Artificial Intelligence Projects Research Articles

Promoting Hospitals’ Reputation through Smart Branding Initiatives. A Quantitative Analysis of the Best Hospitals in the United States

Hospitals use different technological tools to implement corporate communication initiatives and, in this way, improve their relationships with stakeholders (employees, patients, media companies) and build a reputed brand. However, they face different barriers: limited budgets for corporate communication, strict legal frameworks, and stakeholders’ new needs regarding information and emotional support. This paper aims to analyze how the 100 best hospitals in the United States manage smart technologies to promote their brand. To do that, we conducted a literature review about smart hospitals, branding, and corporate communication; and then we defined 34 quantitative indicators to evaluate how the hospitals previously mentioned managed their websites, online newsrooms, about us sections, and artificial intelligence department web sites for reputation purposes. Our results proved that most hospitals respected indicators related to the homepage (8.67/11) but not those referring to online newsrooms (4.44/11) or about us sections (2.66/6). Besides, only 23 hospitals had implemented a department specialized in artificial intelligence that collaborated with external organizations. We concluded that most American hospitals focused their reputation efforts on patients rather than other targets (media companies, employees, suppliers, shareholders); and that these organizations did not integrate enough artificial intelligence projects into their smart branding initiatives.

Enhancing Gesture-Controlled Virtual Mouse and Virtual Keyboard Using AI Techniques

Artificial Intelligence has become an essential part of modern technology. Although computer technology is advanced, it can still be improved to make it more user-friendly. One way to do this is to replace touchscreen desktops with a virtual mouse and keyboard. This can reduce the need for gadgets and enhance human-computer interaction. During the COVID-19 pandemic, reducing human intervention and dependency on devices has been critical in controlling the spread of the virus. This is where a battery-powered or Bluetooth mouse, powered by virtual reality technology, can be helpful. The virtual mouse is created using OpenCV and virtual reality technology, with the proposed system utilizing advanced tools such as MediaPipe and Python. The MediaPipe library is particularly useful in artificial intelligence projects, as it enhances the efficiency of the model. The system is an AI-based mouse and keyboard that can be controlled using hand gestures. The user interacts with the system through the camera output displayed on the screen, while the webcam serves as an input device. Python and OpenCV tools are used for implementation, making it applicable in pandemic situations and smart teaching systems. The proposed system works on Enhancing gesture Controlled Virtual Mouse and Virtual Keyboard through Virtual Assistant using AI Techniques.

DIGITAL TRANSFORMATION POTENTIAL: THE ROLE OF ARTIFICIAL INTELLIGENCE IN BUSINESS

Purpose: The integration of Artificial Intelligence (AI) into business operations has become a pivotal driver of innovation and efficiency. This research paper explores the multifaceted landscape of AI implementation in businesses, examining the benefits of AI implementation.   Theoretical Framework: The methodology adopted for this research comprises Semi-structured interviews with six key stakeholders’ executives, AI project managers, and digital transformation staff. Six additional globally businesses were chosen for case studies based on AI adoption maturity and digital transformation performance. Additionally, a qualitative content study of AI and digital transformation literature was conducted.   Results and Discussion: The study revealed that as businesses continue to integrate AI, a balanced approach considering workforce implications is crucial to realizing the full potential of AI. The study also found out that the adoption of Artificial Intelligence in business is a nuanced process shaped by a confluence of factors. Organizational leadership, culture, resource availability, perceived benefits, regulatory considerations, data security, technology evaluation, and workforce readiness all play intricate roles. Thus, a holistic understanding of these factors empowers organizations to navigate the complex space of AI adoption, unlocking its transformative potential.   Research Implications: The study's findings have many practical implications to businesses adoption of AI. AI, managers must promote innovation and adaptability. AI implementation requires human and technology capital. To reduce AI risks, legal and data security measures must be followed. To prepare employees for an AI-enabled workplace, organizations should adopt comprehensive training and development programs. Businesses may overcome AI implementation challenges and capitalize on its disruptive potential by addressing these five pragmatic elements.   Originality and Value: This study contributes to the literature by offering insights into the nuanced landscape of AI adoption in businesses, emphasizing the crucial role of workforce considerations. It provides a comprehensive understanding of the multifaceted factors shaping AI implementation. The relevance and value of this research are evidenced by the holistic approach and contributes valuable guidance for organizations navigating the complexities of AI adoption, fostering innovation and efficiency while prioritizing workforce integration.

