard the collective identity of societies. This study emphasizes the role of tangible and intangible cultural assets in maintaining traditions and preserving human history, echoing the sentiments expressed by scholars such as Smith (2016) on the social cohesion that stems from cultural heritage. The advent of digital technologies has ushered in a new era for cultural preservation, offering novel approaches to archiving, restoration, and dissemination. However, this digital transformation presents challenges such as data security, copyright concerns, and addressing the digital divide, as highlighted by Jones (2018). Crucially, marketing strategies play a pivotal role in raising awareness and garnering support for cultural heritage preservation initiatives. Successful case studies, such as the British Museum's digital outreach (Smithsonian, 2020), underscore the potency of digital marketing in the cultural preservation domain. As technology continues to advance, the future of cultural heritage preservation will likely witness innovations in artificial intelligence, virtual reality, and augmented reality. This paper provides insights into these future trends and recommends adaptive strategies for marketers, policymakers, and cultural institutions to foster sustainable preservation efforts. KEY WORDS: cultural heritage preservation, virtual reality (VR), artificial intelligence (AI), tourism, digital reconstruction, technological advancements, transformative impact.

AI for social good and the corporate capture of global development

ABSTRACT This article focuses on the AI for Social Good (AI4SG) movement, which aims to leverage Artificial Intelligence (AI) and Machine Learning (ML) to achieve the United Nations Sustainable Development Goals (UN SDGs). It argues that, through AI4SG, Big Tech is attempting to advance AI-driven technosolutionism within the development policy and scholarly space creating new opportunities for rent extraction. The article situates AI4SG, within the history of ICT4D. It also highlights the contiguity of AI4SG with the so-called 4th Industrial Revolution (4IR), a framework that places AI and other digital innovations at the center of national and international development and industrial policy agendas. By exploring how Big Tech has attempted to depoliticize datafication, we thus suggest that AI4SG and 4IR are mutually reinforcing discourses that serve the purpose of depoliticizing the development arena by bestowing legitimacy and authority to Big Tech to reshape policy spaces and epistemic infrastructures while inserting themselves, to an unprecedented degree, between the citizen (data) and the state (development and policy).

Exploring Cross-Cultural Design in the Evolution of Intelligent English Teaching: The Impact and Innovation of Artificial Intelligence

Exploring Cross-Cultural Design in the Evolution of Intelligent English Teaching: The Impact and Innovation of Artificial Intelligence

Digital education: new paradigms for teaching

From the advent of the internet to the latest innovations in artificial intelligence and virtual reality, it is essential to identify and understand the technological changes shaping this educational landscape. Thus, the general objective of the research is to analyze how the adoption of Digital Education as a new paradigm in teaching impacts the effectiveness of the teaching-learning process, considering the specific challenges faced by educators in the integration of these innovative practices. Regarding the methods, a literature review with a qualitative approach was used. It is concluded, therefore, that Digital Education emerges as a catalyzing tool for a more effective education, preparing students for the challenges of the 21st century.

Exploring Business Potential Through the Application of Artificial Intelligence: Impact Analysis on Operational Efficiency, Decision Making, and Customer Experience

This article investigates the transformative impact of Artificial Intelligence (AI) on business dynamics through multiple linear regression analysis. Using data from PT. Samudera Indonesia Padang Branch, this research evaluates the influence of operational efficiency with the application of AI (X1), decision making supported by AI technology (X2), and efforts to improve customer experience through AI innovation (X3) on the Evaluation of the Impact of AI-Based Marketing Strategy variables (dependent) . The results show a robust model, with an R-square value of 0.783, indicating that approximately 78.3% of the variability in the dependent variable is explained by the AI-driven predictor variables. Each variable demonstrated a positive and substantial impact, emphasizing the importance of AI in improving operational processes, decision making and customer interactions. The high F-square value of 47.342 confirms the statistical significance of the entire model. This study contributes to understanding how AI adoption in business drives innovation and success, providing valuable insights for organizations looking to harness the potential of AI in a rapidly evolving business landscape.

