Artificial Intelligence Innovation Research Articles

A COMPARATIVE STUDY REGARDING THE PERCEPTION OF PHYSICAL THERAPIST AND PHYSICAL THERAPY STUDENTS ABOUT USABILITY OF ARTIFICIAL INTELLIGENCE CHATBOT I.E, OPENAI-CHATGPT FOR THE RETRIEVAL OF INFORMATION IN ACADEMIC AND CLINICAL SETTINGS

In the modern era dominated by technological advancements, artificial intelligence (AI) has transcended the realm of science fiction to become a pivotal aspect of various fields, including health sciences. AI innovations like ChatGPT, an AI-based chatbot, have sparked discussions regarding their practical applications in healthcare and academia. Objectives: The primary objective of this study was to assess and compare the perceptions of physical therapists and physical therapy students regarding the usability of ChatGPT for information retrieval in academic and clinical settings. Method: A cross-sectional study design was employed. The sample size, calculated to be 176 using G*Power analysis, was equally divided into two groups: 88 physical therapists and 88 physical therapy students. Data collection was conducted via Google Forms and manual questionnaires. Result: Out of the respondents, 39 (47.6%) physical therapy students and 43 (52.4%) physical therapists reported that they perceived ChatGPT and related AI technologies as effective tools for enhancing job performance and academic studies. Additionally, 49 physical therapy students and 52 physical therapists found AI chatbots easy to use. However, statistical analysis revealed no significant difference (p > 0.05) in the perceptions of usability between the two groups. Conclusion: The study concluded that physical therapists and physical therapy students share similar perceptions regarding the usability of AI chatbots like ChatGPT for information retrieval in clinical and academic settings. A significant majority of both groups acknowledged the potential benefits of these technologies, highlighting their applicability in professional and educational environments.

Identifying Bulls and bears? A bibliometric review of applying artificial intelligence innovations for stock market prediction

The literature on stock forecasting using the innovative technique of Artificial Intelligence (AI) has become overwhelming, making it quite challenging for academics and relevant researchers to gain an elaborative, structured, and organised overview of the relevant information. We fill this gap by contributing and conducting a robust bibliometric review on the application of AI innovations for stock market prediction. More specifically, we conducted a bibliometric review by identifying 241 relevant papers related to stock forecasting using AI by taking a quantitative approach. A quantitative approach uses an examination of linked articles to look at the development of research topics and the structure of existing knowledge. We identified five significant themes based on exploratory factor and hierarchical cluster analyses. We posited that successful AI-based models could aid stock traders, brokers, and investors in better decision-making, a task that had previously been fraught with difficulties. Overall, this paper is aimed at benefiting stock traders, brokers, businesses, investors, government, financial institutions, depositories, and banks. This paper concludes with a future research agenda.

ARTIFICIAL INTELLIGENCE: LEGAL IMPLICATIONS AND CHALLENGES

Artificial intelligence (AI) technologies are rapidly transforming various aspects of society, from healthcare and finance to transportation and education. While AI offers tremendous potential for innovation and efficiency, its widespread adoption raises significant legal implications and challenges. This paper examines the legal landscape surrounding AI, focusing on key areas such as privacy, liability, intellectual property, and employment law. One of the primary concerns with AI is the privacy implications stemming from the collection, storage, and analysis of vast amounts of data. Regulations such as the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States aim to safeguard individuals' privacy rights and impose strict requirements on data handling practices. Another critical area of concern is liability, particularly regarding the accountability for AI-driven decisions that may result in harm to individuals or entities. Questions arise about who should be held responsible for such decisions the developers, users, or the AI systems themselves. Furthermore, AI raises complex issues related to intellectual property, including the ownership of AI-generated works, patentability of AI algorithms, and the protection of AI innovations. Additionally, the integration of AI into the workforce raises questions about the future of employment, job displacement, and the need for new regulations to protect workers in the age of automation. In conclusion, while AI presents unprecedented opportunities for advancement, it also poses significant legal challenges that require careful consideration and proactive regulation. Policymakers, legal professionals, and stakeholders must collaborate to develop frameworks that promote the responsible development, deployment, and regulation of AI technologies while safeguarding individual rights, privacy, and societal values.

Perspectives on Artificial Intelligence in Nursing in Asia.

