Ethics Education in Neonatology: Integrating Theory, Multimodal Methods, and AI Innovation.

Although the prevalence of artificial intelligence (AI) innovations is on the rise, firms frequently report failures and setbacks in their development and implementation of AI innovation efforts. One common issue behind many failing AI initiatives is that they are organized just like other information technology (IT) innovation efforts. To elucidate why and how the production of AI and IT innovations may need to be managed differently, this study juxtaposes these two types of innovations based on two key dimensions of the Schumpeterian framework: the form (product vs. process) and magnitude (radical vs. incremental) of innovations. By analyzing a matched sample of AI and IT patents, we found robust evidence that AI innovations are less radical and more process oriented than comparable IT innovations. Drawing upon our empirical discovery, we developed a conceptual framework to suggest a new way to think about organizing AI innovation. Our research contributes to the literature and practice on AI innovation by illuminating the comparative differences between AI innovations and other IT innovations and advancing a set of empirically derived propositions on how firms may be able to better manage their AI innovation activities.

Artificial intelligence (AI) innovation offers promising opportunities for underperforming firms but entails risks of failure. Whether such firms should proactively increase or conservatively decrease their AI engagement remains a controversial but important issue. This paper combines the attention-based view and resource orchestration theory to investigate this issue using panel data on Chinese A-share listed companies from 2008 to 2022. Our results reveal that performance shortfalls significantly inhibit AI innovation. The mechanism analysis confirms that performance shortfalls negatively affect AI innovation by reducing executive attention to AI. The moderation analysis indicates that government support for AI can mitigate this inhibitory effect. Heterogeneity tests reveal that the inhibitory effect is weaker when firms have more abundant external AI resources and stronger risk-taking internal governance. Further analysis shows that performance shortfalls negatively affect various R&D models for AI innovation and different types of AI technologies. Nevertheless, underperforming firms can enhance their long-term profitability by continuing AI innovation. These findings provide theoretical and practical guidance for underperforming firms in making AI innovation decisions.

Artificial intelligence (AI), commonly defined as “a system’s ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation,” can be classified into analytical, human-inspired, and humanized AI depending upon its application of cognitive, emotional, and social intelligence. AI’s foundations took place in the 1950s. A sequence of vicissitudes of funding, interest in, and support for AI followed subsequently. In 2015 AlphaGo, Google’s AI-driven system, won against the human grandmaster in the highly complex board game Go. This is considered one of the most significant milestones in the development of AI and marks the starting of a new period, enabling several AI innovations in a variety of sectors and industries. Higher education, the fashion industry, and the arts serve as illustrations of areas wherein ample innovation based on AI occurs. Using these domains, various angles of innovation in AI can be presented and decrypted. AI innovation in higher education, for example, indicates that at some point, AI-powered robots might take over the role of human teachers. For the moment, however, AI in academia is solely used to support human beings, not to replace them. The apparel industry, specifically fast fashion—one of the planet’s biggest polluters—shows how innovation in AI can help the sector move toward sustainability and eco-responsibility through, among other ways, improved forecasting, increased customer satisfaction, and more efficient supply chain management. An analysis of AI-driven novelty in the arts, notably in museums, shows that developing highly innovative, AI-based solutions might be a necessity for the survival of a strongly declining cultural sector. These examples all show the role AI already plays in these sectors and its likely importance in their respective futures. While AI applications imply many improvements for academia, the apparel industry, and the arts, it should come as no surprise that it also has several drawbacks. Enforcing laws and regulations concerning AI is critical in order to avoid its adverse effects. Ethics and the ethical behavior of managers and leaders in various sectors and industries is likewise crucial. Education will play an additional significant role in helping AI positively influence economies and societies worldwide. Finally, international entente (i.e., the cooperation of the world’s biggest economies and nations) must take place to ensure AI’s benefit to humanity and civilization. Therefore, these challenges and areas (i.e., enforcement, ethics, education, and entente) can be summarized as the four summons of AI.

This study investigates the impact of artificial intelligence (AI) innovation on economic growth in East Asia (China, Japan, and South Korea) from 2010 to 2023, using AI patent filings as a proxy for technological advancement. A panel data approach is employed, incorporating fixed effects, random effects, and pooled ordinary least squares (OLS) models to examine the relationship between AI innovation and GDP growth. Panel cointegration tests assess long-run equilibrium relationships, while the Granger non-causality test determines the direction of causality. The results indicate that AI innovation significantly contributes to GDP growth, reinforcing the role of technological progress in economic expansion. Trade openness is also positively associated with economic performance. However, gross capital formation exhibits a counterintuitive negative effect, suggesting inefficiencies in capital allocation or diminishing returns. Inflation has a mild yet statistically significant impact on growth. This study provides empirical evidence on the role of AI-driven innovation in shaping East Asian economies. The findings offer valuable insights for policymakers, emphasizing the need for strategies that enhance AI adoption, optimize capital investment, and leverage trade to sustain economic growth in the AI era.

