Development Of AI Research Articles

The Impact of AI Development on Fertility Rate

Fertility treatments are set to change with the help of AI technology, which is now known to optimize fertility treatments. Evidence shows that AIs cutting-edge technologies can help eliminate infertility issues through an optimized treatment of genetic disorders and optimized embryo selections. At the same time, the need to address infertility issues has been considered a global health problem, which is indicated by the decline in global fertility rates. These fertility rates are posing a major crisis in the future in the nation that is currently dealing with an aging population and major declines. The data collection showed that there was a higher number of ART births that were directly influenced by AI-enhanced fertility technologies. The success rates, as well as the rates of failed fertilization show whether AI technologies have a positive or negative impact on fertility rates and whether they should be considered the game changer in a city and a nation where fertility rates have been declining since the early 2000s. A scoping review of the literature provided an overview of the ways in which fertility clinics are now using AI technology to enhance the chance of success for their patients.

Hybrid intelligence – systematic approach and framework to determine the level of Human-AI collaboration for production management use cases

AbstractArtificial intelligence (AI) and machine learning (ML) have emerged as transformative technologies in production management, enhancing decision-making and operational efficiency across a range of use cases. The challenge of determining the optimal level of Human-AI collaboration in decision-making processes persists for many organizations, despite the potential benefits of such integration. However, the current systematic approaches frequently lack a structured approach to determine the level of Human-AI collaboration in production management use cases. This results in inconsistent applications and suboptimal outcomes. This study presents a first and superior systematic approach for the systematic evaluation, development and implementation of AI in production management and introduces a structured framework that can be employed to assess and determine the optimal level of Human-AI collaboration for a range of production use cases. The framework classifies use cases based on critical factors such as data availability, process variability, error susceptibility, and decision complexity. This classification assists managers in calibrating the balance between automation and human intervention. The application of this framework could enhance the efficacy, productivity, and accuracy of Human-AI collaboration in production management. Furthermore, the systematic approach incorporates continuous evaluation and adaptation, ensuring long-term success in dynamic production environments. These findings provide a practical tool for managers to strategically implement AI, improving decision-making processes and operational outcomes. The proposed framework offers immediate opportunities to enhance production management through a structured, scalable, and human-centered approach to AI integration.

Exploring Generative AI as a Catalyst for Sustainability: Strategies for Waste and Energy Reduction

The study explores the potential of generative AI as a transformative tool for advancing sustainability, focusing on waste and energy reduction strategies. Generative AI technologies offer innovative solutions for optimizing resource use, minimizing material waste, and improving energy efficiency across various industries. By leveraging AI for predictive analytics, process automation, and real-time decision-making, businesses can significantly reduce their environmental impact. However, the integration of AI into sustainability efforts also raises important policy considerations, particularly regarding energy consumption during AI model development and deployment. The study aims to provide a comprehensive analysis of the opportunities and challenges associated with using generative AI to drive sustainable practices, offering strategic recommendations for policymakers and organizations alike. Generative AI can model complex systems, simulate environmental impacts, and optimize production processes, leading to reduced material waste, lower energy consumption, and more efficient resource allocation. Industries such as manufacturing, energy, and logistics stand to benefit immensely from AI-driven innovations, which can refine processes, predict maintenance needs, and optimize supply chains. Nevertheless, the widespread implementation of generative AI comes with challenges, especially the high energy demands of AI training models, which could offset some sustainability gains. As a result, the paper underscores the need for balanced policy frameworks that encourage AI adoption while promoting sustainable AI development practices, such as the use of energy-efficient hardware and renewable energy sources. The study also highlights the potential for generative AI to influence global sustainability targets, driving a shift towards greener, smarter technologies and providing a pathway for industries to achieve net-zero emissions.

Development of transient ischemic attack risk prediction model suitable for initializing a learning health system unit using electronic medical records

