The role of generative AI in enhancing predictive modeling for cost-effectiveness analysis in healthcare

Current advanced AI technologies like LLM and popular generative AI technologies based on generative adversarial networks (GANs) are revolutionising industries. Yet, it brings about severe cybersecurity threats at the same time. This chapter goes over one general risk of generative AI: fake information, or Generative AI Misinformation and Deepfakes, and six more specific risks: Generative AI Phishing, Generative AI Data Poisoning, Malicious Code Generation by Generative AI, and Privacy Violation by Generative AI. It raises the purpose of misuse with an elaboration of social engineering, smart malware, and leakage of sensitive data. This chapter provides a framework of measures: using detection tools, fortifying defences against phishing attacks, securing training data, proper control of AI misuse, and better data privacy regulation. Minimizing these risks, organizations, and stakeholders will be able to safely take advantage of reasons for the implementation of generative AI.

This study investigates the potential of small marketing firms to disrupt the market by adopting generative AI technology and the theory of disruptive innovation. The study employs a qualitative approach, combining a comprehensive literature review with in-depth interviews with leaders of small marketing firms. The research findings position generative and conversational AI as the next technological evolution, succeeding the internet and mobile/social era. It is the first study applying the theory of disruptive innovation to generative AI use in small marketing firms, presenting a positive outlook toward integrating generative AI into marketing operations. The study contributes to the emerging knowledge of AI in marketing, offering practical implications for scholars and practitioners to advance this field.

Aim: This study examines the role of generative AI in enhancing financial inclusion among Small and Medium Enterprises (SMEs) in the United States. It explores the potential of AI- driven frameworks in mitigating financial constraints, improving credit accessibility, and streamlining financial processes for SMEs. Study Design: A systematic review of literature published between 2019 and 2024 was conducted to assess the impact of generative AI technologies on financial services for SMEs. The study specifically focuses on digital lending innovations, AI-driven credit assessment strategies, and their role in advancing financial inclusion. Methodology: The study employed a systematic literature review approach, sourcing peer- reviewed journal articles and reports from Google Scholar, Scopus, IEEE Xplore, and SSRN. Articles were selected based on their direct relevance to generative AI applications in financial inclusion and SME development in the United States. Only studies that explicitly addressed AI-driven financial solutions for SMEs were included in the review. Results: The findings reveal that generative AI has significantly contributed to reducing financial exclusion among SMEs. Key applications such as automated credit scoring, fraud detection, and AI-powered financial advisory services have shown high potential in improving credit access, operational efficiency, and risk management. However, the adoption of these technologies faces critical challenges, including data privacy concerns, ethical issues, and high implementation costs. Conclusions: Generative AI has the potential to drive financial inclusion for SMEs in the United States by expanding access to financial services and improving credit assessment methodologies. However, addressing barriers to adoption requires collaborative efforts among policymakers, financial institutions, and technology developers to ensure equitable access, ethical implementation, and long-term sustainability of AI-driven financial solutions.

This paper reports preliminary findings from an ongoing, campus wide research project on effective methods for generative AI applicability in pursuit of effective and engaging teaching and learning activities. Generative AI has had a tremendous adoption rate since the public release of ChatGPT 3.5 on November 30th 2022. This has necessitated that educators and administrators consider the potential opportunities and threats usage of generative AI by students and faculty may have on higher education. Recognizing the inevitability of generative AI, the researchers have proposed a university-wide research project to ascertain the changes in faculty and students perspectives when using generative AI The research project is two-fold. First, a longitudinal survey has been developed to address research questions about usage and perceptions of generative AI change over time. The second prong of this research project focuses on the implementation of new and continuing generative AI professional development workshops. These “AI Institutes” are targeted educational opportunities to provide faculty, staff, and students with hands-on experiences that model appropriate ways to teach and learn with generative AI tools. Workshops change based on audience needs, but will be designed to support such processes as introductory and advanced lessons on building learning activities which engage students with generative AI, administrative shortcuts, best practices for writing, and our university’s AI policy and principles. The longitudinal survey, thus, allows the research team to gauge changes in perspectives as the “AI Institutes'' are deployed and widespread adoption of generative AI tools become more mainstream. This paper reports on the first year of this research project, including one survey and one AI Institute. This research on integrating generative AI technologies into teaching and learning has important implications for the field of networked learning. As the paper explores, rapid advances in AI are changing how students and faculty interact with content and each other. Findings from the longitudinal survey and AI Institutes could provide insights into how to thoughtfully leverage these emerging tools to enhance connections, dialogue, collaboration, and co-creation of knowledge within digital learning networks. While further research is needed, this project takes an important first step in assessing faculty and student perceptions that can inform appropriate AI integration. Lessons learned could guide other institutions exploring the potentials and pitfalls of weaving generative AI into networked learning ecosystems.