Розвиток інновацій у системі фінансового контролю підприємницьких структур: соціальні проблеми впровадження та правовий статус інформаційних ресурсів

The article is devoted to studying the impact of the rapid development of financial technologies (fintech) on the system of financial control in business structures. The systemic changes in financial services thanks to fintech are highlighted, and its importance and challenges for modern business are emphasized. The article includes research on various fintech innovations, including digital payments, blockchain, artificial intelligence, and data analytics. The work examines social problems associated with introducing innovations and the legal status of information resources involved in financial control. The potential advantages and problems of introducing technological and financial innovations into the financial control system are substantiated. Adhering to ethical principles, including data privacy, transparency, and bias, was emphasized, especially when implementing blockchain and artificial intelligence innovations. The risks and challenges associated with introducing fintech innovations in financial control were revealed, and the authors analyzed social aspects such as resistance to change, uncertainty in new technologies, and information confidentiality. An important consideration is also the legal status of information resources used in financial control. A few limitations and synergistic effects in the financial control of business structures, positive and negative aspects of innovations in the financial control system, and determination of ways to overcome existing challenges have been identified. The methodological basis of the article includes historical and logical methods, legal, ethical, comparative analysis, and data triangulation methods. The historical and logical method is used to analyze the evolution of financial and technological innovation. With the help of legal analysis, normative legal acts relating to the relevant sphere of social relations were studied. The ethical analysis allowed us to identify ethical guidelines for assessing the social and legal consequences of innovations in financial control. Comparative analysis has become the basis for comparing enterprise approaches to introducing innovations into the financial control system. Generalizing situational problems made it possible to evaluate the results of innovation implementation by business structures. The data triangulation method ensured the high reliability of the research results. The integration of these methods made it possible to obtain a comprehensive understanding of innovations in financial control, to study the social problems of their implementation, and to study the legal status of information resources in the context of relevant risks and challenges. Keywords: business structures, innovative development, financial control, social risks, social challenges, information resources, legal information.

The Ethical Considerations of Business Artificial Intelligence Exploration Through the Lenses of the Global AI Technology Acceptance Model

The present study aims to examine the ethical considerations about the exploration of Artificial Intelligence technology. As the field of artificial intelligence (AI) continues to grow, it is important to examine the ethical implications of its use. The Global AI Technology Acceptance Model and Innovation Resistance Theory are two theoretical frameworks that can be used to understand the impact of AI on ethical considerations. By analyzing these frameworks, we can better understand the factors contributing to adopting AI and how ethical concerns can be addressed. This paper aims to explore the intersection of these two theories and their potential implications for ethical considerations in the development and deployment of artificial intelligence. This research contributes to a deeper understanding of the ethical considerations surrounding the use of AI. It provides insights into how we can ensure that AI is used responsibly and ethically. The result of this study is of great importance given the rapid pace of technological advancement and the potential for AI to significantly impact society.

Artificial Intelligence in Remote Monitoring and Telemedicine

Telemedicine, remote monitoring coupled with Artificial Intelligence innovations are redesigning the face of the healthcare sector in record way and increasing satisfaction levels for patients’ clinical enhancements, reduced charges and increased effectiveness in the delivery of services. The applied and advanced AI technologies consist of machine learning, natural language processing, and predictive analytics used in applications for RPM, PT, and VC. In RPM, AI augments the processes of data aggregation and data analysis of patients’ real-time health data from wearable and other digital health technologies. The former capability makes it possible to check for the possible infections, caretaker interferences, and regular management of recurring diseases. For example, AI can help in anticipating incidents such as heart attacks based on previous data of patients, thus preventive care is implemented. On the other hand, AI is incorporated in telemedicine through applying virtual health assistance, diagnostic tools and even chats. With the help of AI, virtual assistants can filter patients’ complaints, give first-stages diagnoses, and suggest necessary treatments, which will decrease loads of clinicians and increase availability of medical services. This review analyses the current uses, advantages and disadvantages of AI in remote observation and m-telemedicine. Here the details what kind of AI technologies implied in different spheres of healthcare, diagnosing, treatment planning, and individual therapies. Also, it examines how the responsible AI should be implemented ethically in a healthcare setting. Moreover, it presents the further prospects of AI in telemedicine which underlines the importance of AI development and enhancement of patient treatment.

How do green intellectual and co-creational capitals drive artificial intelligence innovation and green innovation in start-ups?