Artificial intelligence (AI) is reshaping health care, including nursing, across Asia, presenting opportunities to improve patient care and outcomes. This viewpoint presents our perspective and interpretation of the current AI landscape, acknowledging its evolution driven by enhanced processing capabilities, extensive data sets, and refined algorithms. Notable applications in countries such as Singapore, South Korea, Japan, and China showcase the integration of AI-powered technologies such as chatbots, virtual assistants, data mining, and automated risk assessment systems. This paper further explores the transformative impact of AI on nursing education, emphasizing personalized learning, adaptive approaches, and AI-enriched simulation tools, and discusses the opportunities and challenges of these developments. We argue for the harmonious coexistence of traditional nursing values with AI innovations, marking a significant stride toward a promising health care future in Asia.

Pushing Boundaries: AI and Computer Science in the Era of Technological Revolution

The advent of Artificial Intelligence (AI) has heralded a transformative era in Computer Science, revolutionizing various facets of technology. This paper explores the profound impact of AI on the field of Computer Science, delving into its advancements, innovations, and the potential future trajectory of technology. The primary objective of this paper is to elucidate the significant role of AI in shaping the landscape of Computer Science. Through comprehensive research and analysis, it aims to provide insights into the evolution of AI, its applications, and the implications for future technological developments. This study employs a library research approach, gathering information from academic journals, conference proceedings, books, and reputable online sources. By synthesizing existing literature and scholarly works, it seeks to construct a comprehensive overview of the advancements and innovations in AI within the realm of Computer Science. The findings reveal a dynamic and rapidly evolving field driven by AI technologies. From machine learning algorithms to neural networks and deep learning models, AI has revolutionized data analysis, pattern recognition, and decision-making processes. Moreover, AI-driven applications such as natural language processing, computer vision, and robotics have reshaped various industries, including healthcare, finance, transportation, and manufacturing. However, alongside these advancements come ethical considerations, privacy concerns, and challenges related to algorithmic biases and societal implications. Looking ahead, the future of technology promises further integration of AI into various domains, leading to unprecedented opportunities and challenges in the realm of Computer Science

How Artificial Intelligence Innovations in Journalism Can Overcome the Digital Divide

How Artificial Intelligence Innovations in Journalism Can Overcome the Digital Divide

To be an eco- and tech-friendly society: Impact research of green finance on AI innovation

Green finance can effectively support industrial upgrading and technology development, such as artificial intelligence (AI). In this paper, we attempt to pursue the possibility of a win-win situation for achieving an eco- and tech-friendly society. By asking where and how we could develop green finance and AI innovation simultaneously, this paper conducts an empirical investigation on the influence of green finance on the development of AI in China from 2011 to 2020. Based on the panel data from the 30 provinces, we introduce spatial measurement, policy effect, heterogeneity, and threshold analyses to present deeper insights into the impact of green finance on AI innovations. Indeed, green finance could promote the progress of AI innovation. China's green finance pilot policy is verified to promote the progress of local AI innovations. Furthermore, the spatial spillover effect and regional heterogeneity are observed as well. The promotion effect is most significant in the western area, where the green finance index is relatively low. Besides, the threshold analysis also considers how to increase the marginal effect of green finance in different areas. Finally, several policy recommendations are proposed, which contribute to providing specific directions for the policymakers to improve AI innovation and achieve sustainable development at the same time.

Text-to-video generative artificial intelligence: sora in neurosurgery.

Artificial intelligence (AI) has increased in popularity in neurosurgery, with recent interest in generative AI algorithms such as the Large Language Model (LLM) ChatGPT. Sora, an innovation in generative AI, leverages natural language processing, deep learning, and computer vision to generate impressive videos from text prompts. This new tool has many potential applications in neurosurgery. These include patient education, public health, surgical training and planning, and research dissemination. However, there are considerable limitations to the current model such as physically implausible motion generation, spontaneous generation of subjects, unnatural object morphing, inaccurate physical interactions, and abnormal behavior presentation when many subjects are generated. Other typical concerns are with respect to patient privacy, bias, and ethics. Further, appropriate investigation is required to determine how effective generative videos are compared to their non-generated counterparts, irrespective of any limitations. Despite these challenges, Sora and other iterations of its text-to-video generative application may have many benefits to the neurosurgical community.