BackgroundPatients with transient ischemic attack (TIA) face a significantly increased risk of stroke. However, TIA screening and early detection rates are low, especially in developing countries. This study aims to develop an inclusive and practical TIA risk prediction model using machine learning (ML) that performs well in both hospital and resource-limited clinic settings. This model is essential for initiating the first ML-enabled learning health system (LHS) unit designed for routine and equitable TIA screening and early detection across broad populations.MethodsEmploying a novel protocol, this study first standardized data from a hospital’s electronic medical records (EMR) to construct inclusive TIA risk prediction ML models using a data-centric approach. Subsequently, a quantitative distribution of TIA risk factors was applied in feature engineering to reduce the number of variables for a practical ML model. This refined model initiated a TIA ML-LHS unit that is capable of continuously updating with new EMR data from hospitals and clinics. Additionally, the practical model underwent external validation using data from another hospital.ResultsThe inclusive 150-variable ML models, derived from all available EMR variables for TIA, achieved a recall of 0.868 and an accuracy of 0.886 in predicting TIA risk. Further feature engineering produced a practical XGBoost model with 20 variables, maintaining acceptable performance of 0.855 recall and 0.796 accuracy. The initialized TIA ML-LHS unit, based on the practical model, achieved performance metrics of 0.830 recall, 0.726 precision, 0.816 ROC-AUC, and 0.812 accuracy. The model also performed well in external validation, confirming its effectiveness with patient data from different clinical settings.ConclusionsThis study developed the first inclusive and practical TIA XGBoost model from full hospital EHR and initiated the first TIA risk prediction ML-LHS unit. This TIA model, which requires only 20 variables, enables the ML-LHS to serve not only patients in hospitals but also those in resource-limited clinics. These results have significant implications for expanding risk-based TIA screening in community and rural clinics, thereby enhancing early detection of TIA among underserved populations and improving health equity. The novel protocol used in this study is also applicable for initiating ML-LHS units for various preventable diseases, providing a new system-level approach to responsible AI development and applications.

A Bias Network Approach (BNA) to Encourage Ethical Reflection Among AI Developers.

We introduce the Bias Network Approach (BNA) as a sociotechnical method for AI developers to identify, map, and relate biases across the AI development process. This approach addresses the limitations of what we call the "isolationist approach to AI bias," a trend in AI literature where biases are seen as separate occurrences linked to specific stages in an AI pipeline. Dealing with these multiple biases can trigger a sense of excessive overload in managing each potential bias individually or promote the adoption of an uncritical approach to understanding the influence of biases in developers' decision-making. The BNA fosters dialogue and a critical stance among developers, guided by external experts, using graphical representations to depict biased connections. To test the BNA, we conducted a pilot case study on the "waiting list" project, involving a small AI developer team creating a healthcare waiting list NPL model in Chile. The analysis showed promising findings: (i) the BNA aids in visualizing interconnected biases and their impacts, facilitating ethical reflection in a more accessible way; (ii) it promotes transparency in decision-making throughout AI development; and (iii) more focus is necessary on professional biases and material limitations as sources of bias in AI development.

The dual faces of social media: connectivity and fraud in the digital age

Purpose This paper aims to explore fraud’s pervasive nature and social media’s critical role in modern forensic investigations. As fraudsters increasingly exploit social media, investigators must continuously educate themselves on emerging means of effectively identifying, collecting and analyzing social media data. This paper highlights the role of social media in detecting and preventing fraud by providing digital evidence. It outlines the forensic process – evidence identification, collection and examination – and emphasizes using commercial and open-source tools to gather, analyze and secure evidence. The research contributes insights into the evolving fraud detection techniques using social media. Design/methodology/approach This paper uses a narrative review (Sylvester et al., 2013). Findings Social media can aid fraud investigations by revealing suspect behaviors (witness tampering in The Prosecutor v. Jean-Pierre Bemba Gombo), networks (Operation Firewall) and communications, offering real-time data, and providing evidence of fraudulent activities through messages, connections and posts (US v. Brooklyn Men). An extensive review of commercial forensic software packages highlights their utility in preserving and verifying social media data’s authenticity for legal proceedings. In addition, the paper discusses open-source tools like HTTrack and TweetBeaver. Research limitations/implications An extensive review of commercial forensic software packages highlights their utility in preserving and verifying social media data’s authenticity for legal proceedings. In addition, the paper discusses open-source tools like HTTrack and TweetBeaver. Future research directions discussed include the development of advanced SOCMINT tools, AI and machine learning integration and examining how social media policy changes impact forensic investigations. Practical implications The social media forensic analysis procedure includes these steps: forensic investigator orientation, fraud identification, due diligence search, collection and preservation of social media data, technical search and forensic analysis. The paper also addresses the challenge posed by increasing awareness among criminals, who may alter their online behaviors to evade detection. Originality/value All content in this paper is original in its creation.