PurposeThis research examines the relationship between the green version of intellectual capital (IC) (measured through green versions of human, structural and relational capitals (GHC, GSC and GRC)), co-creational capital (CC), green innovation (GI), technological innovation (TI) (measured through artificial intelligence) and start-up competitive advantage (SCA).Design/methodology/approachAn online questionnaire collected data from 275 participants. To test the hypotheses, the data were analyzed using SmartPLS.FindingsThe results confirmed the positive influence of GSC and CC on TI and GI, GRC with GI and that of GI and TI with SCA. The results also reveal that IC can influence innovation and describe how innovation can drive the competitive advantage (CA) of start-ups.Research limitations/implicationsThis self-report study examines the associations by collecting data at one point in time, which results in methodological limitations regarding the generalization of the results. The second limitation is that the findings are limited to start-ups.Originality/valueThis research work examined a model that combined three components of green IC, customer capital, two forms of innovation and CA. These associations have not been previously examined yet can provide useful insight into what drives green and TIs and how they further influence competitiveness. This study provides unique inferences that improve the value of the literature on IC and innovation, using start-ups as context.

Navigating the perils of artificial intelligence: a focused review on ChatGPT and responsible research and innovation

While the rise of artificial intelligence (AI) tools holds promise for delivering benefits, it is important to acknowledge the associated risks of their deployment. In this article, we conduct a focused literature review to address two central research inquiries concerning ChatGPT and similar AI tools. Firstly, we examine the potential pitfalls linked with the development and implementation of ChatGPT across the individual, organizational, and societal levels. Secondly, we explore the role of a multi-stakeholder responsible research and innovation framework in guiding chatbots’ sustainable development and utilization. Drawing inspiration from responsible research and innovation and stakeholder theory principles, we underscore the necessity of comprehensive ethical guidelines to navigate the design, inception, and utilization of emerging AI innovations. The findings of the focused review shed light on the potential perils of ChatGPT implementation across various societal levels, including issues such as devaluation of relationships, unemployment, privacy concerns, bias, misinformation, and digital inequities. Furthermore, the proposed multi-stakeholder Responsible Research and Innovation framework can empower AI stakeholders to proactively anticipate and deliberate upon AI’s ethical, social, and environmental implications, thus substantially contributing to the pursuit of responsible AI implementation.

A Scanner Darkly: Copyright Liability and Exceptions in Artificial Intelligence Inputs and Outputs

Abstract This article delves into the legal issues surrounding the use of copyrighted works in training artificial intelligence (AI). It examines two critical questions: firstly, whether accessing and analysing copyrighted works for AI training constitutes copyright infringement; and secondly, whether outputs generated by AI from these inputs infringe on copyright. The primary focus is on the United Kingdom’s jurisdiction, with comparative analyses from EU law and a few US cases. The article aims to bring clarity to these multifaceted legal issues by thoroughly exploring the technicalities of machine learning and its implications for ongoing and future litigation. The hypothesis suggests that most inputs may fall under existing exceptions and limitations, but the outputs’ legality may depend on individual case specifics. The conclusion reflects on the inevitable legal challenges that accompany technological advances, as happened in the past with file sharing and the early World Wide Web. The article highlights the current stage of AI technology and law, suggesting that legal precedents or legislative actions might eventually settle disputes. It also emphasizes the importance of finding technological solutions – like metadata standards or proactive initiatives – to balance copyright holders’ rights with AI innovation. The article concludes by acknowledging the irreversible emergence of AI in our lives and the legal system’s need to adapt, offering equitable solutions to copyright holders while fostering technological advancement.

The nexus between Artificial Intelligence and Sustainable Development Goals: A review

This article focuses on the link between Artificial Intelligence and Sustainable Development Goals and, more precisely, how the former can be applied to achieve the latter. The solution to SDG-related issues may be one goal of AI applicability. There have been suggestions that the current status of artificial intelligence (AI) might serve hundreds of millions of people in both rich and developing countries by helping to resolve problems related to all 17 UN Sustainable Development Goals. Examples include helping those who are blind or visually handicapped navigate their environment, detecting victims of sexual exploitation online, supporting relief operations in the event of a disaster, helping to detect diabetes early, and using image scanning to diagnose skin cancer. These are but a few examples of how AI works and how AI innovation can be advantageous. It also reviews how Artificial Intelligence can be applied to protect the environment, food production, the energy industry, and healthcare development.

