Generative AI and foundation models in medical image

The computational logics of large language models (LLMs) or generative AI – from the early models of CLIP and BERT to the explosion of text and image generation via ChatGPT and DALL-E − are increasingly penetrating the social and political world. Not merely in the direct sense that generative AI models are being deployed to govern difficult problems, whether decisions on the battlefield or responses to pandemic, but also because generative AI is shaping and delimiting the political parameters of what can be known and actioned in the world. Contra the promise of a generalizable “world model” in computer science, the article addresses how and why generative AI gives rise to a model of the world, and with it a set of political logics and governing rationalities that have profound and enduring effects on how we live today. The article traces the genealogies of generative AI models, how they have come into being, and why some concepts and techniques that animate these models become durable forms of knowledge that actively shape the world, even long after a specific material commercial GPT model has moved on to a new iteration. Though generative AI retains significant traces of former scientific and computational regimes – in statistical practices, probabilistic knowledge, and so on – it is also dislocating epistemological arrangements and opening them to novel ways of perceiving, characterising, classifying, and knowing the world. Four defining aspects of the political logic of generative AI are elaborated: i) generativity as something more than the capacity to generate image or text outputs, so that a generative logic acts upon the world understood as estimates of “underlying distributions” in data; ii) latency as a political logic of compression in which (by contrast with claims to reduction or distortion) the thing that is hidden, unknown or latent becomes surfaced and amenable to being governed; iii) broken and parallelized sequences as the ordering device of the political logic of generative AI, where attention frameworks radically change the possibilities for governing non-linear problems; iv) pre-training and fine-tuning as a computational logic of generative AI that simultaneously shapes a “zero shot politics” oriented towards unencountered data and new tasks. Across each of the four aspects, the article maps the emerging contemporary political logic of generative AI.

This research looks at the potential effects of generative artificial intelligence AI on the country's media landscape. Given their pervasiveness, it aims to reveal how AI-powered technologies in media content creation, distribution, and personalisation contribute to the overall process of national progress. Using well-designed questionnaires, the study quantitatively collects data from media professionals, techies, and communication scholars in large cities throughout China. Using statistical tools such as structural equation modelling and regression analysis, one investigated the interplay between the rate of modernisation, the effects of national development, and AI-driven media innovation. Media indices of generative AI demonstrate a clear positive correlation with the effect of modernism and national development programs. As China strives to digitally change its communication infrastructure and increase its cultural influence, technological prowess, and media production, generative AI is playing an increasingly crucial role. This study shows that AI in media may lead to more dynamic stories, practical audience participation, and worldwide outreach, all thanks to modernist techniques. There is no part of this that does not contribute to the advancement of national development goals. The results provide policymakers, media outlets, and AI developers with valuable information for formulating strategies to integrate AI with sustainable development objectives. Via an experimental interaction between generative AI and national development perceived via a modernist lens, this study provides a framework for future research on new media technologies and national change. The discussion of the societal potential presented by AI may now begin.

Abstract—Smart home security cameras are becoming more common, but their usefulness can be diminished by notification fatigue from too many alerts about minor incidents. This paper examines the gaps of existing event detection and notification systems in security cameras and then recommends using Generative AI and Large Language Models (LLMs) to add intelligence which would improve user experience. Generative AI can be leveraged to classify events more accurately and assist with anomaly detection. LLMs can further be used to create notifications that are tailored to the context and personalized to users behavior, helping to reduce notification fatigue and provide meaningful user alerts. The paper also looks into wider applications of these technologies to add intelligence and improve other related experiences like automated video summarization, proactive security measures, and improved privacy controls. The integration of Generative AI and LLMs with smart home security camera systems advances the smart cameras capabilities and offers enhanced security, personalized user experiences. Keywords—Smart home security, Generative AI, Large Language Models (LLMs), Event detection, Anomaly detection, Notification fatigue, Context-aware notifications, Personalized security, Reinforcement Learning from Human Feedback (RLHF), Internet of Things (IoT).

