Generative AI Applications for Enhancing Medical Training

Generative AI is basically a subfield of artificial intelligence. It mainly focuses on developing systems that can generate creative outputs such as images, music, text, and more. By deep learning techniques, Generative models are capable of independent producing content that look like human-generated creations. The key characteristic of Generative AI is its capacity to learn from huge datasets, catch patterns, and generate new content that show similar characteristics. In recent years, Generative AI models such as generative adversarial networks (GANs) and variational autoencoders (VAEs). GANs consist of two components: a generator network and a discriminator network those engaged in a competitive process of generating and evaluating content. VAEs employ an encoder-decoder architecture to learn and generate new samples. This paper discusses the key areas where Generative AI is expected to make significant contributions in the future. These areas include: Healthcare, Art and Entertainment, Ethical and Societal Considerations, Autonomous Systems, Content Creation etc.

Generative Artificial Intelligence Techniques is an AI system capable of generating new, original content. The field of artificial intelligence is witnessing a period of rapid growth, driven by the emergence of large generative AI models in recent years. OpenAI introduced ChatGPT, a dialogue chatbot with advanced natural language generation capabilities, which garnered significant attention globally. This led to a surge in the development of large generative AI models, including Gemini, Copilot, LLaMA, SAM, SORA, and numerous others. At the present time, the information age is entering a phase of accelerated development characterized by the advent of intelligent computing. The breakthroughs in generative AI technology that have been achieved to date are numerous and significant, and they are gradually and profoundly transforming thousands of industries.First, this paper elaborates on the development history of artificial technology, especially the iteration of generative AI technology. Secondly, this paper provides a systematic overview of the current status of the application of generative AI technology in education, and at the same time analyzes the role played by generative AI technology in the digital transformation of schools. Finally, the paper sheds light on the issues and challenges that generative AI technologies will bring to education.

Generative artificial intelligence, enabled through sophisticated machine learning frameworks, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), is disrupting the creative landscape within many of the creative industries, opening a new set of tools for content creation. Therefore, this article investigates the disruptions generative models present across multiple aspects of creative production, focusing on the influences of art, music, literature, and design. In the visual arts, generative AI produces original artworks that blend aesthetics and styles that extend conventional notions of creativity; in music, AI-composition tools allow musicians to compose original musical works and experiment with entirely new genres. In literature and storytelling, the same generative processes offer AI systems the ability to propose narrative frameworks and other textual elements that may instigate human writers to take these concepts further with a narrative or may stand on their own as original content. In design, generative algorithms support product development all the way down to quickly prototyping new products. The paper includes specific case studies or outlines of the application of generative AI tools to proposed projects, a discussion of effectiveness with the respective generative model, and the potential limitations generative models present for each creative area. The paper will also reflect on the threats generative AI may pose, the potential to redefine human creativity, originality, and ownership, implications for the potential decision-making capabilities of these systems, and if there may be consequences for society. In all these considerations, there are opportunities for and more significant implications of the potential of generative AI to assist and expand human creative possibilities throughout the entire creative process.

Generative artificial intelligence, the new buzzword in technology, is the next step in the evolution of traditional artificial intelligence. Unlike traditional AI that excels in data analyzing and automating processes, generative AI (GenAI) is a pioneer in creating new and original content. GenAI is very close to human intelligence, capable of logical thinking, imitating human behavior and armed with decision making capabilities. Generative AI creates new texts, images, music, 3D designs and codes, thus strongly influencing the activities, strategies, and consumer interactions of various industries. Key industries most affected by GenAI are banking and finance, retail and consumer goods, medicine and pharmaceuticals, education, media and marketing. In marketing, generative AI is significant in the process of personalization, content creation, audience engagement and interactions, performing the STP strategy (segmentation, targeting, positioning), market research, etc. Although it has great advantages, GenAI also has significant limitations, such as unresolved ethical issues, the spread of outdated or imprecise data, lack of legal regulation and control, etc. This paper, with the aid of secondary research, is aimed at exploring the possibilities of GenAI and its impact on marketing, especially advertising.

