Artificial Intelligence Innovation Research Articles

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.

The Crucial Role of Interdisciplinary Conferences in Advancing Explainable AI in Healthcare

As artificial intelligence (AI) integrates within the intersecting domains of healthcare and computational biology, developing interpretable models tailored to medical contexts is met with significant challenges. Explainable AI (XAI) is vital for fostering trust and enabling effective use of AI in healthcare, particularly in image-based specialties such as pathology and radiology where adjunctive AI solutions for diagnostic image analysis are increasingly utilized. Overcoming these challenges necessitates interdisciplinary collaboration, essential for advancing XAI to enhance patient care. This commentary underscores the critical role of interdisciplinary conferences in promoting the necessary cross-disciplinary exchange for XAI innovation. A literature review was conducted to identify key challenges, best practices, and case studies related to interdisciplinary collaboration for XAI in healthcare. The distinctive contributions of specialized conferences in fostering dialogue, driving innovation, and influencing research directions were scrutinized. Best practices and recommendations for fostering collaboration, organizing conferences, and achieving targeted XAI solutions were adapted from the literature. By enabling crucial collaborative junctures that drive XAI progress, interdisciplinary conferences integrate diverse insights to produce new ideas, identify knowledge gaps, crystallize solutions, and spur long-term partnerships that generate high-impact research. Thoughtful structuring of these events, such as including sessions focused on theoretical foundations, real-world applications, and standardized evaluation, along with ample networking opportunities, is key to directing varied expertise toward overcoming core challenges. Successful collaborations depend on building mutual understanding and respect, clear communication, defined roles, and a shared commitment to the ethical development of robust, interpretable models. Specialized conferences are essential to shape the future of explainable AI and computational biology, contributing to improved patient outcomes and healthcare innovations. Recognizing the catalytic power of this collaborative model is key to accelerating the innovation and implementation of interpretable AI in medicine.

Optimizing maintenance logistics on offshore platforms with AI: Current strategies and future innovations

Offshore platforms are vital assets for the oil and gas industry, serving as the primary facilities for exploration, extraction, and processing. Maintenance logistics plays a crucial role in ensuring these platforms operate efficiently and safely. However, the remote and harsh environments of offshore platforms present significant challenges for maintenance activities. Traditional maintenance strategies often struggle to meet the demands of these environments, leading to inefficiencies, increased costs, and potential safety risks. This review discusses the application of Artificial Intelligence (AI) in optimizing maintenance logistics on offshore platforms. Current strategies involve a combination of preventive, predictive, and corrective maintenance approaches. Preventive maintenance schedules regular inspections and replacements based on predetermined intervals, while predictive maintenance utilizes data analytics to predict equipment failures and plan maintenance activities accordingly. Corrective maintenance addresses issues as they arise, often in response to unexpected failures. AI offers opportunities to enhance these strategies by leveraging advanced data analytics, machine learning, and optimization algorithms. AI-enabled predictive maintenance can analyze vast amounts of data from sensors, historical maintenance records, and environmental factors to forecast equipment failures with greater accuracy. This allows for proactive maintenance planning, minimizing downtime and reducing maintenance costs. Furthermore, AI can optimize maintenance logistics by improving resource allocation and scheduling. Through real-time monitoring and analysis, AI systems can prioritize maintenance tasks based on urgency, equipment criticality, and resource availability. This ensures that maintenance crews are deployed efficiently, reducing idle time and improving overall productivity. Future innovations in AI for maintenance logistics on offshore platforms include the integration of Internet of Things (IoT) devices and autonomous systems. IoT sensors can provide real-time data on equipment condition and environmental factors, enabling more precise predictive maintenance models. Autonomous maintenance robots equipped with AI algorithms can perform routine inspections and minor repairs, reducing the need for human intervention in hazardous environments. However, implementing AI in offshore maintenance logistics also poses challenges, including data quality, cybersecurity, and workforce readiness. Ensuring data accuracy and reliability is crucial for effective AI models, requiring robust data collection and management processes. Cybersecurity measures must be strengthened to protect AI systems from malicious attacks that could disrupt operations or compromise safety. Additionally, workforce training and education are essential to prepare personnel for working alongside AI systems and interpreting AI-generated insights. Optimizing maintenance logistics on offshore platforms with AI offers significant benefits in terms of efficiency, cost savings, and safety. By leveraging AI technologies, current maintenance strategies can be enhanced, and future innovations can revolutionize offshore maintenance practices, making operations more sustainable and resilient in the face of evolving challenges.