Challenges in Value-Sensitive AI Design: Insights from AI Practitioner Interviews

As AI systems become increasingly prevalent in critical domains, ensuring their alignment with stakeholders’ values is essential. However, recent studies have revealed deficiencies in socio-technical design processes and design activities for AI, particularly in eliciting diverse stakeholder values and integrating them into system design and development. To investigate these challenges empirically, we conducted 30 semi-structured interviews with AI practitioners. Our findings reveal several key challenges faced during AI design and development. Firstly, practitioners struggle with identifying and involving stakeholders due to uncertainties regarding user demo-graphics, and a lack of interdisciplinary expertise. Secondly, they encounter obstacles in integrating values into technologies, citing practical complexities, unclear responsibilities, and limited support. This paper presents a concept map detailing these four primary barriers and discusses potential strategies and recommendations for overcoming them. These strategies revolve around improving value elicitation practices, facilitating more meaningful stakeholder engagement, and understanding the impact of stakeholder values By qualitatively describing the barriers to integrating stakeholder values into AI systems, this study contributes to the emerging field of value-sensitive AI.

Students' AI-generated Images: Impact on Motivation, Learning and, Satisfaction

In the contemporary society where the rapid development of generative AI (GenAI) infiltrate daily life, it is imperative for schools to keep up with the development. Future generations are expected to have GenAI skills and knowledge in the same way that traditional literacy is a recruitment condition today. School curricula need to integrate this new technology to support students' learning and development. Research on artificial in education (AIED) has reported on the challenges with the involvement of GenAI in teaching and learning, but also on GenAi as a study support. To not fall behind many schools have started out with AIED initiatives which creates a need for studying how GenAI could be applied in a useful way in teaching and learning activities. The research question that guided this study was: In what ways could the use of GAI in visual form support students' learning process and motivation in upper secondary school settings? The overall research strategy was a qualitative case study approach with investigator triangulation. Data were collected in a combination of observations at workshop sessions and semi-structured interviews. with teachers as well as students. In a six-step inductive thematic analysis data excerpts were coded, aggregated to categories, and presented. Findings indicate that GenAI tools for image generation, can have a positive effect on learning. At the same time that memorisation of information is supported, there were also a positive impact on motivation and student satisfaction. The involvement of image generation tools not a substitute, but rather a complement to traditional teaching and learning activities. The conclusion is that the use of AI in education can offer new learning opportunities, and with the increased use of GenAI, it is crucial for both students and teachers to keep pace. However, this would require that more time and resources for teacher professional development on AIED.

Discourse Around the Ethics of Artificial Intelligence: Features of Formation and Institutionalization

The purpose of the article is to identify the features of the formation of the discourse around the ethics of artificial intelligence, as well as to characterize the main legal acts that set out its key principles. Research methods. The methods of analysis and synthesis, generalization and abstraction were applied, which made it possible to achieve the set goal. The scientific novelty lies in identifying the features of the public (determined by the strategies and limitations characteristic of the media arena) and academic (long, deep and reasoned discussions of researchers) discourses around the ethics of artificial intelligence, clarifying the main approaches (risk-oriented, according to the areas of application) to setting out its key principles in legal acts at the international level, emphasizing the need to improve the tools for managing AI technologies, in particular the creation of global governance structures to prevent their misuse. Conclusions. It is emphasized that, given the steady growth in the scale of data and AI use worldwide, it is necessary to systematically make efforts to increase literacy, awareness and education about the ethical consequences of the use of AI technologies. Ethical challenges associated with different ways of using AI require interdisciplinary interaction and interaction with many stakeholders, as well as cooperation between cultures, organizations, academic institutions, etc. By directly addressing the ethical issues surrounding the development and use of AI, collaboration between policymakers, technologists, and ethicists can ensure that AI serves humanity responsibly and fairly. It is emphasized that, despite the promotion of policy approaches to regulating AI by some countries and international organizations, the impact of corporate investment in AI and the political responses associated with governance have yet to be assessed.

Is the Rule-Based Order in AI Dead, or is Still Kicking?

This article focuses on the possibility of revival of the classical rule-based approach in AI for building heterogeneous AI systems. It is based on personal experience while working on several research and innovation projects in diverse areas - business process management, unauthorized intrusion detection, malicious interference protection, digital forensics and diagnostics, data management, etc. Despite their differences, all these areas share something in common: they require multiple operations to be executed in a single transaction and incorporate heuristic rules for different purposes, related to data, knowledge, and operation management. The position of this article is that combining the classical rulebased approach from the early days of AI with more recent developments in AI, such as data-focused machine learning and utility-based reinforcement learning, as well as utilization of the recent technological developments on the cloud and at data centers can be beneficial for widening the realworld application of AI. The challenges, which this complexity creates require joined efforts of academic researchers, industrial engineers, and business enablers. Collaboration between them across the board can be highly beneficial and the author is looking for opportunities in this direction.