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

In the architectural exterior design domain, design intent is usually expressed by textual design intent [e.g., client needs, architectural language (AL)] and non-verbal design intent (e.g., sketch). However, existing generative AI-based methods for automated architectural exterior conceptual design can only use the general image description as the prompt. Thus, despite its potential, existing generative image AI cannot produce appropriate design alternatives that meet various design requirements. Enabling automated architectural exterior conceptual design requires solving two problems: teaching the AI model to understand textual design intent and allowing generative AI to combine textual design intent with non-verbal design intent. The study aims to propose an automated architectural exterior conceptual design approach by incorporating domain-specific prompting strategies and sketch-to-image synthesis into fine-tuned generative image AI models. In the proposed approach, textual design intent annotations (including client needs and AL) are added to architectural images and general image description annotations. Web crawler and ChatGPT automatically extract design intent-related annotations from online sources for famous architectural works that are used as training images. The constructed dataset is then used to fine-tune a generative AI model [i.e., Stable Diffusion (SD)] via the Lora algorithm, teaching the AI model to understand textual design intent. Also, ControlNet is used to control the generation process of the SD model to enable the generative AI to reflect the design intent expressed by the sketches. The proposed approach is validated by comparing generated images from our approach with those from two existing models. The results show that the proposed method can successfully generate architectural exterior conceptual design images that fulfil the requirements based on the architectural design intent. The proposed approach is expected to streamline and facilitate time-consuming and demanding iterative processes during a conceptual design phase.

This research paper explores the transformative potential of generative AI in the context of document processing within large financial organizations, with a particular focus on fraud detection. As financial institutions increasingly rely on vast amounts of documentation for operations ranging from customer onboarding to compliance, the inefficiencies and limitations of traditional manual processing methods become glaringly apparent. These legacy systems are not only time-consuming and prone to human error but also struggle with scalability, a critical requirement in today’s fast-paced financial environment. Moreover, manual systems and traditional Optical Character Recognition (OCR) engines often lack the necessary accuracy and contextual understanding to reliably process complex financial documents and detect fraudulent activities. While OCR technology has automated certain aspects of document processing, its inherent limitations in accuracy, particularly in dealing with degraded documents or complex layouts, and its inability to interpret context, significantly impede its effectiveness in high-stakes financial applications. Furthermore, OCR’s limited capability in detecting subtle indicators of fraud leaves financial organizations vulnerable to increasingly sophisticated fraudulent schemes. Generative AI emerges as a revolutionary solution to these challenges by enhancing the accuracy, scalability, and security of document processing systems. Unlike traditional OCR, generative AI models are designed to understand and interpret the context of documents, thereby significantly improving the accuracy of text recognition, even in complex scenarios. These AI models, trained on vast datasets, are capable of processing large volumes of documents in parallel, making them ideally suited for the high-speed, high-volume environments characteristic of financial institutions. Additionally, generative AI incorporates advanced algorithms that enhance fraud detection capabilities by analyzing patterns, detecting anomalies, and cross-referencing data across multiple documents. This approach not only improves the detection of fraudulent activities but also reduces the likelihood of false positives, thereby enhancing the overall reliability of the system. The paper further delves into the practical applications of generative AI in various critical areas within financial organizations. Key applications include Know Your Customer (KYC) compliance, where AI streamlines the processing and verification of customer documents, thereby ensuring both compliance with regulatory requirements and the authenticity of the information provided. In loan processing, generative AI accelerates the analysis of loan applications, providing real-time risk assessments that enable faster decision-making. Additionally, the technology is applied in invoice and payment processing, where it automates and verifies transactions, reducing errors and ensuring the timely execution of financial operations. In the realm of contract analysis, generative AI facilitates the extraction and interpretation of key terms and clauses, enabling more effective contract negotiation and management. Beyond its practical applications, the paper also addresses the continuous learning capabilities of generative AI models, which allow them to evolve and adapt to new data and document types over time. This feature is particularly crucial in the financial sector, where the types of documents and the nature of fraudulent activities are continually changing. The continuous learning aspect of generative AI ensures that the systems remain up-to-date and effective, even as new challenges and document types emerge. The research also highlights the comparative analysis between traditional OCR-based systems and AI-powered systems, demonstrating the superior performance, efficiency, and scalability of the latter. Moreover, the paper discusses the challenges associated with the implementation of generative AI in financial document processing. These include technical challenges such as the integration of AI systems with existing IT infrastructure, as well as regulatory and compliance issues that arise when deploying AI technologies in the highly regulated financial sector. Despite these challenges, the paper argues that the long-term benefits of adopting generative AI, including improved accuracy, enhanced fraud detection, and greater operational efficiency, far outweigh the initial hurdles. The research also considers the future of generative AI in financial document processing, suggesting that as the technology continues to advance, its applications and benefits will expand even further. Future research opportunities are identified, particularly in the areas of improving the efficiency and scalability of AI models, enhancing their ability to handle increasingly complex document types, and developing more sophisticated fraud detection algorithms. The paper concludes with a discussion on the potential long-term impact of generative AI on the financial industry, arguing that it will play a crucial role in shaping the future of financial operations by providing more accurate, scalable, and secure document processing solutions. This paper makes a significant contribution to the existing body of knowledge on the application of AI in financial services, particularly in the area of document processing and fraud detection. By providing a detailed analysis of the challenges faced by financial organizations and demonstrating how generative AI can address these challenges, the research offers valuable insights for both academic researchers and practitioners in the field. The findings presented in this paper have important implications for the future of document processing in financial organizations, suggesting that the adoption of generative AI will be essential for maintaining operational efficiency, accuracy, and security in an increasingly complex and fast-paced financial environment. In summary, this research not only highlights the transformative potential of generative AI in financial document processing but also provides a roadmap for its successful implementation in large financial organizations, with a particular emphasis on enhancing fraud detection capabilities.