Ethical Implication of Artificial Intelligence (AI) Adoption in Financial Decision Making

The integration of artificial intelligence (AI) into the financial sector has raised ethical concerns that need to be addressed. This paper analyzes the ethical implications of using AI in financial decision-making and emphasizes the importance of an ethical framework to ensure its fair and trustworthy deployment. The study explores various ethical considerations, including the need to address algorithmic bias, promote transparency and explainability in AI systems, and adhere to regulations that protect equity, accountability, and public trust. By synthesizing research and empirical evidence, the paper highlights the complex relationship between AI innovation and ethical integrity in finance. To tackle this issue, the paper proposes a comprehensive and actionable ethical framework that advocates for clear guidelines, governance structures, regular audits, and collaboration among stakeholders. This framework aims to maximize the potential of AI while minimizing negative impacts and unintended consequences. The study serves as a valuable resource for policymakers, industry professionals, researchers, and other stakeholders, facilitating informed discussions, evidence-based decision-making, and the development of best practices for responsible AI integration in the financial sector. The ultimate goal is to ensure fairness, transparency, and accountability while reaping the benefits of AI for both the financial sector and society.

Synergies and Challenges: Exploring the Intersection of Artificial Intelligence and Cybersecurity

Abstract: The fusion of artificial intelligence (AI) and cybersecurity signifies a profound turning point in our perpetual struggle against cyber threats. With the expansion of digital landscapes and the escalating sophistication of cyber adversaries, harnessing the capabilities of AI to bolster our defences has become not only advisable but imperative. This paper embarks on a deep exploration of the intricate relationship between AI and cybersecurity, uncovering the rich tapestry of synergies that underlie their integration while deftly navigating the labyrinth of complex challenges and ethical quandaries inherent in this convergence. Through a meticulous examination of advanced threat detection methodologies, adaptive defence mechanisms, and the unfolding panorama of emerging trends, this paper endeavours to lay the groundwork for a seismic shift in cybersecurity paradigms. It is a journey through the corridors of innovation, where AI-driven insights illuminate the path forward and interdisciplinary collaboration serves as the compass guiding us toward a future fortified by AI innovation and fortified by collective wisdom.

Artificial intelligence in judicial adjudication: Semantic biasness classification and identification in legal judgement (SBCILJ)

History reveals that human societies have suffered in terms of social justice due to cognitive bias. Semantic bias tends to amplify cognitive bias. Therefore, the presence of cognitive biases in extensive historical data can potentially result in unethical and allegedly inhumane predictions since AI systems are trained on this data. The innovation of artificial intelligence and its rapid integration across disciplines has prompted questions regarding the subjectivity of the technology. Current research focuses the semantic bias in legal judgment to increase the legitimacy of training data. By the application of general-purpose Artificial Intelligence (AI) algorithms, we classify and detect the semantics bias that is present in the Chinese Artificial Intelligence and Law (CAIL) dataset. Our findings demonstrate that AI models acquire superior prediction power in the CAIL dataset, which is comprised of hundreds of cases, compared to a structured professional risk assessment tool. To assist legal practitioners during this process, innovative approaches that are based on AI may be implemented inside the legal arena. To accomplish this objective, we suggested a classification model for semantic bias that is related to the classification and identification of semantic biases in legal judgment. Our proposed model legal field uses the example of categorization along with the identification of the CAIL dataset. This will be accomplished by identifying the semantics biases in judicial decisions. We used different types of classifiers such as the Support Vector Machine (SVM), Naïve-Bayes (NB), Multi-Layer Perceptron (MLP), and the K-Nearest Neighbour (KNN) to come across the preferred results. SVM got 96.90 %, NB has 88.80 %, MLP has 86.75 % and KNN achieved 85.66 % accuracy whereas SVM achieved greater accuracy as compared to other models. Additionally, we demonstrate that we were able to get a relatively high classification performance when predicting outcomes based just on the semantic bias categorization in judicial judgments that determine the outcome of the case.