Generative AI is reshaping the enterprise technology landscape, offering intelligent automation, insight generation, and contextual understanding capabilities that redefine how businesses handle data. Enterprise data management (EDM) - once constrained by rigid architectures, manual processing, and fragmented governance - can now evolve into a dynamic, self-improving ecosystem through the integration of generative AI. With organizations generating petabytes of data from operations, customer interactions, supply chains, and IoT devices, the need for scalable and intelligent data handling systems has never been greater. Generative AI models, including large language models (LLMs) and multimodal transformers, provide new tools for data ingestion, cleansing, integration, transformation, synthesis, and summarization. By applying generative AI to enterprise data workflows, companies can enhance metadata enrichment, automate data cataloging, improve data lineage tracking, and simplify data governance. These capabilities increase data discoverability, trust, and compliance—core principles of modern data management. Additionally, generative AI supports natural language querying, automates report writing, and generates synthetic data for training and simulation, boosting data availability and operational speed. While generative AI brings immense promise, it also raises concerns around hallucination, model transparency, data privacy, and regulatory compliance. Ensuring responsible AI adoption requires rigorous validation, bias mitigation, and alignment with existing data governance policies. Nonetheless, enterprises that embrace generative AI can unlock superior decision-making, improve productivity, and democratize data access across technical and non-technical users. This white paper explores the opportunities, challenges, architectural considerations, and best practices for embedding generative AI into enterprise data management. Through industry examples and forward- looking analysis, it offers a roadmap for transforming data operations and maximizing enterprise intelligence in the era of AI. Keywords: Generative AI, Enterprise Data Management, LLMs, Data Governance, Metadata, Data Cataloging, Synthetic Data, Data Lineage, Natural Language Processing, Responsible AI

The world of health insurance and Medicare has traditionally been perceived as complex and difficult to navigate. Fortunately, the application of Generative AI to virtual agents has begun to transform the industry. Large language and image, AI models, also known as generative AI or foundation models, have opened up new prospects for organizations and people involved in content creation. Once trained, a generative model can be "fine-tuned" for a certain content domain with far less data.

This review paper explores how Generative AI (GAI) and Large Language Models (LLMs) have the potential to reshape smart cities in the industry 5.0 era. By examining case studies and relevant literature, we analyze the influence of these technologies on industrial operations and urban management. The paper focuses on GAI as a key tool for optimizing industries and enabling predictive maintenance, while demonstrating how experts can leverage LLMs to enhance municipal services and communication with citizens. It also discusses the practical and ethical challenges of implementing these technologies. Additionally, the paper highlights emerging trends, illustrated through real-world examples ranging from factories to city-wide pilot projects, and identifies potential pitfalls. The widespread adoption of GAI faces obstacles such as infrastructure constraints and the lack of specialized knowledge needed for effective implementation. While LLMs open new opportunities for citizen services in smart cities, they also raise concerns about privacy, which this study seeks to address. Finally, the paper suggests future research areas, including the development of new ethical AI frameworks and long-term studies on the societal impacts of these technologies. This paper serves as a starting point for industrial leaders and urban developers to navigate the complexities of integrating GAI and LLMs, balancing technological innovation with ethical considerations.

Generative AI, including large language models (LLMs), has transformed the paradigm of data generation and creative content, but this progress raises critical privacy concerns, especially when models are trained on sensitive data. This review provides a comprehensive overview of privacy-preserving techniques aimed at safeguarding data privacy in generative AI, such as differential privacy (DP), federated learning (FL), homomorphic encryption (HE), and secure multi-party computation (SMPC). These techniques mitigate risks like model inversion, data leakage, and membership inference attacks, which are particularly relevant to LLMs. Additionally, the review explores emerging solutions, including privacy-enhancing technologies and post-quantum cryptography, as future directions for enhancing privacy in generative AI systems. Recognizing that achieving absolute privacy is mathematically impossible, the review emphasizes the necessity of aligning technical safeguards with legal and regulatory frameworks to ensure compliance with data protection laws. By discussing the ethical and legal implications of privacy risks in generative AI, the review underscores the need for a balanced approach that considers performance, scalability, and privacy preservation. The findings highlight the need for ongoing research and innovation to develop privacy-preserving techniques that keep pace with the scaling of generative AI, especially in large language models, while adhering to regulatory and ethical standards.