The critical need for an open medical imaging database in Japan: implications for global health and AI development.

Japan leads OECD countries in medical imaging technology deployment but lacks open, large-scale medical imaging databases crucial for AI development. While Japan maintains extensive repositories, access restrictions limit their research utility, contrasting with open databases like the US Cancer Imaging Archive and UK Biobank. The 2018 Next Generation Medical Infrastructure Act attempted to address this through new data-sharing frameworks, but implementation has been limited by strict privacy regulations and institutional resistance. This data gap risks compromising AI system performance for Japanese patients and limits global medical AI advancement. The solution lies not in developing individual AI models, but in democratizing access to well-curated Japanese medical imaging data. By implementing privacy-preserving techniques and streamlining regulatory processes, Japan could enhance domestic healthcare outcomes while contributing to more robust global AI models, ultimately reclaiming its position as a leader in medical innovation.

Comparing the Paraphrasing Ability of ChatGPT and Kimi AI in Jane Eyre：a Qualitative Study

This study compares the paraphrasing abilities of ChatGPT and Kimi AI in Jane Eyre, based on a linguistic typology standard. Firstly, the data shows a general paraphrasing situation across each facet of the standard, demonstrating the two Large Language Models(LLMs) performed better in their original source languages, English and Chinese. Subsequently, qualitative case studies demonstrate the discrepancy of single quantitative study and delves deeper into the English-Chinese comparison behind the behaviour of the two models, which is analyzed with comparative Linguistics, advanced translation studies and even Literature theories. The results demonstrate that English is more hypotactic, using more variations of words or clause to express compared to Chinese. Chinese, on the other hand, is more paratactic, requiring more modifiers to concretize the same expression. In conclusion, this paper sheds light on and contributes to the linguistic characteristics of the source languages in developing and refining LLMs for NLP tasks. Meanwhile, concerns are raised about the limitations due to the limited case study and translation deviation. Additionally, this paper rasies suggestions for future AI modification and personalizes development of future LLMs and AI.

Responsible AI Development: A Comprehensive Framework for Ethical Implementation in Contemporary Technological Systems

Responsible AI Development: A Comprehensive Framework for Ethical Implementation in Contemporary Technological Systems

A Radiologist's Perspective of Medical Annotations for AI Programs: The Entire Journey from Its Planning to Execution, Challenges Faced

AbstractArtificial intelligence (AI) in radiology and medical science is finding increasing applications with annotations being an integral part of AI development. While annotation may be perceived as passive work of labeling a certain anatomy, the radiologist plays a more important role in this task apart from marking the structures needed. Apart from annotation, more important aspect of their role is planning the anatomies/pathologies needed, type of annotations to be done, choice of the annotation tool, training the annotators, planning the duration of annotation, etc. A close interaction with the technical team is a key factor in the success of the annotations. The quality check of both the internally and externally annotated data, creating a team of good annotators, training them, and periodically reviewing the quality of data become an integral part of their work. Documentation related to the annotation work is another important area where the clinician plays an integral role to comply with the Food and Drug Administration requirements, focused on a clinically explainable and validated AI algorithms. Thus, the clinician becomes an integral part in the ideation, design, implementation/execution of annotations, and its quality control. This article summarizes the experiences gained during planning and executing the annotations for multiple annotation projects involving various imaging modalities with different pathologies.

Imagining Human-AI Memory Symbiosis

Abstract In this article, we critically examine the history of artificial intelligence (ai) to explore how it can shape the future of collective memory. By situating our work within critical debates that challenge the dominant anthropomorphising discourse of ai, we scrutinise how ai specificities shape its interactions with information about the past. We highlight how the development of ai has been often misremembered in public discourse, and by extension in the humanities, and explore the consequences for ai’s emerging status as a form of media memory. Based on our exploration, we outline three scenarios for the future of ai-shaped collective memory: 1) the reiteration of the simulative paradigm of ai media memory; 2) the enfolding of ai as an alien actant in human (memory) collectives; and 3) the recognition of the radical alterity of ai for human-ai memory symbiosis.