Research Trends in Artificial Intelligence and Security—Bibliometric Analysis

This paper provides a bibliometric analysis of current research trends in the field of artificial intelligence (AI), focusing on key topics such as deep learning, machine learning, and security in AI. Through the lens of bibliometric analysis, we explore publications published from 2020 to 2024, using primary data from the Clarivate Analytics Web of Science Core Collection. The analysis includes the distribution of studies by year, the number of studies and citation rankings in journals, and the identification of leading countries, institutions, and authors in the field of AI research. Additionally, we investigate the distribution of studies by Web of Science categories, authors, affiliations, publication years, countries/regions, publishers, research areas, and citations per year. Key findings indicate a continued growth of interest in topics such as deep learning, machine learning, and security in AI over the past few years. We also identify leading countries and institutions active in researching this area. Awareness of data security is essential for the responsible application of AI technologies. Robust security frameworks are important to mitigate risks associated with AI integration into critical infrastructure such as healthcare and finance. Ensuring the integrity and confidentiality of data managed by AI systems is not only a technical challenge but also a societal necessity, demanding interdisciplinary collaboration and policy development. This analysis provides a deeper understanding of the current state of research in the field of AI and identifies key areas for further research and innovation. Furthermore, these findings may be valuable to practitioners and decision-makers seeking to understand current trends and innovations in AI to enhance their business processes and practices.

On manipulation by emotional AI: UK adults' views and governance implications.

With growing commercial, regulatory and scholarly interest in use of Artificial Intelligence (AI) to profile and interact with human emotion ("emotional AI"), attention is turning to its capacity for manipulating people, relating to factors impacting on a person's decisions and behavior. Given prior social disquiet about AI and profiling technologies, surprisingly little is known on people's views on the benefits and harms of emotional AI technologies, especially their capacity for manipulation. This matters because regulators of AI (such as in the European Union and the UK) wish to stimulate AI innovation, minimize harms and build public trust in these systems, but to do so they should understand the public's expectations. Addressing this, we ascertain UK adults' perspectives on the potential of emotional AI technologies for manipulating people through a two-stage study. Stage One (the qualitative phase) uses design fiction principles to generate adequate understanding and informed discussion in 10 focus groups with diverse participants (n = 46) on how emotional AI technologies may be used in a range of mundane, everyday settings. The focus groups primarily flagged concerns about manipulation in two settings: emotion profiling in social media (involving deepfakes, false information and conspiracy theories), and emotion profiling in child oriented "emotoys" (where the toy responds to the child's facial and verbal expressions). In both these settings, participants express concerns that emotion profiling covertly exploits users' cognitive or affective weaknesses and vulnerabilities; additionally, in the social media setting, participants express concerns that emotion profiling damages people's capacity for rational thought and action. To explore these insights at a larger scale, Stage Two (the quantitative phase), conducts a UK-wide, demographically representative national survey (n = 2,068) on attitudes toward emotional AI. Taking care to avoid leading and dystopian framings of emotional AI, we find that large majorities express concern about the potential for being manipulated through social media and emotoys. In addition to signaling need for civic protections and practical means of ensuring trust in emerging technologies, the research also leads us to provide a policy-friendly subdivision of what is meant by manipulation through emotional AI and related technologies.

Exploiting the margin: How capitalism fuels AI at the expense of minoritized groups

AbstractThis paper explores the intricate relationship between capitalism, racial injustice, and artificial intelligence (AI), arguing that AI acts as a contemporary vehicle for age-old forms of exploitation. By linking historical patterns of racial and economic oppression with current AI practices, this study illustrates how modern technology perpetuates and deepens societal inequalities. It specifically examines how AI is implicated in the exploitation of marginalized communities through underpaid labor in the gig economy, the perpetuation of biases in algorithmic decision-making, and the reinforcement of systemic barriers that prevent these groups from benefiting equitably from technological advances. Furthermore, the paper discusses the role of AI in extending and intensifying the social, economic, and psychological burdens faced by these communities, highlighting the problematic use of AI in surveillance, law enforcement, and mental health contexts. The analysis concludes with a call for transformative changes in how AI is developed and deployed. Advocating for a reevaluation of the values driving AI innovation, the paper promotes an approach that integrates social justice and equity into the core of technological design and policy. This shift is crucial for ensuring that AI serves as a tool for societal improvement, fostering empowerment and healing rather than deepening existing divides.