The European Union’s Artificial Intelligence Act (AI Act) is a groundbreaking regulatory framework that integrates technical concepts and terminology from the rapidly evolving ecosystems of AI research and innovation into the legal domain. Precise definitions accessible to both AI experts and lawyers are crucial for the legislation to be effective. This paper provides an interdisciplinary analysis of the concepts of AI system , general purpose AI system , foundation model and generative AI across the different versions of the legal text (Commission proposal, Parliament position and Council General Approach) before the final political agreement. The goal is to help bridge the understanding of these key terms between the technical and legal communities and contribute to a proper implementation of the AI Act. We provide an analysis of the concept of AI system considering its scientific foundation and the crucial role that it plays in the regulation, which requires a sound definition both from legal and technical standpoints. We connect the outcomes of this discussion with the analysis of the concept of general purpose AI system and its evolution during the negotiations. We also address the distinct conceptual meanings of AI system vs AI model and explore the technical nuances of the term foundation model . We conclude that rooting the definition of foundation model to its general purpose capabilities following standardised evaluation methodologies appears to be most appropriate approach. Lastly, we tackle the concept of generative AI , arguing that definitions of AI system that include “content” as one of the system’s outputs already captures it, and concluding that not all generative AI is based on foundation models .

Today's business environment is moving faster than ever, and the expressive and adaptive capabilities of generative AI (GenAI) and large language models (LLMs) are redefining the enterprise rails of tomorrow. Given the abundance of industry hype, investor expectations and leadership pressure, the initial impulse is to ‘get in the game’. But how does one implement initiatives that drive business outcomes within ethical parameters while avoiding technical pitfalls? Marketers need practical guidance to navigate through these changes. In this paper, the authors examine multiple considerations for deployment of GenAI in marketing and customer experience. How does the marketer decide on which initiatives and opportunities to begin with? Which use cases will drive value as the organisation adapts to deploying these new capabilities? Once a marketer has identified the opportunities to capitalise on through GenAI, how is the capability deployed? There are a variety of approaches that can be considered given the level of organisational capability with AI and resource levels to be applied. As with any cutting-edge capability, there are potential missteps that must be avoided to ensure success. This paper provides some insight based on practical experiences to date that cover ethical, technical and process concerns. The paper presents thoughtful approaches to the deployment of LLMs and GenAI that can result in concrete ROI and reduced risk even in this early stage of adoption. With this information, marketers can be prepared to confidently begin their journey using GenAI to transform their customer experience and drive enterprise value for their organisations.

This study explores the transformative impact of Generative AI and Large Language Models (LLMs) on software development by leveraging intelligent automation. It delves into sophisticated methods for refining LLMs to enhance code generation, improve adaptive learning abilities, and support autonomous software engineering processes [1] [2]. This study investigates how these technologies can be integrated into current development workflows to tackle issues such as code quality, scalability, and ethical concerns. Innovative strategies to boost model performance have been introduced, such as targeted data augmentation and domain-specific pre-training. The results showed notable advancements in the accuracy, efficiency, and adaptability of code generation across various programming languages and frameworks. Finally, the study discusses the implications of these developments for future software development and outlines a roadmap for further research and industrial implementation. Keywords — Generative AI, Large Language Models, Intelligent Automation, Software Development, Code Generation, Adaptive Learning, Autonomous Engineering, Data Augmentation, Domain-Specific Pre-trainings, Transfer Learning, Code Quality, Ethical Considerations.

AI models, particularly generative AI and large language models, reshape digital information ecosystems by curating and amplifying content through user engagement metrics. Despite their capacity to reduce bias and promote inclusivity, these models simultaneously amplify cognitive biases, entrench filter bubbles, and spread misinformation. The intensification of human–machine interaction and hyper-industrialization complicates this further, as large language models increasingly mediate how information is produced and consumed. Socio-technical agency describes how AI systems co-construct human behavior and societal norms through their design, yet their effects remain understudied in regions with limited technological infrastructure. This paper investigates AI’s influence on information dissemination, cognitive biases, and user agency across digital media environments in key regions of the Global South. Drawing on qualitative interviews and a survey of 580 media technology users in South Africa, Indonesia, India, the Philippines, and Brazil it examines how generative AI affects emotional engagement, exposure to content, and perceptions of digital truth. Framed by media ecology theory, the study evaluates AI as a cognitive extension that can both reinforce and challenge digital biases. The study proposes strategies for using generative AI to support information integrity while addressing the risks of polarization and exclusion. By centering perspectives from regions in the Global South, it contributes to more equitable discourse on AI governance, advocating regulatory and design solutions responsive to diverse media ecologies.