Image-Based Sexual Abuse and EU Law: A Critical Analysis

Abstract In May 2024, the European Union adopted the Directive on violence against women and domestic violence, marking the first EU-wide binding legislation to address various forms of sexualized and gendered harm. This Article provides the first comprehensive analysis of the Directive’s provisions on image-based sexual abuse (“IBSA”), encompassing the non-consensual taking, creating, and sharing of intimate materials, as well as threats to distribute them. While acknowledging the aim to harmonize legislation at the Union level, the Article identifies a range of limitations and the failure to fully reflect the diverse experiences of victims. Additionally, the Article evaluates the complementary roles in combating IBSA of the Digital Services Act and the AI Act which impose obligations on online platforms, search engines, and AI developers. Overall, the current EU framework represents a promising but partial approach. If the EU is to comprehensively address IBSA and safeguard victims’ rights, implementation beyond the minimum will be required together with proactive, effective regulation.

Meta-Entrepreneurship

Metaverse entrepreneurship has emerged as an innovative topic alongside the development of generative AI, agentic AI and metaverse. This study conceptualizes meta-entrepreneurship as a novel form of entrepreneurial activity that enables value creation within virtual and physical realms and proposes an analytical theoretical framework based on a systematic literature review, observations, and focus group study. Our framework is structured around three layers (infrastructure, content, and experience) and two domains (metaverse-based operational domain and AI-based production domain), aims to conceptualize “what is meta-entrepreneurship” and identify new possibilities. The research highlights the multifaceted impact of meta-entrepreneurship on individuals, corporations, industries, societies, and countries. Further, we delineate three pathways to achieve meta-entrepreneurship, analyze nine critical challenges, and propose potential future research directions to contribute to the theoretical foundation of this emerging field.

Development of AI Based E-Commerce IOT platform For Shop Now Application

The Shop Now application is an e-commerce project designed and developed to facilitate the buying and selling of books over the internet. Electronic Commerce is the process of doing business through IOT computer networks. A person sitting on his chair in front of a computer can access all the facilities of the Internet to buy or sell the products. E-commerce has made it easier for humans to reduce physical work and save time. The Shop Now application provides customers with online shopping through a web browser. A customer can, create, sign in to his account, place items into a shopping cart and purchase it. The Administrator will have additional functionalities when compared the common user. He can add, delete and update the book details and also confirm a placed order. Key Words: IOT, E-Commerce, Buy, Sell, Shop

UltimateSynth: MRI Physics for Pan-Contrast AI.

Magnetic resonance imaging (MRI) is commonly used in healthcare for its ability to generate diverse tissue contrasts without ionizing radiation. However, this flexibility complicates downstream analysis, as computational tools are often tailored to specific MRI types and lack generalizability across the full spectrum of scans used in healthcare. Here, we introduce a versatile framework for the development and validation of pan-contrast AI models that can exhaustively cater to the full spectrum of scans achievable with MRI, enabling model deployment across scanner models, scan types, and age groups. Core to our framework is UltimateSynth, a technology that combines tissue physiology and MR physics in synthesizing realistic images across a comprehensive range of contrasts to bolster the AI development life cycle through efficient data labeling, generalizable model training, and thorough performance benchmarking. UltimateSynth is a platform for pan-contrast generalization of contrast-specific tools. We showcase the effectiveness of UltimateSynth by training an off-the-shelf U-Net to generalize anatomical segmentation across over 150,000 unique MRI contrasts, achieving robust tissue volumetric quantification with exceptionally low variability below 2%.

A Study on how to Secure Competitiveness through Case Analysis of Media and Contents using Generative AI

Due to the ChatGPT craze, all industries around the world have a hot Generative AI of interest. In particular, since Open AI announced AI Sora, which makes text into video on February 15, 2024, the media and content industries, both at home and abroad, have expressed expectations and a sense of crisis. By learning a large amount of Hyper-scale Data with artificial intelligence technology that actively generates results according to the specific needs of live Generative AI users, it is looking beyond the realm of creation, which can be called the human domain[1][2][3]. Although, unlike image-generating AI, the video live Generative AI service still has the limit of generating only short-length videos because it has to maintain temporal coherence. However, as live Generative AI developers supplement these problems and continue to offer advanced services, they will become the mainstream of live Generative AI in the media and content industries. Therefore, as of 2024, this study intends to present 3I(Inquiry-Inspection-Idea) as a strategy for competitive advantage in the media and content industries to respond to changes in the media and content industries that will be triggered by generative Generative AI along with ways to secure competitiveness through case analysis of generative Generative AI in the media and content industries[2].