Generative AI is a new branch of artificial intelligence, which creates fresh content using neural networks and machine learning methods. Systems of generative AI can generate music, images, text, speech, and other types of content by finding new styles in huge databases. The automation of tedious tasks through the creation of personalized content, and the improvement of accuracy in difficult tasks makes generative AI technology to transform a variety of industries, including gaming, advertising, and healthcare. There are many types of generative AI models. Each has pros and cons of its own. Despite being a relatively young technology, generative AI has many potential applications that make it a fascinating field to research. More research, growth, and advancement in the future may be seen. Future potential uses for generative AI include improving cybersecurity by identifying and preventing cyberattacks, creating human-interactive virtual assistants, and creating intelligent robots that can do challenging tasks in various industries. As generative AI continues to be developed, we should expect to see increasingly sophisticated applications in the years to come, which will open up new opportunities for growth across numerous industries.

This paper delves into the realm of recent advancements in artificial intelligence, with a particular focus on Generative AI. Generative AI, an emerging field within AI, leverages machine learning algorithms and neural networks to generate original content across various mediums such as images, music, speech, and text. Its potential to revolutionize industries like advertising, gaming, and healthcare through personalized content creation, task automation, and enhanced accuracy in complex endeavors like drug discovery and medical diagnosis is profound. We explore different models of Generative AI, highlighting their strengths and limitations. Despite being in its early stages, Generative AI presents a promising avenue for research and development, offering numerous unexplored opportunities. Examples of prominent Generative AI models such as ChatGPT and DALL-E are provided, elucidating their applications across diverse domains. Looking forward, the potential applications of Generative AI are vast, including the development of virtual assistants for human interaction, bolstering cybersecurity, and designing intelligent robots for industrial tasks. As Generative AI continues to advance, it holds the promise of driving innovation and transformation across industries, paving the way for growth and progress in the future. Key Words: Generative AI, artificial intelligence, content generation, machine learning, neural networks, industry applications, innovation.

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.