Recent studies utilizing AI-driven speech-based Alzheimer’s disease (AD) detection have achieved remarkable success in detecting AD dementia through the analysis of audio and text data. However, detecting AD at an early stage of mild cognitive impairment (MCI), remains a challenging task, due to the lack of sufficient training data and imbalanced diagnostic labels. Motivated by recent advanced developments in Generative AI (GAI) and Large Language Models (LLMs), we propose an LLM-based data generation framework, leveraging prior knowledge encoded in LLMs to generate new data samples. Our novel LLM generation framework introduces two novel data generation strategies, namely, the cross-lingual and the counterfactual data generation, facilitating out-of-distribution learning over new data samples to reduce biases in MCI label prediction due to the systematic underrepresentation of MCI subjects in the AD speech dataset. The results have demonstrated that our proposed framework significantly improves MCI Detection Sensitivity and F1-score on average by a maximum of 38% and 31%, respectively. Furthermore, key speech markers in predicting MCI before and after LLM-based data generation have been identified to enhance our understanding of how the novel data generation approach contributes to the reduction of MCI label prediction biases, shedding new light on speech-based MCI detection under low data resource constraint. Our proposed methodology offers a generalized data generation framework for improving downstream prediction tasks in cases where limited and/or imbalanced data have presented significant challenges to AI-driven health decision-making. Future study can focus on incorporating more datasets and exploiting more acoustic features for speech-based MCI detection.

This paper is concerned with issues of trust and bias in generative AI in general, and chatbots based on large language models in particular (e.g. ChatGPT). The discussion argues that intercultural communication scholars must do more to better understand generative AI and more specifically large language models, as such technologies produce and circulate discourse in an ostensibly impartial way, reinforcing the widespread assumption that machines are objective resources for societies to learn about important intercultural issues, such as racism and discrimination. Consequently, there is an urgent need to understand how trust and bias factor into the ways in which such technologies deal with topics and themes central to intercultural communication. It is also important to scrutinize the ways in which societies make use of AI and large language models to carry out important social actions and practices, such as teaching and learning about historical or political issues.

This review paper examines how Generative AI (GAI) and Large Language Models (LLMs) can transform smart cities in the Industry 5.0 era. Through selected case studies and portions of the literature, we analyze these technologies’ impact on industrial processes and urban management. The paper targets GAI as an enabler for industrial optimization and predictive maintenance, underlining how domain experts can work with LLMs to improve municipal services and citizen communication, while addressing the practical and ethical challenges in deploying these technologies. We also highlight promising trends, as reflected in real-world case studies ranging from factories to city-wide test-beds and identify pitfalls to avoid. Widespread adoption of GAI still faces challenges that include infrastructure and lack of specialized knowledge as a limitation of proper implementation. While LLMs enable new services for citizens in smart cities, they also expose certain privacy issues, which we aim to investigate in this study. Finally, as a way forward, the paper suggests future research directions covering new ethical AI frameworks and long-term studies on societal impacts. Our paper is a starting point for industrial pioneers and urban developers to navigate the complexity of GAI and LLM integration, balancing the demands of technological innovation on one hand and ethical responsibility on the other.

With the growing capabilities of generative AI, enhancing search infrastructures by building inference pipelines has become essential for achieving more relevant and context-aware search results. Traditional search engines are largely dependent on keyword matching and limited natural language processing techniques, which often fail to understand complex user intents or handle ambiguous queries effectively. Generative AI, particularly large language models (LLMs) and transformer-based architectures, enables deeper semantic understanding and the ability to generate contextually rich responses. By embedding generative AI into search pipelines, it becomes possible to deliver personalized and nuanced results, increasing both relevance and user satisfaction. Inference pipelines equipped with generative AI can dynamically adapt to user queries, offering a multi-step process where search engines first analyze the query's intent and then employ the language model to retrieve and rank relevant information. This multi-layered approach involves stages such as query expansion, semantic matching, content summarization, and reranking of results, all driven by AI inferences. Advanced natural language understanding (NLU) models are used to decompose complex queries and match them against large datasets, while natural language generation (NLG) models summarize or rephrase responses for clarity. Moreover, generative AI can improve the search experience by providing contextual suggestions, summaries, or even direct answers to queries, thereby reducing user effort. In practice, these inference pipelines can be integrated into existing search frameworks through microservices or APIs, allowing for modular scalability and ease of deployment across varied infrastructures. This setup supports real-time processing, low latency, and optimized resource allocation, essential for handling high query volumes. Additionally, with the advent of hybrid retrieval-augmentation systems, these AI-driven pipelines enable both keyword and semantic search capabilities, leading to a more robust, adaptable search experience.