Dr. R.O. Bott Will See You Now: Exploring AI for Wellbeing with Middle School Students

Artificial Intelligence (AI) is permeating almost every area of society, reshaping how many people, including youth, navigate the world. Despite the increased presence of AI, most people lack a baseline knowledge of how AI works. Moreover, social barriers often hinder equal access to AI courses, perpetuating disparities in participation in the field. To address this, it is crucial to design AI curricula that are effective, inclusive, and relevant, especially to learners from backgrounds that are historically excluded from working in tech. In this paper, we present AI for Wellbeing, a curriculum where students explore conversational AI and the ethical considerations around using it to promote wellbeing. We specifically designed content, educator materials, and educational technologies to meet the interests and needs of students and educators from diverse backgrounds. We piloted AI for Wellbeing in a 5-day virtual workshop with middle school teachers and students. Then, using a mixed-methods approach, we analyzed students' work and teachers' feedback. Our results suggest that the curriculum content and design effectively engaged students, enabling them to implement meaningful AI projects for wellbeing. We hope that the design of this curriculum and insights from our evaluation will inspire future efforts to create culturally relevant K-12 AI curricula.

A multi-skilled staff scheduling and team configuration optimisation model for artificial intelligence project portfolio considering competence development and innovation-driven

This paper emphasises the pivotal role of enhancing research and development (R&D) staff competence and boosting team innovation efficiency in configuring R&D project teams for artificial intelligence (AI) product development. Diverging from traditional studies primarily focused on time, quality, and cost objectives, this study proposes prioritising the skill increments of staff and team diversity, aligning with the overarching goals of the R&D cycle. Targeting multi-skilled R&D personnel for scheduling and configuration, a three-objective tradeoff optimisation model is established. The nonlinear mixed-integer constrained programming model incorporates a learning effect for calculating employee skill value and employs a heterogeneity efficiency formula for assessing team diversity, particularly emphasising the diversity of R&D personnel research backgrounds. An algorithm based on Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is designed to obtain the approximate Pareto optimal solution. Furthermore, it compares the algorithm with the multi-objective particle swarm optimisation (MOPSO), explores practical decision-making methods for Pareto solutions, and conducts sensitivity analyses on the learning rate and diversity level. Our research is relevant to enterprises seeking to enhance R&D capabilities with a certain degree of homogeneity among R&D employees. This paper exemplifies and validates the proposed model and solution approach using a new AI product from a healthcare company.

Artificial Intelligence (AI) in Brazilian Digital Journalism: Historical Context and Innovative Processes

This article investigates the historical uses and types of artificial intelligence (AI) systems and resources in Brazilian journalistic products. It is a work anchored in critically analyzing the literature on the subject, mapping and observing cases, seeking to identify uses and innovative processes, and analyzing AI projects for journalism. A search was carried out in web repositories, specifically Google, Google Scholar, and Scopus, using the terms: “inteligência artificial” + “jornalismo”, “bot + jornalismo”, “Geração de linguagem natural [NLG] + jornalismo”, “aprendizado de máquina [machine learning] + jornalismo”, and “algoritmos + jornalismo”. The corpus analysis (N = 45) includes the evaluation of the impacts of AI on the production and distribution of news in the context of Brazilian digital journalism. We try to answer questions about the uses of databases, approximation with platforms, uses of shared codes, connections with other Ais, and sources of funding, and whether they are backend or frontend initiatives. In a parallel investigation, we try to identify if Brazilian newsrooms are officially using ChatGPT, a generative AI. The findings point to advances in using low-cost and low-impact AI, with the predominance of bots. The great availability of this kind of AI in web repositories is believed to facilitate native digital media to incorporate innovative processes in using these technologies.

Investigation of artificial intelligence in SMEs: a systematic review of the state of the art and the main implementation challenges

AbstractWhile the topic of artificial intelligence (AI) in multinational enterprises has been receiving attention for some time, small and medium enterprises (SMEs) have recently begun to recognize the potential of this new technology. However, the focus of previous research and AI applications has therefore mostly been on large enterprises. This poses a particular issue, as the vastly different starting conditions of various company sizes, such as data availability, play a central role in the context of AI. For this reason, our systematic literature review, based on the PRISMA protocol, consolidates the state of the art of AI with an explicit focus on SMEs and highlights the perceived challenges regarding implementation in this company size. This allowed us to identify various business activities that have been scarcely considered. Simultaneously, it led to the discovery of a total of 27 different challenges perceived by SMEs in the adoption of AI. This enables SMEs to apply the identified challenges to their own AI projects in advance, preventing the oversight of any potential obstacles or risks. The lack of knowledge, costs, and inadequate infrastructure are perceived as the most common barriers to implementation, addressing social, economic, and technological aspects in particular. This illustrates the need for a wide range of support for SMEs regarding an AI introduction, which covers various subject areas, like funding and advice, and differentiates between company sizes.