Navigating Now and Next: Recent Advances and Future Horizons in Robotic Radical Prostatectomy.

Robotic-assisted radical prostatectomy (RARP) has become the leading approach for radical prostatectomy driven by innovations aimed at improving functional and oncological outcomes. The initial advancement in this field was transperitoneal multiport robotics, which has since undergone numerous technical modifications. These enhancements include the development of extraperitoneal, transperineal, and transvesical approaches to radical prostatectomy, greatly facilitated by the advent of the Single Port (SP) robot. This review offers a comprehensive analysis of these evolving techniques and their impact on RARP. Additionally, we explore the transformative role of artificial intelligence (AI) in digitizing robotic prostatectomy. AI advancements, particularly in automated surgical video analysis using computer vision technology, are unprecedented in their scope. These developments hold the potential to revolutionize surgeon feedback and assessment and transform surgical documentation, and they could lay the groundwork for real-time AI decision support during surgical procedures in the future. Furthermore, we discuss future robotic platforms and their potential to further enhance the field of RARP. Overall, the field of minimally invasive radical prostatectomy for prostate cancer has been an incubator of innovation over the last two decades. This review focuses on some recent developments in robotic prostatectomy, provides an overview of the next frontier in AI innovation during prostate cancer surgery, and highlights novel robotic platforms that may play an increasing role in prostate cancer surgery in the future.

The Study on the Impact of Business Artificial Intelligence Innovation on Fair Value Investments in the United States

The purpose of the study is to offer valuable insights into how artificial intelligence is revolutionizing investment practices, and the impact of this transformation on investors, as well as the wider financial market scenario in the United States. The study investigated how the use of advanced AI technologies in business settings affects the valuation and fairness of investments in the United States. The goal of this research is to provide insights into how AI can influence financial decision-making and improve investment outcomes. The study findings suggest that AI possesses the potential to influence investor behavior, as AI-powered analytics and robot-advisors continue to gain prominence in guiding investment decisions. The increasing integration of AI in business practices raises ethical and regulatory concerns that impact public perception and the regulatory landscape, thereby affecting investment values. AI-based tools can process vast amounts of data accurately and quickly, enabling identification of investment opportunities, risks, and trends more efficiently than traditional methods. This, in turn, could foster better investment decisions and potentially higher returns.

The Legality of Intellectual Property by Artificial Intelligence in Indonesia

This study aims to examine the legitimacy of intellectual property generated by artificial intelligence (AI) in Indonesia. With the rapid development of AI, intellectual property generated by AI has become a complex issue, attracting the attention of legal experts and stakeholders. Although the legal framework in Indonesia does not specifically address intellectual property generated by AI, certain aspects of existing law may apply in this context. This study uses normative or doctrinal research methods to analyze the legal framework in Indonesia regarding intellectual property, including copyright law, patent law, industrial design law, and trade secret protection. In addition, this research examines the views of legal experts and stakeholders in Indonesia regarding the legitimacy of intellectual property generated by AI. The results of this research are expected to provide a clearer understanding of the legality of intellectual property law produced by AI in Indonesia. Research results will contribute to discussions around AIrelated laws and regulations and may serve as the basis for future regulatory changes or adjustments. This study has significant implications for stakeholders, including AI creators, users, and developers in Indonesia. By understanding the applicable legal framework, appropriate legal protection frameworks can be created for AI-generated intellectual property, promoting continuous innovation and responsible utilization of AI.
 Keywords: artificial intelligence, intellectual property, legality

Mapping the conceptual structure of innovation in artificial intelligence research: A bibliometric analysis and systematic literature review