This study examines the duty of care for AI software developers, focusing on copyright infringement issues raised by the proliferation of generative AI. Specifically, this study attempts to interpret the specific provisions of copyright law that may be infringed by AI software developers amid the proliferation of generative AI technology, distinguishing between the learning stage and the output and distribution stages.Currently, there is considerable confusion among Korean developers regarding the duty of care they must bear when developing generative AI software. This study examines the potential for copyright infringement at each stage of generative AI software technology and examines the potential legal issues that may arise. Next, this study explores the relationship between generative AI technology and copyright. To assist Korean developers in pursuing a sophisticated legal approach, this study explains the differences between traditional copyright law and the copyright requirements for generative AI technology. Therefore, to address the current anxiety surrounding the still significant uncertainty surrounding the ex post facto, fact-based, and expost facto duty of care for generative AI software developers, it is necessary to consider enacting a universal legal principle that can be summarized as follows: 1) only use legally accessible data for training; 2) minimize the removal or modification of Rights Management Information (RMI) and ensure transparency; and 3) control substantial similarity and market substitutability at the output stage. This represents a balance that protects the legitimate interests of copyright holders without hindering the development of AI technology. Going forward, our legal system should also gradually establish explicit regulations on TDM, transparency standards, and an accountability structure centered on output control, rather than relying solely on fair use interpretations to address the uncertainty surrounding AI learning. Such institutional reforms will lay the foundation for transforming generative AI technology from a “dangerous gray area” to a predictable and reliable area of innovation.

This study examines the exploration of Model-as-a-Service for generative AI on cloud platforms. Model-as-a-Service (MaaS) could revolutionize generative AI; thus, we examine its impact on sectors, implementation best practices, and future trends. Business usage of generative AI for content development, predictive modelling, and consumer engagement is flexible and scalable using Software as a Service (SaaS). We explore how MaaS lets companies access, train, and deploy complex generative models like Generative Adversarial Networks (GAN), Variational Autoencoders (VAE), and Transformers without expensive in-house AI infrastructure. Lifecycle management in MaaS simplifies model training, deployment, versioning, and continuous improvement for iterative development in dynamic business contexts. MaaS security and compliance are crucial in highly regulated areas, including healthcare, finance, and law. Encryption, network isolation, and access control protect data and models. Generative AI models handle sensitive data; hence, industry standards and data sovereignty must be followed. Ethical AI, edge computing, and low-code/no-code platforms will enable more people to use models in real time and follow responsible AI guidelines, making MaaS's future bright. Generative AI applications and real-world case studies in healthcare, banking, retail, and entertainment demonstrate how MaaS can create value and stimulate innovation. Our study finds that using MaaS for generative AI, businesses can immensely benefit and explains how developers can speed up development, improve customer experiences, and remain ahead in the ever-changing digital landscape.