Artificial Intelligence has become a focal point of interest across various sectors due to its ability to generate creative and realistic outputs. A specific subset, generative artificial intelligence, has seen significant growth, particularly in late 2022. Tools like ChatGPT, Dall-E, or Midjourney have democratized access to Large Language Models, enabling the creation of human-like content. However, the concept 'Generative Artificial Intelligence lacks a universally accepted definition, leading to potential misunderstandings. While a model that produces any output can be technically seen as generative, the Artificial Intelligent research community often reserves the term for complex models that generate high-quality, human-like material. This paper presents a literature mapping of AI-driven content generation, analyzing 631 solutions published over the last five years to better understand and characterize the Generative Artificial Intelligence landscape. Our findings suggest a dichotomy in the understanding and application of the term "Generative AI". While the broader public often interprets "Generative AI" as AI-driven creation of tangible content, the AI research community mainly discusses generative implementations with an emphasis on the models in use, without explicitly categorizing their work under the term "Generative AI".

With the development of AI technology, generative AI has gradually entered the life of the public, for example, the explosion of CHAT-GPT has allowed more people to see the huge potential and obvious advantages of generative AI. However, in the process of generative AI operation, events that violate social responsibility and ethics often occur, which makes the research on the scientific and technological ethics of generative AI more urgent. In the past literature and research, many industry experts have analysed the impact of generative AI on specific industries, but everyone is or will be a user of generative AI, so we should pay attention to the study of the people's scientific and technological ethical issues of generative AI after putting aside the industry background, so this paper collects primary data by means of questionnaire surveys to find out the public's awareness of generative AI and their perception of generative AI. and attitudes towards generative AI, and using the decision tree C4.5 algorithm with Python as the tool, it is used to respond to people's awareness of generative AI and the public's perception of the relationship between the various factors of the ethical issues of

• A dual taxonomy is introduced linking generative AI tools with reinforcement learning. • First review to analyze RL training and fine-tuning of generative policies for robotics control tasks. • Covers 245 papers integrating Transformer and Diffusion-based architectures into RL pipelines. • Highlights key roles of LLMs, VLMs, diffusion models, world and video prediction models in robotics policy learning. • Identifies open challenges in grounding, scalability, and safety of robotics generative policies. Recently, generative AI and reinforcement learning (RL) have been redefining what is possible for AI agents that take information flows as input and produce intelligent behavior. As a result, we are seeing similar advancements in embodied AI and robotics for control policy generation. Our review paper examines the integration of generative AI models with RL to advance robotics. Our primary focus is on the duality between generative AI and RL for robotics downstream tasks. Specifically, we investigate: (1) The role of prominent generative AI tools as modular priors for multi-modal input fusion in RL tasks. (2) How RL can train, fine-tune and distill generative models for policy generation, such as VLA models, similarly to RL applications in large language models. We then propose a new taxonomy based on a considerable amount of selected papers. Lastly, we identify open challenges accounting for model scalability, adaptation and grounding, giving recommendations and insights on future research directions. We reflect on which generative AI models best fit the RL tasks and why. On the other side, we reflect on important issues inherent to RL-enhanced generative policies, such as safety concerns and failure modes, and what are the limitations of current methods. A curated collection of relevant research papers is maintained on our GitHub repository , serving as a resource for ongoing research and development in this field.