Impact of artificial intelligence on health information literacy: guidance for healthcare professionals

Purpose This paper aims to explore the intricate relationship between artificial intelligence (AI) and health information literacy (HIL), examining the rise of AI in health care, the intersection of AI and HIL and the imperative for promoting AI literacy and integrating it with HIL. By fostering collaboration, education and innovation, stakeholders can navigate the evolving health-care ecosystem with confidence and agency, ultimately improving health-care delivery and outcomes for all. Design/methodology/approach This paper adopts a conceptual approach to explore the intricate relationship between AI and HIL, aiming to provide guidance for health-care professionals navigating the evolving landscape of AI-driven health-care delivery. The methodology used in this paper involves a synthesis of existing literature, theoretical analysis and conceptual modeling to develop insights and recommendations regarding the integration of AI literacy with HIL. Findings Impact of AI on health-care delivery: The integration of AI technologies in health-care is reshaping the industry, offering unparalleled opportunities for improving patient care, optimizing clinical workflows and advancing medical research. Significance of HIL: HIL, encompassing the ability to access, understand and critically evaluate health information, is crucial in the context of AI-driven health-care delivery. It empowers health-care professionals, patients and the broader community to make informed decisions about their health and well-being. Intersection of AI and HIL: The convergence of AI and HIL represents a critical juncture, where technological innovation intersects with human cognition. AI technologies have the potential to revolutionize how health information is generated, disseminated and interpreted, necessitating a deeper understanding of their implications for HIL. Challenges and opportunities: While AI holds tremendous promise for enhancing health-care outcomes, it also introduces new challenges and complexities for individuals navigating the vast landscape of health information. Issues such as algorithmic bias, transparency and accountability pose ethical dilemmas that impact individuals’ ability to critically evaluate and interpret AI-generated health information. Recommendations for health-care professionals: Health-care professionals are encouraged to adopt strategies such as staying informed about developments in AI, continuous education and training in AI literacy, fostering interdisciplinary collaboration and advocating for policies that promote ethical AI practices. Practical implications To enhance AI literacy and integrate it with HIL, health-care professionals are encouraged to adopt several key strategies. First, staying abreast of developments in AI technologies and their applications in health care is essential. This entails actively engaging with conferences, workshops and publications focused on AI in health care and participating in professional networks dedicated to AI and health-care innovation. Second, continuous education and training are paramount for developing critical thinking skills and ethical awareness in evaluating AI-driven health information (Alowais et al., 2023). Health-care organizations should provide opportunities for ongoing professional development in AI literacy, including workshops, online courses and simulation exercises focused on AI applications in clinical practice and research. Originality/value This paper lies in its exploration of the intersection between AI and HIL, offering insights into the evolving health-care landscape. It innovatively synthesizes existing literature, proposes strategies for integrating AI literacy with HIL and provides guidance for health-care professionals to navigate the complexities of AI-driven health-care delivery. By addressing the transformative potential of AI while emphasizing the importance of promoting critical thinking skills and ethical awareness, this paper contributes to advancing understanding in the field and promoting informed decision-making in an increasingly digital health-care environment.

Regulating artificial intelligence: A technology-independent approach

Successful applications of artificial intelligence (AI), such as ChatGPT, have been prompting regulators to speed up the related regulation processes. China and the EU have been particularly ambitious in this regard. The EU AI Act has been swiftly progressing through the institutions and is expected to be officially adopted in spring 2024. This article argues that its overall approach is wrong, that it extends EU regulation into policy areas which come under national competences and that it will hurt European AI innovation in particular and society in general. Instead of regulating AI per se, the EU or the member states should regulate the use of AI in specific sectors or, better still, regulate it in technologically independent ways—by specifying what is allowed or prohibited, regardless of the technology used.

‘We have opened a can of worms’: using collaborative ethnography to advance responsible artificial intelligence innovation

ABSTRACT With the recent rapid developments in artificial intelligence (AI), social scientists and computational scientists have approached overlapping questions about ethics, responsibility, and fairness. Joined-up efforts between these disciplines have nonetheless been scarce due to, among other factors, unfavourable institutional arrangements, unclear publication avenues, and sometimes incompatible normative, epistemological and methodological commitments. In this paper, we offer collaborative ethnography as one concrete methodology to address some of these challenges. We report on an interdisciplinary collaboration between science and technology studies scholars and data scientists developing an AI system to detect online misinformation. The study combined description, interpretation, and (self-)critique throughout the design and development of the AI system. We draw three methodological lessons to move from critique to action for interdisciplinary teams pursuing responsible AI innovation: (1) Collective self-critique as a tool to resist techno-centrism and relativism, (2) Moving from strategic vagueness to co-production, and (3) Using co-authorship as a method.