This paper presents a comprehensive survey of the applications, challenges, and limitations of Generative AI (GenAI) in enhancing threat intelligence within cybersecurity, supported by real-world case studies. We examine a wide range of data sources in Cyber Threat Intelligence (CTI), including security reports, blogs, social media, network traffic, malware samples, dark web data, and threat intelligence platforms (TIPs). This survey provides a full reference for integrating GenAI into CTI. We discuss various GenAI models such as Large Language Models (LLMs) and Deep Generative Models (DGMs) like Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Diffusion Models, explaining their roles in detecting and addressing complex cyber threats. The survey highlights key applications in areas such as malware detection, network traffic analysis, phishing detection, threat actor attribution, and social engineering defense. We also explore critical challenges in deploying GenAI, including data privacy, security concerns, and the need for interpretable and transparent models. As regulations like the European Commission’s AI Act emerge, ensuring trustworthy AI solutions is becoming more crucial. Real-world case studies, such as the impact of the WannaCry ransomware, the rise of deepfakes, and AI-driven social engineering, demonstrate both the potential and current limitations of GenAI in CTI. Our goal is to provide foundational insights and strategic direction for advancing GenAI’s role in future cybersecurity frameworks, emphasizing the importance of innovation, adaptability, and ongoing learning to enhance resilience against evolving cyber threats. Ultimately, this survey offers critical insights into how GenAI can shape the future of cybersecurity by addressing key challenges and providing actionable guidance for effective implementation.

Generative Artificial Intelligence (Generative AI) is a collection of AI technologies that can generate new information such as texts and images. With its strong capabilities, Generative AI has been actively studied in creative design processes. However, limited studies have explored the roles of humans and Generative AI in conceptual design processes, which leaves a gap for human–AI collaboration investigation. To address this gap, this study attempts to uncover the contributions of different Generative AI technologies in assisting humans in the conceptual design process. Novice designers were recruited to complete two design tasks in the condition of with or without the assistance of Generative AI. The results revealed that Generative AI primarily assists humans in the problem definition and idea generation stages, while the idea selection and evaluation stage remains predominantly human-led. Additionally, with the assistance of Generative AI, the idea selection and evaluation stages were further enhanced. Based on the findings, we discussed the role of Generative AI in human–AI collaboration and the implications for enhancing future conceptual design support with Generative AI’s assistance.

Purpose: This paper explores the recent literature on Generative AI applications in the financial industry and delineates its role in the future. Methodology: Our paper follows secondary research analyzing current literature on Generative AI in finance. It is one of the essential tools for understanding background information, identifying research problems, and filling the literature gaps. This paper studies how Generative AI has potential financial benefits and risks, providing unique insights into the financial landscape in the coming years. Findings: The findings unveil that Generative AI can become a strategic tool to redefine financial services and operational effectiveness. It can substantially improve the services by reducing costs, bringing efficiency, and enhancing corporate performance. It has the enormous transformative power to revolutionize client product and service offerings, improving risk management assessments and bringing efficiency to operations. However, our study indicates that the financial service industry can get into practices and decisions that are potentially unethical and financial exclusion due to an embedded bias in its algorithm and design of Generative AI technologies. Since Generative AI continues to evolve, its role and effectiveness in decision-making are expected to shape the financial services landscape significantly. Unique Contribution to Theory, Practice, and Policy: Generative AI can be a game changer for the financial industry, fueling digital transformation across industries. The transformative potential of generative AI can optimize operations, revolutionize customer experiences, and drive innovation seamlessly in finance. Our paper suggests how policymakers can foresee the challenges ahead due to the Generative AI in finance services, which is challenging the existing regulatory landscape. To stay ahead in the competition, financial firms must balance data privacy and algorithmic bias and ensure the responsible use of AI.

The growing sophistication and frequency of cyberattacks has necessitated the development of more advanced and dynamic cybersecurity strategies. Traditional methods of threat detection and response, while effective in many cases, often struggle to keep pace with the constantly evolving tactics employed by cybercriminals. Generative AI presents a transformative opportunity to enhance cybersecurity by offering a more proactive, adaptive, and intelligent approach to threat identification, prevention, and mitigation.Generative AI, a class of machine learning models capable of creating new data from learned patterns, can be leveraged in cybersecurity for various tasks such as anomaly detection, malware generation analysis, and predictive threat modeling. By analyzing vast amounts of data and identifying patterns of normal and malicious behavior, generative AI can improve the detection of previously unknown threats and zero-day vulnerabilities. Furthermore, generative models can simulate potential attack vectors, helping security teams identify weaknesses in systems and networks before they can be exploited by adversaries.There were somany benefits of AI;among them,this entitledworkexplores how generative AI can be integrated into cybersecurity frameworks to enhance protection against cyber threats. We examine the benefits of using Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and other generative models to improve intrusion detection systems, automate security operations, and bolster real-time response capabilities. Additionally, we address the challenges and risks of deploying generative AI in cybersecurity, including the potential for adversarial AI attacks and the ethical implications of AI-driven decision-making in sensitive environments.