Understanding AI innovation contexts: a review and content analysis of artificial intelligence and entrepreneurial ecosystems research

PurposeAn emerging research stream focuses on the place-based ecosystems where artificial intelligence (AI) innovations emerge and develop. This literature builds on the contextual turn in management research and, specifically, work on entrepreneurial ecosystems. However, as a nascent research area, the literature on AI and entrepreneurial ecosystems is fragmented across academic and practitioner boundaries and unconnected disciplines because of disparate and ill-defined concepts. As a result, the literature is disorganized and its main insights are latent. The purpose of this paper is to synthesize research on AI ecosystems and identify the main insights.Design/methodology/approachWe first consolidate research on the “where” of AI innovation through a scoping review. To address the fragmentation in the literature and understand how entrepreneurial ecosystems are associated with AI innovation, we then use content analysis to explore the literature.FindingsWe identify the main characteristics of the AI and ecosystems literature and the key dimensions of “AI entrepreneurial ecosystems”: the local actors and factors in geographic territories that are coordinated to support the creation and development of AI technologies. We clarify the relationships among AI technologies and ecosystem dimensions and uncover the latent themes and underlying structure of research on AI entrepreneurial ecosystems.Originality/valueWe increase conceptual precision by introducing and defining an umbrella concept—AI entrepreneurial ecosystem—and propose a research agenda to spur further insights. Our analysis contributes to research at the intersection of management, information systems, and entrepreneurship and creates actionable insights for practitioners influenced by the geographic agglomeration of AI innovation.

MIECF: Multi-faceted information extraction and cross-mixture fusion for multimodal aspect-based sentiment analysis

Aspect-level sentiment analysis within multimodal contexts, focusing on the precise identification and interpretation of sentiment attitudes linked to the target aspect across diverse data modalities, remains a focal research area that perpetuates the advancement of discourse and innovation in artificial intelligence. However, most existing methods tend to focus on extracting visual features from only one facet, such as face expression, which ignores the value of information from other key facets, such as the textual information presented by the image modality, resulting in information loss. To overcome the aforementioned constraint, we put forth a novel approach designated as Multi-faceted Information Extraction and Cross-mixture Fusion (MIECF) for Multimodal Aspect-based Sentiment Analysis. Our approach captures more comprehensive visual information in the image and integrates these local and global key features from multiple facets. Local features, such as facial expressions and textual features, provide direct and rich emotional cues. By contrast, the global feature often reflects the overall emotional atmosphere and context. To enhance the visual representation, we designed a Cross-mixture Fusion method to integrate this local and global multimodal information. In particular, the method establishes semantic relationships between local and global features to eliminate ambiguity brought by single-facet information and achieve more accurate contextual understanding, providing a richer and more precise manner for sentiment analysis. The experimental findings indicate that our proposed approach achieves a leading level of performance, resulting in an Accuracy of 79.65 % on the Twitter-2015 dataset, and Macro-F1 scores of 75.90 % and 73.11 % for the Twitter-2015 and Twitter-2017 datasets, respectively.

Adapting the labor market to artificial intelligence innovations

Adapting the labor market to artificial intelligence innovations

AI innovations and financial performance: An examination of patent filings and revenue generation

Artificial intelligence (AI) is increasingly becoming indispensable for businesses, offering numerous advantages to compete in today's dynamic market landscape. This study investigates the relationship between AI patent filings and firms' performance in revenue generation from a point of view, aiming to elucidate the impact of AI investments on revenue generation. Leveraging data from The United States Patent and Trademark Office (USPTO) and employing regression analysis, the research finds a significant correlation between firms with AI patent applications and their generated revenue. The findings underscore the importance of AI investments for financial outcomes and provide valuable empirical evidence for strategic decision-making by businesses and policymakers. Additionally, the methodological approach adopted in the study contributes to the methodological approach available for future research in the field of AI and business performance analysis.

The evolution of AI: What does the future hold in the next two years

Artificial intelligence (AI) has evolved rapidly in recent years, transforming several industries and reshaping cultural norms. This article examines the current state of AI technology, focusing on significant trends and developments that have accelerated its growth. This paper sheds light on the evolving environment of AI innovation by examining current breakthroughs in machine learning, deep learning, and ethical concerns surrounding AI deployment. The following two years should see substantial advances in natural language processing, computer vision, and autonomous systems. Despite the excitement, difficulties such as ethical considerations, regulatory frameworks, and societal repercussions loom large, needing careful study and decisive action. This study aims to inform stakeholders about the transformative potential of AI and the methods required to properly navigate its growing landscape by conducting a balanced examination of opportunities and challenges.