Advances in Explainable, Fair, and Trustworthy AI

This special issue encapsulates the multifaceted landscape of contemporary challenges and innovations in Artificial Intelligence (AI) and Machine Learning (ML), with a particular focus on issues related to explainability, fairness, and trustworthiness. The exploration begins with the computational intricacies of understanding and explaining the behavior of binary neurons within neural networks. Simultaneously, ethical dimensions in AI are scrutinized, emphasizing the nuanced considerations required in defining autonomous ethical agents. The pursuit of fairness is exemplified through frameworks and methodologies in machine learning, addressing biases and promoting trust, particularly in predictive policing systems. Human-agent interaction dynamics are elucidated, revealing the nuanced relationship between task allocation, performance, and user satisfaction. The imperative of interpretability in complex predictive models is highlighted, emphasizing a query-driven methodology. Lastly, in the context of trauma triage, the study underscores the delicate trade-off between model accuracy and practitioner-friendly interpretability, introducing innovative strategies to address biases and trust-related metrics.

Significance of Artificial Intelligence in Recruitment Process

HR is the division of a business that is charged with finding, screening, recruitment, training job applicants. It typically finds, trains, hires and fires employees. Organization are increasingly recognizing the critical role of HRD in nurturing their most valuable asset- their people, it starts from the very sourcing, screening, recruiting, induction, performance management and their development. With the changing scenario, technological advancement has impacted HR process significantly. The innovation of artificial intelligence and machine learning are now significantly entered the field of human resource development. AI system can analyze vast amount of HR data to identify potential candidate and predict their chances of getting shortlisted foe a particular job. AI recruitment reduces by automating all the mundane tasks. Recruitment has become a hotbed for AI applications in HR. The platform has led to an improvement in candidate experience by making it easier for job seeker to apply for open position and communicate directly with the company. With the changing scenario and advancement in technology, AI has incorporated in every disciple. It helps I increasing the human efficiency of doing task for quick and better achievement of organizational objectives. HR department in itself is in a pivot position as it starts before any function comes into action. It plays key role in streamlining all the departments and processes that are working towards business operations. AI isn’t here to replace human recruitment but it’s here to make their lives infinitely easier. Though AI finds no comparison to the human brain, still it’s a game changer for improving the quality of work done.

Navigating the Divide: A Comprehensive Review of the Mechanical and Anatomical Axis Approaches in Total Knee Replacement.

This comprehensive review explores the mechanical and anatomical axis approaches in total knee replacement (TKR) surgery, addressing the ongoing debate within the orthopedic community. Emphasizing the significance of TKR in alleviating knee-related disorders, this review underscores the pivotal role of accurate alignment in achieving optimal surgical outcomes. The purpose is to navigate the divide between the well-established mechanical axis approach, focusing on a straight-line alignment, and the anatomical axis approach, aligning with natural knee landmarks. The analysis delves into the advantages, disadvantages, and clinical implications of each approach, offering a nuanced perspective on their efficacy. The conclusion emphasizes a patient-centric approach, recommending the adoption of hybrid strategies and the incorporation of emerging technologies for enhanced precision. The future of TKR aligns with personalized medicine, leveraging advancements in computer-assisted navigation, robotics, and patient-specific implants. Ongoing professional development and interdisciplinary collaboration are crucial for surgeons, and as the field evolves, innovations in artificial intelligence, imaging, and 3D printing are expected to shape the trajectory of TKR alignment approaches.

Role of artificial intelligence in customer engagement: a systematic review and future research directions

Role of artificial intelligence in customer engagement: a systematic review and future research directions

The mechanisms of AI hype and its planetary and social costs

AbstractOur global landscape of emerging technologies is increasingly affected by artificial intelligence (AI) hype, a phenomenon with significant large-scale consequences for the global AI narratives being created today. This paper aims to dissect the phenomenon of AI hype in light of its core mechanisms, drawing comparisons between the current wave and historical episodes of AI hype, concluding that the current hype is historically unmatched in terms of magnitude, scale and planetary and social costs. We identify and discuss socio-technical mechanisms fueling AI hype, including anthropomorphism, the proliferation of self-proclaimed AI “experts”, the geopolitical and private sector “fear of missing out” trends and the overuse and misappropriation of the term “AI” in emerging technologies. The second part of the paper seeks to highlight the often-overlooked costs of the current AI hype. We examine its planetary costs as the AI hype exerts tremendous pressure on finite resources and energy consumption. Additionally, we focus on the connection between AI hype and socio-economic injustices, including perpetuation of social inequalities by the huge associated redistribution of wealth and costs to human intelligence. In the conclusion, we offer insights into the implications for how to mitigate AI hype moving forward. We give recommendations of how developers, regulators, deployers and the public can navigate the relationship between AI hype, innovation, investment and scientific exploration, while addressing critical societal and environmental challenges.