Choosing the right path for AI integration in engineering companies: A strategic guide

The Engineering, Procurement and Construction (EPC) businesses operating within the energy sector are recognizing the increasing importance of Artificial Intelligence (AI). Many EPC companies and their clients have realized the benefits of applying AI to their businesses in order to reduce manual work, drive productivity, and streamline future operations of engineered installations in a highly competitive industry. The current AI market offers various solutions and services to support this industry, but organizations must understand how to acquire AI technology in the most beneficial way based on their business strategy and available resources. This paper presents a framework for EPC companies in their transformation towards AI. Our work is based on examples of project execution of AI-based products development at one of the biggest EPC contractors worldwide and on insights from EPC vendor companies already integrating AI into their engineering solutions. The paper covers the entire life cycle of building AI solutions, from initial business understanding to deployment and further evolution. The framework identifies how various factors influence the choice of approach toward AI project development within large international engineering corporations. By presenting a practical guide for optimal approach selection, this paper contributes to the research in AI project management and organizational strategies for integrating AI technology into businesses. The framework might also help engineering companies choose the optimum AI approach to create business value.

A novel physically interpretable end-to-end network for stress monitoring in laser shock peening

The data-driven method based on acoustic emission signals is gradually becoming a hot topic in the field of laser shock peening quality monitoring. Although some existing deep learning methods do provide excellent monitoring accuracy and speed, they lack physical interpretability in nature, and the opacity of these decisions poses a great challenge to their credibility. The weak interpretability of deep learning models has become the biggest obstacle to the landing of artificial intelligence projects. To overcome this drawback, this paper proposes a monitoring strategy that can achieve physical interpretability in feature extraction, selection and classification, namely, jointly generating monitoring results and explanations. Specifically, it is an end-to-end model that combines convolutional neural units, gated recurrent units, and attention mechanisms. Firstly, a wavelet analysis with physical meaning that can be autonomously learned is performed on the acoustic emission. Then, the contribution of features is distinguished based on the correlation of information in different frequency bands, and redundant and noisy features are removed. Finally, the interpretability evaluation of processing quality is realized by using gated recurrent units with attention mechanisms. The effectiveness and reliability of the proposed method are confirmed by the experimental data of both laser shock peening at small and large gradient energies compared to state-of-the-art feature methods, CNN- and LSTM-based models. Most importantly, the physical interpretation of acoustic emission signals during the processing can increase the credibility of decisions and provide a basic logic for on-site judgments by professionals.

Discovery Viewer (DV): Web-Based Medical AI Model Development Platform and Deployment Hub.

The rapid rise of artificial intelligence (AI) in medicine in the last few years highlights the importance of developing bigger and better systems for data and model sharing. However, the presence of Protected Health Information (PHI) in medical data poses a challenge when it comes to sharing. One potential solution to mitigate the risk of PHI breaches is to exclusively share pre-trained models developed using private datasets. Despite the availability of these pre-trained networks, there remains a need for an adaptable environment to test and fine-tune specific models tailored for clinical tasks. This environment should be open for peer testing, feedback, and continuous model refinement, allowing dynamic model updates that are especially important in the medical field, where diseases and scanning techniques evolve rapidly. In this context, the Discovery Viewer (DV) platform was developed in-house at the Biomedical Engineering and Imaging Institute at Mount Sinai (BMEII) to facilitate the creation and distribution of cutting-edge medical AI models that remain accessible after their development. The all-in-one platform offers a unique environment for non-AI experts to learn, develop, and share their own deep learning (DL) concepts. This paper presents various use cases of the platform, with its primary goal being to demonstrate how DV holds the potential to empower individuals without expertise in AI to create high-performing DL models. We tasked three non-AI experts to develop different musculoskeletal AI projects that encompassed segmentation, regression, and classification tasks. In each project, 80% of the samples were provided with a subset of these samples annotated to aid the volunteers in understanding the expected annotation task. Subsequently, they were responsible for annotating the remaining samples and training their models through the platform's "Training Module". The resulting models were then tested on the separate 20% hold-off dataset to assess their performance. The classification model achieved an accuracy of 0.94, a sensitivity of 0.92, and a specificity of 1. The regression model yielded a mean absolute error of 14.27 pixels. And the segmentation model attained a Dice Score of 0.93, with a sensitivity of 0.9 and a specificity of 0.99. This initiative seeks to broaden the community of medical AI model developers and democratize the access of this technology to all stakeholders. The ultimate goal is to facilitate the transition of medical AI models from research to clinical settings.