The use of artificial intelligence (AI) in medicine, potentially leading to substantial advancements such as improved diagnostics, has been of increasing scientific and societal interest in recent years. However, the use of AI raises new ethical challenges, such as an increased risk of bias and potential discrimination against patients, as well as misdiagnoses potentially leading to over- or underdiagnosis with substantial consequences for patients. Recognizing these challenges, current research underscores the importance of integrating AI ethics into medical education. This viewpoint paper aims to introduce a comprehensive set of ethical principles for teaching AI ethics in medical education. This dynamic and principle-based approach is designed to be adaptive and comprehensive, addressing not only the current but also emerging ethical challenges associated with the use of AI in medicine. This study conducts a theoretical analysis of the current academic discourse on AI ethics in medical education, identifying potential gaps and limitations. The inherent interconnectivity and interdisciplinary nature of these anticipated challenges are illustrated through a focused discussion on “informed consent” in the context of AI in medicine and medical education. This paper proposes a principle-based approach to AI ethics education, building on the 4 principles of medical ethics—autonomy, beneficence, nonmaleficence, and justice—and extending them by integrating 3 public health ethics principles—efficiency, common good orientation, and proportionality. The principle-based approach to teaching AI ethics in medical education proposed in this study offers a foundational framework for addressing the anticipated ethical challenges of using AI in medicine, recommended in the current academic discourse. By incorporating the 3 principles of public health ethics, this principle-based approach ensures that medical ethics education remains relevant and responsive to the dynamic landscape of AI integration in medicine. As the advancement of AI technologies in medicine is expected to increase, medical ethics education must adapt and evolve accordingly. The proposed principle-based approach for teaching AI ethics in medical education provides an important foundation to ensure that future medical professionals are not only aware of the ethical dimensions of AI in medicine but also equipped to make informed ethical decisions in their practice. Future research is required to develop problem-based and competency-oriented learning objectives and educational content for the proposed principle-based approach to teaching AI ethics in medical education.

Purpose This study aims to examine the impact of artificial intelligence (AI) innovation on debt default risk of construction firms. It also explores the moderating role of intellectual capital. Design/methodology/approach This study analyzes 846 firm-year observations from Chinese listed construction firms between 2007 and 2022. A two-way fixed-effects model is used to test the proposed hypotheses. Findings The findings of this study are as follows: (1) AI innovation significantly reduces debt default risk of construction firms. (2) The three dimensions of intellectual capital (i.e. human capital, structural capital, and relational capital) strengthen the mitigating effect of AI innovation on debt default risk, with human capital playing the most significant role. (3) AI innovation mitigates debt default risk through internal operational optimization and external relationship coordination. Practical implications This study offers empirical evidence on the negative AI innovation–debt default risk relationship of construction firms. It also provides actionable guidance for construction managers to improve the effectiveness of AI innovation by fostering the development of intellectual capital. Originality/value By providing empirical evidence for the mitigating effect of AI innovation on debt default risk of construction firms, this study fills a critical gap in the literature of AI innovation and construction risk management. It also provides academics and practitioners with novel insights for synergistically leveraging AI innovation and intellectual capital to manage debt default risk in the construction industry.

Purpose This study aims to examine how artificial intelligence (AI) innovation influences open innovation activities in new ventures, with a focus on the mediating role of innovation niches and the alignment between inbound and outbound open innovation in shaping entrepreneurial performance. Despite growing interest in AI-driven strategies, limited research explores these interactions in the context of emerging economies. Design/methodology/approach Using survey data from 329 high-tech venture founders in Vietnam, the study applies polynomial regression with response surface analysis to test the proposed hypotheses on the relationships between AI innovation, innovation niches, open innovation and entrepreneurial performance. Findings The results show that AI innovation enhances both inbound and outbound open innovation and strengthens innovation niches, which serve as key mediators. Additionally, the polynomial regression with response surface analysis reveals that entrepreneurial performance improves when inbound and outbound open innovation are aligned but declines under conditions of imbalance. This highlights the importance of congruence in open innovation strategies. Practical implications The findings offer actionable insights for entrepreneurs on leveraging AI and strategically balancing inbound and outbound open innovation, especially in emerging markets where resources and innovation capabilities are often limited. Originality/value This study contributes to the literature by integrating AI innovation, innovation niches and open innovation strategies into a unified framework. It offers novel empirical insights using PRRSA and responds to recent calls to understand how AI influences entrepreneurial strategies, particularly in resource-constrained and transitional economies like Vietnam.