Generative artificial intelligence technologies have disrupted information ecosystems, posing new threats to public health by enabling rapid, scalable manufacture of convincing but false health stories. This systematic review synthesizes evidence on how generative AI reconfigures health misinformation creation, dissemination, and moderation. In line with PRISMA 2020, 15 empirical studies published between January 2023 and August 2025 were included. Databases consulted were MEDLINE (via PubMed), Embase, Scopus, Web of Science Core Collection, ACM Digital Library, IEEE Xplore, PsycINFO, Communication & Mass Media Complete, arXiv, and medRxiv/SSRN. Studies were contrasted on the basis of production capacity, propagation dynamics, and efficacy of mitigation at technical, sociotechnical, and governance layers. The synthesis indicates that generative AI substantially increases the volume, speed, and perceived credibility of health disinformation production, while altering its propagation dynamics. Users often struggle to distinguish AI‑generated from human‑authored health misinformation, and their sharing intentions are not tightly coupled with perceived accuracy. Existing detection systems show limited performance against AI‑generated content, and while labeling interventions can reduce perceived accuracy, their effects are context‑dependent. Generative AI transforms the health misinformation landscape by lowering barriers to creation and exploiting platform and behavioral dynamics. Current mitigation strategies-spanning technical, sociotechnical, and governance layers-are promising but remain nascent and unevenly evaluated. Future work must prioritize multimodal, multilingual, and health‑specific verification, as well as real‑world testing of interventions, to build equitable and resilient health information ecosystems.

Abstract—Generative AI has emerged as a promising solution for automated analysis and validation of the final outgate quality in semiconductor manufacturing. This review explores the potential of leveraging generative AI models, such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and transformers, to address the challenges faced by traditional quality control methods in the semiconductor industry. These models offer unique capabilities for image analysis, defect detection, and process optimization, enabling more accurate and efficient quality control processes. Applications of generative AI in semiconductor manufacturing include defect classification, anomaly detection, predictive maintenance, and process simulation. By learning complex data distributions and generating synthetic data, generative AI can enhance the robustness and generalization of defect-detection models, capture subtle defect patterns, and discover novel defect types without explicit labeling. However, implementing generative AI in real-time manufacturing environments presents challenges related to the computational requirements, model interpretability, and integration with existing workflows. Addressing these challenges requires careful consideration of the data quality, model architecture, and deployment strategies. Case studies demonstrated the significant benefits of generative AI in improving defect detection, increasing yield, reducing time-to-market, and lowering manufacturing costs. As technology continues to evolve, future research should focus on emerging trends such as the AI-driven design of new materials and devices, while addressing ethical considerations and potential workforce impacts. This review provides a comprehensive overview of the current state and future directions of generative AI in semiconductor manufacturing, offering valuable insights for researchers and practitioners in the field. Keywords—semiconductor manufacturing, Generative AI, quality control, defect detection, final outgate quality, process optimization, anomaly detection

Protecting virtual assets from cyber threats is essential as we live in a digitally advanced world. Providing a responsible emphasis on proper network security and intrusion detection is imperative. On the other hand, traditional strategies need a supportive tool to adapt to the transforming threat space. New generative AI techniques like generative adversarial networks (GANs) and variational autoencoders (VAEs) are the mainstream technologies required to meet the gap. This chapter deals with how these models can enhance network security by inspecting the network traffic for anomalies and malicious behaviors detected through unsupervised learning, which considers strange or emerging phenomena. This survey features innovations in fault detection, behavior control, deep packet inspection, traffic classification, and examples of real-world intrusions detected by GAN-based systems. Furthermore, the chapter focuses on the challenges of adversarial attacks on models that require the development of solid defense mechanisms, such as generative adversarial networks. Ethics becomes the following matter on our list of discussions, given that privacy transparency and accountability are to be observed when working with generative AI technologies in network security. Finally, the authors examine trends that determine how cyber-attacks are dealt with comprehensively.