This study uses bibliometric analysis and a systematic literature review to map the conceptual structure of artificial intelligence innovations (AI-I) in the social sciences between 2000 and 2023. It explicitly focuses on non-economic aspects conducive to AI-I, namely social, technological, cultural, sustainable, personal, moral, and ethical. Our analysis reveals that 1225 articles and proceeding papers have been published, and terms such as “technology,” “big data,” “management,” “performance,” “future,” and “impact” are the most frequently used when discussing innovation and AI. According to our time-zone analysis, the last two years have shown a significant emphasis on concepts such as “transformation,” “corporate social responsibility,” and “resource-based view.” In terms of citations, the countries that receive the highest number of references in the AI-I field are the United Kingdom, the United States, Germany, Australia, and China. The most prolific authors in terms of publications are David Teece, Erik Brynjolfsson, and Anjan Chatterjee. Given that most studies highlight the economic side of AI-I, we selected the most prolific 163 articles from all social science research areas. These studies legitimize the main non-economic aspects that highlight both certainties and uncertainties conducive to such innovations. Although the technological component is the most popular in our analysis of the non-economic aspects of the AI-I subfield, we find an important emphasis on ethical/moral dimensions conducive to slow innovation principles. We also observe a growing interest in the cultural dimension, specifically exploring potential factors that can lead to better human acceptance of these innovations.

How do corporate governance, artificial intelligence, and innovation interact? Findings from different industries

Research in the field of corporate governance has been exhaustive, and recently many scholars have focused on the relationship between corporate governance attributes and artificial intelligence (AI), corporate governance attributes, and corporate innovation (Asensio-López et al., 2019), however, there are few studies that combine corporate governance, AI, and corporate innovation. This article examines the relationships among corporate governance attributes, AI, and corporate innovation. Adopting a new perspective, we have tried to help resolve this issue using a content-analysis that integrates data from over 50 companies that trade on National Association of Securities Dealers Automated Quotations (NASDAQ) to analyze the relationship between board attributes, the practice of AI and firm innovation for the time 2018–2022. The results suggest that particular aspects of boards, such as board size, board diversity, and ownership concentration show significant correlations with firm AI development and innovation for overall industries, but the levels of associations also vary depending on different innovation measurements and samples considered in specific industries. Corporate governance has more significant variables in the manufacturing and information technology service industries. Moreover, the mediating effects of AI and innovation are examined, respectively. This research offers implications to corporate decision-makers as to how to proceed if the intent is to offer commercialized AI advancements and successful breakthrough innovations.

Present and Future Innovations in AI and Cardiac MRI.

Cardiac MRI is used to diagnose and treat patients with a multitude of cardiovascular diseases. Despite the growth of clinical cardiac MRI, complicated image prescriptions and long acquisition protocols limit the specialty and restrain its impact on the practice of medicine. Artificial intelligence (AI)-the ability to mimic human intelligence in learning and performing tasks-will impact nearly all aspects of MRI. Deep learning (DL) primarily uses an artificial neural network to learn a specific task from example data sets. Self-driving scanners are increasingly available, where AI automatically controls cardiac image prescriptions. These scanners offer faster image collection with higher spatial and temporal resolution, eliminating the need for cardiac triggering or breath holding. In the future, fully automated inline image analysis will most likely provide all contour drawings and initial measurements to the reader. Advanced analysis using radiomic or DL features may provide new insights and information not typically extracted in the current analysis workflow. AI may further help integrate these features with clinical, genetic, wearable-device, and "omics" data to improve patient outcomes. This article presents an overview of AI and its application in cardiac MRI, including in image acquisition, reconstruction, and processing, and opportunities for more personalized cardiovascular care through extraction of novel imaging markers.

Integration and Innovation of Artificial Intelligence and Traditional English Translation Methods

Abstract In this study, we employ autoregressive and neural machine translation models to process natural language. Our exploration of AI translation workflows encompasses three key aspects: the encoder-decoder framework, text feature representation, and translation derivation methodologies. Subsequently, we identify the core evaluation indices essential for assessing intelligent translation systems. An innovative translation model is constructed by amalgamating AI-driven and traditional English translation techniques. This model is scrutinized for its effectiveness through various lenses, including the quality of English-to-Chinese poetry translations and manual evaluations. Comparative analysis of the proposed translation method with other extant methods across diverse datasets reveals superior performance metrics: the BLEU score consistently exceeds 4.5 across all three test sets, and the METEOR score ranges between 4.3 and 4.6, surpassing competing methods. Additionally, translation accuracy for sentences of varying lengths in the source language is maintained between 85% and 96%. The objective of this research is to conduct a comprehensive examination of the integration of AI English translation with conventional methodologies, aiming to foster innovative theoretical contributions and practical advancements in the field.