Bridging the ‘Concept–Product’ gap in new product development: Emerging insights from the application of artificial intelligence in FinTech SMEs

Building on the literature on the concept-product gap in new product development, we examine how FinTech SMEs are developing Artificial Intelligence (AI)-based innovations and which organisational or project factors best contribute to the acceleration of AI innovation. The empirical evidence collected from interviews with key stakeholders, practitioners’ forums, and public company documents yields two distinct approaches that differ in their potential for accelerating innovation and reducing the concept-product gap. From a contingency perspective, these two approaches are expanded into four distinct development process configurations, contingent on the business development stage, reliance on 3rd party platforms, availability of high volumes of data, investment level, organisational agility, and level of novelty. The resulting process typology could be used as a diagnostic tool for FinTech SMEs interested in effectively leveraging AI innovation. Using contingency theory, we further develop these insights into a new theoretical framework to explain how AI innovation development unfolds in FinTech SMEs and the rationale for different implementations. Our new process typology and theoretical model can help researchers investigate the mechanisms underlying technological innovation processes. We further identify the specific reasons why the potential of AI for creating new services and disrupting incumbents via digital startups has not been fully realised even in contexts with significant investment and support from public and private business development programmes. This field is still rapidly evolving, and thus, new areas for future research are also highlighted.

Navigating the decision-making landscape of AI in risk finance: Techno-accountability unveiled.

The integration of artificial intelligence (AI) systems has ushered in a profound transformation. This conversion is marked by revolutionary extrapolative capabilities, a shift toward data-centric decision-making processes, and the enhancement of tools for managing risks. However, the adoption of these AI innovations has sparked controversy due to their unpredictable and opaque disposition. This study employs the transactional stress model to empirically investigate how six technological stressors (techno-stressors) impact both techno-eustress (positive stress) and techno-distress (negative stress) experienced by finance professionals and experts. To collect data for this research, an e-survey was distributed to a diverse group of 251 participants from various sources. The findings, particularly the identification and development of techno-accountability as a significant factor, contribute to the risk analysis domain by improving the failure mode and effect analysis framework to better fit the rapidly evolving landscape of AI-driven innovations.

The advancement of artificial intelligence in biomedical research and health innovation: challenges and opportunities in emerging economies

The advancement of artificial intelligence (AI), algorithm optimization and high-throughput experiments has enabled scientists to accelerate the discovery of new chemicals and materials with unprecedented efficiency, resilience and precision. Over the recent years, the so-called autonomous experimentation (AE) systems are featured as key AI innovation to enhance and accelerate research and development (R&D). Also known as self-driving laboratories or materials acceleration platforms, AE systems are digital platforms capable of running a large number of experiments autonomously. Those systems are rapidly impacting biomedical research and clinical innovation, in areas such as drug discovery, nanomedicine, precision oncology, and others. As it is expected that AE will impact healthcare innovation from local to global levels, its implications for science and technology in emerging economies should be examined. By examining the increasing relevance of AE in contemporary R&D activities, this article aims to explore the advancement of artificial intelligence in biomedical research and health innovation, highlighting its implications, challenges and opportunities in emerging economies. AE presents an opportunity for stakeholders from emerging economies to co-produce the global knowledge landscape of AI in health. However, asymmetries in R&D capabilities should be acknowledged since emerging economies suffers from inadequacies and discontinuities in resources and funding. The establishment of decentralized AE infrastructures could support stakeholders to overcome local restrictions and opens venues for more culturally diverse, equitable, and trustworthy development of AI in health-related R&D through meaningful partnerships and engagement. Collaborations with innovators from emerging economies could facilitate anticipation of fiscal pressures in science and technology policies, obsolescence of knowledge infrastructures, ethical and regulatory policy lag, and other issues present in the Global South. Also, improving cultural and geographical representativeness of AE contributes to foster the diffusion and acceptance of AI in health-related R&D worldwide. Institutional preparedness is critical and could enable stakeholders to navigate opportunities of AI in biomedical research and health innovation in the coming years.

Summary of Research on Artificial Intelligence Innovation and Digital Rural Construction in the Yellow River Basin

In the Outline of Ecological Protection and High quality Development Plan for the Yellow River Basin approved at the 2020 conference, innovative concepts are integrated throughout the entire process, and the development of the Yellow River Basin will collide with innovation to create new sparks. This article systematically reviews the current research on digital rural construction and explores the relationship between artificial intelligence innovation and digital rural construction. Based on the research on relevant issues in the Yellow River Basin, it is pointed out that in the future, more attention should be paid to the coupling and coordination relationship between artificial intelligence innovation and digital rural construction in the basin.