The rapid advancement of artificial intelligence (AI) technologies has highlighted the critical importance of data privacy within the framework of international trade law. This study aims to explore the dynamic interplay between AI innovation and data privacy regulations in the context of global trade. The primary objective is to understand how international data privacy laws influence AI development within trade activities and how AI advancements, in turn, affect data privacy compliance and public trust in international markets. This study employs system dynamics modeling to analyze the complex interplay between artificial intelligence (AI) innovation and data privacy regulations within the context of international trade law. These variables include AI innovation level (AIL), investment in AI R&D (I AI R&D), data privacy compliance level (DPC), public trust in AI (PTAI), regulatory framework strength (RFS), compliance costs (CCs), innovation incentives (II), and cross-border data flow regulations (CBDFRs). By modeling these interactions, the study seeks to provide insights into balancing technological innovation with robust privacy protections specific to international trade. The focus is on the interdependencies between AI research and development (R&D) investments, regulatory frameworks tailored to trade, data privacy compliance levels, public trust, and associated compliance costs within the context of international trade law. The model also considers the impact of cross-border data flow regulations and innovation incentives on these dynamics. A system dynamics approach was employed to create a numerical model that simulates the relationships and feedback loops among the identified variables. Initial values were set on the basis of plausible estimates, and the model was run over five years to observe the trajectories of each variable under various regulatory and innovation scenarios.

BackgroundImage-driven specialisms such as radiology and pathology are at the forefront of medical artificial intelligence (AI) innovation. Many believe that AI will lead to significant shifts in professional roles, so it is vital to investigate how professionals view the pending changes that AI innovation will initiate and incorporate their views in ongoing AI developments.ObjectiveOur study aimed to gain insights into the perspectives and wishes of radiologists and pathologists regarding the promise of AI.MethodsWe have conducted the first qualitative interview study investigating the perspectives of both radiologists and pathologists regarding the integration of AI in their fields. The study design is in accordance with the consolidated criteria for reporting qualitative research (COREQ).ResultsIn total, 21 participants were interviewed for this study (7 pathologists, 10 radiologists, and 4 computer scientists). The interviews revealed a diverse range of perspectives on the impact of AI. Respondents discussed various task-specific benefits of AI; yet, both pathologists and radiologists agreed that AI had yet to live up to its hype. Overall, our study shows that AI could facilitate welcome changes in the workflows of image-driven professionals and eventually lead to better quality of care. At the same time, these professionals also admitted that many hopes and expectations for AI were unlikely to become a reality in the next decade.ConclusionsThis study points to the importance of maintaining a “healthy skepticism” on the promise of AI in imaging specialisms and argues for more structural and inclusive discussions about whether AI is the right technology to solve current problems encountered in daily clinical practice.

With the rapid advancement of artificial intelligence (AI) technologies, higher education institutions are increasingly exploring innovative ways to rethink teaching and assessment practices. This research paper examines the implications of AI on assessments in online learning environments. Specifically, the objectives of this study were to evaluate the effectiveness of AI-powered teaching methodologies in enhancing student engagement and learning outcomes in online education settings and, secondly, to analyze the impact of AI-driven assessment tools on the accuracy, reliability, and fairness of evaluating student performance in online learning environments through a systematic review and meta-analysis of existing literature. The study adopted activity theory to understand the issues around AI and assessment. The study adopted a mixed-methods design. The study adopted the use of meta-analysis in order to statistically combine results from multiple studies on a particular topic to provide a more comprehensive and reliable summary of the overall findings. The study found that to guarantee moral and just practices, there are issues with the integration of AI in online learning that need to be resolved. Key issues included data privacy, algorithmic prejudice, and the role of human instructors in the administration of the assessments online, carefully considered and addressed in a proactive manner. These findings provided insights on how AI can transform traditional teaching methods and assessment strategies, creating an AI-crowded environment that fosters student learning and academic success. Based on the findings, the study recommends that there is a need to integrate pedagogical strategies that leverage AI innovation, such as adaptive learning approaches, real-time feedback mechanisms, or interactive simulations, to improve teaching effectiveness and student performance in online settings.