Generative Artificial Intelligence or Generative AI is an innovation with a terrible name.First, generative means something that originates, produces, or even reproduces.Next, artificial Intelligence is defined as intelligence exhibited by machines (machine intelligence).So, if the two are put together the result is a machine that originates intelligence—in other words, a thinking machine. Hmmm!Generative artificial intelligence is typically defined as a technology capable of generating text, images, videos, or other data, usually in response to prompts.At best the name generative AI is confusing, and to many it is scary.Everett Rogers in his classic book Diffusion of Innovations (2003) states that “the name given to an innovation often affects its perceived compatibility, and therefore its rate of adoption (p. 250).”Certainly, Generative AI is an innovation—it is new and is probably the innovation of this decade.Everett Rogers defined Innovation as “an idea, practice or object that is perceived as new by an individual or other unit of adoption (p. 12)”.What does all this mean to distance educators?Maybe first we should learn more about Generative AI. So, just for the fun of it, the written sections above were entered into Gemini AI, a generative AI tool, and a competitor to ChatGPT.Here is what Gemini produced:You’re right, “Generative AI” while descriptive, and can be misleading.Here’s a breakdown of why it might not be the best term and some suggestions for alternatives:The Problems with “Generative AI”The best term will depend on the context. “Machine Learning for Content Creation” might be more suitable for a technical audience, while “Creative AI” could be better for a general audience.This revision process took about 5 seconds, and by any measure the results were impressive—the Generative AI analysis seemed insightful and accurate.Rogers recommends that potential innovation users should learn about the innovation by studying its attributes—there are five—relative advantage, compatibility, complexity, trialability, and observability.First is the idea of relative advantage—defined as the degree to which an innovation is perceived as being better than the idea it replaces. Relative advantage is often expressed as the innovation’s economic profitability or its ability to convey social positions.The next characteristic of an innovation is its compatibility, which is the degree to which the innovation is consistent with existing values, past experiences, or user needs. Innovations can be either compatible or incompatible.Next is complexity, explained as whether the innovation is perceived as difficult or easy to use.A complex innovation, as perceived by potential adopters, can significantly hinder adoption.Trialability is the ability to experiment with an innovation before adoption. If an innovation can be easily tried it will be likely to have a rapid rate of adoption or rejection.Observability is the visibility of applying the innovation. Observability means seeing the results of using the innovation. High observability promotes faster decisions about adoption.In other words, does Generative AI allow us to do things better? Next, are the results of use compatible with what the user needs or wants? Third, is Generative AI easy or difficult to use, and can we quickly try it out? Finally, can we see the results when Generative AI is used?Computer scientists argue that Generative AI is better, compatible, easier, not complex, and results are clear—maybe this is true, but the name is still horrible.Generative artificial intelligence sounds intimidating and threatening. Perhaps ‘creative artificial intelligence’ would be more inviting—perhaps not!Distance educators should understand, study, and evaluate Generative Artificial intelligence and write about it—perhaps someone from the U.S. Distance Learning Association could “coin” a new name.(Distance Learning would love to publish manuscripts on this and other related topics.)And finally, Galen said “The chief merit of language is clearness, and we know that nothing detracts so much from this as do unfamiliar terms.”NOTE: Rewrites of the second half of this column using personal intelligence (PI) took 5 tries and 4 hours—a relative advantage?

In recent years, generative AI has attracted significant public attention, and its use has been rapidly expanding across a wide range of domains. From creative tasks such as text summarization, idea generation, and source code generation, to the streamlining of medical support tasks like diagnostic report generation and summarization, AI is now deeply involved in many areas. Today’s breadth of AI applications is clearly distinct from what was seen before generative AI gained widespread recognition. Representative generative AI services include DALL·E 3 (OpenAI, California, USA) and Stable Diffusion (Stability AI, London, England, UK) for image generation, ChatGPT (OpenAI, California, USA), and Gemini (Google, California, USA) for text generation. The rise of generative AI has been influenced by advances in deep learning models and the scaling up of data, models, and computational resources based on the Scaling Laws. Moreover, the emergence of foundation models, which are trained on large-scale datasets and possess general-purpose knowledge applicable to various downstream tasks, is creating a new paradigm in AI development. These shifts brought about by generative AI and foundation models also profoundly impact medical image processing, fundamentally changing the framework for AI development in healthcare. This paper provides an overview of diffusion models used in image generation AI and large language models (LLMs) used in text generation AI, and introduces their applications in medical support. This paper also discusses foundation models, which are gaining attention alongside generative AI, including their construction methods and applications in the medical field. Finally, the paper explores how to develop foundation models and high-performance AI for medical support by fully utilizing national data and computational resources.