Subverting the Future of Teaching: Artificial Intelligence Innovation in Nursing Education

Artificial intelligence (AI) technologies, including machine learning, deep learning, natural language processing, generative AI, the metaverse and other iterations, are rapidly changing the landscape of education. Related technologies not only enhance the teaching and learning process but also improve the quality and availability of educational content. AI facilitates educational transformation, reshapes teaching models, and helps students achieve their personalized learning needs, thus improving learning outcomes, learning efficiencies, and teaching practices. Despite the many AI application cases in nursing management and clinical practice, the application of AI in nursing education remains in its infancy. Machine learning has been used to predict the academic performance and graduation results of nursing students, thereby facilitating the early identification of additional support needs. Natural language processing technology has been used to develop chatbots and virtual teachers to assist learning, providing personalized learning support to help students overcome learning obstacles. Also, generative AI technologies such as ChatGPT (chat generative pre-trained transformer) have been used to create simulated patient cases and as a tool for grading academic writing automatically. Moreover, the combination of generative AI technology and the metaverse has introduced new possibilities to nursing education, allowing students to learn in a more-immersive virtual environment. Despite the significant benefits brought by AI to nursing education, its implementation and integration still face multiple challenges, including high costs, the need to provide technical training to teachers, and the need to address issues such as academic integrity and data privacy. The authors hope this article will help promote interdisciplinary cooperation between nursing educators and information and communication experts and the development of AI-assisted teaching to open a new chapter in nursing education.

Embracing the Era of Artificial Intelligence: Innovation and Preparation in Nursing Education

Rapid recent advances in information technology have opened the door for artificial intelligence (AI)-related technologies to be applied extensively across many industries. The Ministry of Education has emphasized the importance of cultivating advanced-level professionals in diverse fields, particularly in smart machinery, the Asia-Silicon Valley sector, green energy technology, biotechnology, national defense, new agricultural, and circular economy industries, to enhance innovation and promote industrial competitiveness (Kuo, 2019). While interdisciplinary talent in AI and digital innovation is being actively developed elsewhere, nursing education remains in the exploratory phase of AI and digital technology talent cultivation. Although AI is now a well-known term, the competencies required for its application in nursing remain unclear. Moreover, most nursing professionals are unfamiliar with how to best integrate AI into nursing expertise or practice settings. With the application of AI in the healthcare industry now unstoppable, it is vital to consider how to help nursing students adapt to healthcare's new technology landscape (Huang et al., 2021). AI facilitates the digital simulation of human thought patterns, logic, and behaviors with the goal of assisting human users solve problems, especially those that are time-consuming and require repetitive processing. The development of AI requires interdisciplinary collaboration among domain experts, data scientists, software engineers, robotics experts, and computer programmers. Such collaboration is essential to developing products able to meet the demands of the times and to help students become competent future nursing professionals (Murray, 2018). Nurses spend the most time interacting with patients and are thus best able to understand the perceptions and challenges of patients and their families. Collaborating with professionals from interdisciplinary fields is the best strategy for achieving optimal healthcare outcomes. However, nursing schools have yet to provide a clear response to the impact of AI on nursing education. Nursing educational institutions must enable nursing students to comprehend the concepts and principles of AI and equip them with AI literacy to allow them to unleash their potential, continuously innovate, and stay abreast with the times (Ng et al., 2021). In this issue, experts and scholars currently engaged in AI-related research in the nursing discipline share their research findings in the realms of machine learning, deep learning, emotional recognition, and natural language application. These articles offer insights into the implications of AI, suggest how nursing education may best respond to emerging AI trends, and provide the authors' perspectives on nursing education reform. The editor hopes readers will be inspired to explore new concepts, gain a deeper understanding of the application and significance of AI, and apply AI to address clinical and educational challenges to foster competent nursing professionals for tomorrow.