This study investigates the impact of Artificial Intelligence (AI) innovation on the ecological footprint in the Nordic region from 1990 to 2020, alongside the effects of banking development, stock market capitalization, economic growth, and urbanization. Utilizing the STIRPAT model, the study incorporates cross-sectional dependence and slope homogeneity tests, revealing issues of heterogeneity and cross-sectional dependence. The analysis employs both first and second-generation panel unit root tests, confirming that the variables are free from unit root problems. Panel cointegration tests demonstrate that the variables are cointegrated in the long run. To explore the short- and long-term relationships, the study utilizes the Panel Autoregressive Distributed Lag (ARDL) model. The Panel ARDL results indicate that economic growth, stock market capitalization, and urbanization positively correlate with the ecological footprint in both the short and long run. Conversely, AI innovation and banking development negatively correlate with the ecological footprint. To validate the Panel ARDL estimations, robustness checks are performed using Fully Modified OLS, Dynamic OLS, and Fixed Effects with OLS, all of which support the initial findings. Furthermore, the study employs the D-H causality test to identify causal relationships. The results show a unidirectional causal relationship between AI innovation, stock market capitalization, urbanization, and the ecological footprint. In contrast, a bidirectional causal relationship exists between economic growth and the ecological footprint, as well as between banking development and the ecological footprint.

Artificial intelligence (AI) has emerged to be a salient driver for digital innovations. However, there is very limited research into how firms should manage their AI innovations. To fill this gap, we examine the comparative radicalness and process-orientation between AI and non-AI innovations. Prior research suggests that such attributes of innovations require firms to adopt very specific organizing principles. That is, the ways in which firms approach radical innovations will differ from those used in incremental innovations, and the organizing logic for new product innovations will also depart from that for new process innovation. We conduct an inductive exploratory study using a large U.S. patent data set and a multi-method research design. Results from our analysis reveal that AI innovations are significantly less radical and more process-oriented than their similar non-AI counterparts. Theoretical and managerial implications of our findings are discussed.

The escalating challenge of climate change necessitates an urgent exploration of factors influencing carbon emissions. This study contributes to the discourse by examining the interplay of technological, economic, and demographic factors on environmental sustainability. This study investigates the impact of artificial intelligence (AI) innovation, economic growth, foreign direct investment (FDI), energy consumption, and urbanization on CO2 emissions in the United States from 1990 to 2022. Employing the ARDL framework integrated with the STIRPAT model, the findings reveal a dual narrative: while AI innovation mitigates environmental stress, economic growth, energy use, FDI, and urbanization exacerbate environmental degradation. Unit root tests (ADF, PP, and DF-GLS) confirm mixed integration levels among variables, and the ARDL bounds test establishes long-term co-integration. The analysis highlights that AI innovation positively correlates with CO2 reduction when environmental safeguards are in place, whereas GDP growth, energy consumption, FDI, and urbanization intensify CO2 emissions. Robustness checks using FMOLS, DOLS, and CCR validate the ARDL findings. Additionally, Pairwise Granger causality tests reveal significant one-way causal links between CO2 emissions and economic growth, AI innovation, energy use, FDI, and urbanization. These relationships emphasize the critical role of AI-driven technological advancements, sustainable investments, and green energy in fostering ecological sustainability. The study suggests policy measures such as encouraging green FDI, advancing AI technologies, adopting sustainable energy practices, and implementing eco-friendly urban development to promote sustainable growth in the USA.

A substantial body of research highlights how stringent regulations disrupt innovators’ incentives and increase transaction costs, yet their information processing implication remains understudied. Building on information processing theory, we study how the interplay between formal and informal institutions shapes inventors’ information processing in developing artificial intelligence (AI) innovation. We examine the effect of stringent privacy regulations on AI innovation by exploiting the European Union’s General Data Protection Regulation (GDPR) announcement. We argue that, following the GDPR announcement, GDPR-affected countries experience lower national AI innovation rates than unaffected countries. Further, we postulate that this negative effect is weaker in GDPR-affected countries, marked by higher levels of individualism, masculinity, and indulgence, but stronger in the affected countries with higher levels of uncertainty avoidance, power distance, and long-term orientation. Our difference-in-differences analysis supports the proposed framework. Our research contributes to the international business literature by developing novel theoretical predictions at the intersection of comparative institutional analysis and national culture, explaining how privacy protection laws and cultural factors shape AI inventors’ information processing. Finally, this study provides insights into how inventors and entrepreneurs in countries with stringent privacy laws can leverage national culture to shape their AI innovation strategies and inform strategic decision-making.

Co-creation in action: Bridging the knowledge gap in artificial intelligence among innovation champions