PurposeA gripping keyword emerged in the dynamic world of 2022: GPT or the advent of Generative Artificial Intelligence (GAI), at its forefront, embodied by the mysterious ChatGPT. This technological marvel had been silently lurking in the background for just over five years. However, all of a sudden, it emerged onto the scene, capturing the public’s attention and quickly becoming one of the most widely adopted inventions in history. Therefore, this narrative review is conducted in order to explore the impact of generative AI and ChatGPT on lifelong learning and upskilling of students in higher education and address opportunities and challenges proposed by Artificial Intelligence from a global perspective.Design/methodology/approachThis review has been conducted using a narrative literature review approach. For in-depth identification of research gaps, 105 relevant articles were included from scholarly databases such as Scopus, Web of Science, ERIC and Google Scholar. Seven major themes emerged from the literature to answer the targeted research questions that describe the use of AI, the impact of generative AI and ChatGPT on students, the challenges and opportunities of using AI in education and mitigating strategies to cope with the challenges associated with the integration of ChatGPT and generative AI in education.FindingsThe review of the literature presents that generative AI and ChatGPT have gained a lot of recognition among students and have revolutionized educational settings. The findings suggest that there are some contexts in which adult education research and teaching can benefit from the use of chatbots and generative AI technologies like ChatGPT. The literature does, however, also highlight the necessity of carefully considering the benefits and drawbacks of these technologies in order to prevent restricting or distorting the educational process or endangering academic integrity. In addition, the literature raises ethical questions about data security, privacy and cheating by students or researchers. To these, we add our own ethical concerns about intellectual property, such as the fact that, once we enter ideas or research results into a generative chatbot, we no longer have control over how it is used.Practical implicationsThis review is helpful for educators and policymakers to design the curriculum and policies that encourage students to use generative AI ethically while taking academic integrity into account. Also, this review article identifies the major gaps that are associated with the impact of AI and ChatGPT on the lifelong learning skills of students.Originality/valueThis review of the literature is unique because it explains the challenges and opportunities of using generative AI and ChatGPT, also defining its impact on lifelong learning and upskilling of students.

Exploring the Synergy: Generative AI and Venture Capital in Driving Innovation and GrowthGenerative AI and venture capital are two distinct domains that have shown significant impact in their respective fields.Generative AI refers to the application of artificial intelligence techniques to generate new and innovative content, such as images, music, or text, that is not directly copied from existing examples.Venture capital, on the other hand, involves providing financial investment and support to startups and high-growth companies with the aim of generating substantial returns. The intersection of generative AI and venture capital presents unique opportunities and challenges. Generative AI technologies have the potential to revolutionize various industries by enabling the creation of novel and creative outputs. This opens up new possibilities for startups to leverage generative AI in their products or services, attracting the attention of venture capitalfirms seeking to invest in cutting-edge technologies.Venture capital firms, in turn, play a crucial role in fueling the growth of startups working on generative AI.They provide not only financial resources but also mentorship, industry connections, and strategic guidance to help these companies navigate the complex landscape of technology development, intellectual property, and market adoption.However, investing in generative AI startups comes with its own set of challenges.The intellectual property landscape surrounding generative AI can also be complex, requiringcareful evaluation of patent portfolios and potential legal risks.In conclusion, the convergence of generative AI and venture capital represents an exciting frontier in technology innovation and investment.The symbiotic relationship between these domains has the potential to unlock new opportunities, disrupt traditional industries, and shape the futureof AI-driven creativity and entrepreneurship.