Failure factors of AI projects: results from expert interviews

In the last few years, business firms have substantially invested into the artificial intelligence (AI) technology. However, according to several studies, a significant percentage of AI projects fail or do not deliver business value. Due to the specific characteristics of AI projects, the existing body of knowledge about success and failure of information systems (IS) projects in general may not be transferrable to the context of AI. Therefore, the objective of our research has been to identify factors that can lead to AI project failure. Based on interviews with AI experts, this article identifies and discusses 12 factors that can lead to project failure. The factors can be further classified into five categories: unrealistic expectations, use case related issues, organizational constraints, lack of key resources, and technological issues. This research contributes to knowledge by providing new empirical data and synthesizing the results with related findings from prior studies. Our results have important managerial implications for firms that aim to adopt AI by helping the organizations to anticipate and actively manage risks in order to increase the chances of project success.

Design Thinking and AI: A New Frontier for Designing Human‐Centered AI Solutions

AbstractIn recent years, design thinking (DT) has become a pervasive innovation approach in the managerial community as a structured way of solving challenging problems through a human‐centered perspective. The approach fosters lateral thinking and has a huge impact on the organizational culture and the core elements of the innovation process. At the same time, the number of organizations trying to solve problems through the application of artificial intelligence (AI) is constantly growing, although more than 80% of AI projects never reach deployment due to the wrong strategic approach, poor data quality, or a lack of AI awareness among employees—and those that do remain below profitability expectations. The aim of this paper is therefore to connect the AI and DT communities by proposing a first framework supporting the development of AI solutions in a human‐centered way. The contribution of this paper is thus twofold: First, we investigate ways in which design thinking adds real value along the design process of intelligent solutions, thereby enriching the present body of design management literature. Second, we propose a first framework by linking core elements of both worlds to support strategic designers and data engineers in designing such applications. Finally, we propose a research agenda that can serve as a basis for future research directions in order to advance contributions at the intersection of the two fields.

Financing The Development of Artificial Intelligence Maid: An Analysis Of Pertinent Fiqhi Issues

Artificial Intelligence (AI) is the latest machine technology that is replacing the effort of humans and is the fastest-growing sector across the globe. The underlying ethos of AI is to create a secure ecosystem for every human endeavour. Investors are claiming that the invention of an AI assistant such as a robot maid is the future reality. The AI robot is unstoppable and will obstruct how things are done at home. The development of AI and the demand for it indicates a future challenge to domestic activities. Since technology is replacing human efforts, scholars and researchers are concerned about the permissibility of Islamic banks to finance AI projects for the development of robot-maid. A robot maid is likely to exploit gender issues simply because consumers might prefer an AI maid that is male or female. Social violation in the use of AI maid will likely trigger further queries. This study uses the doctrinal approach of the qualitative method to explore Islamic thresholds and analyzes the juristic consideration for financing the development of AI for human development. The study explains the understanding of AI, its contributions to the world economy, and some pertinent issues and challenges. This study found that the growth of AI interventions in homes and care services is a reality, but financing AI maids requires adherence to Islamic requirements, including Shariah compliance and consideration of ethical, economic, security, and social concerns.

Method of creation of FPGA based implementation of artificial intelligence as a service

The subject of study in this article is the technologies of Field Programmable Gate Array (FPGA), methods, and tools for prototyping of hardware accelerators of Artificial Intelligence (AI) and providing it as a service. The goal is to reduce the efforts of creation and modification of FPGA implementation of Artificial Intelligent projects and provide such solutions as a service. Task: to analyze the possibilities of heterogeneous computing for the implementation of AI projects; analyze advanced FPGA technologies and accelerator cards that allow the organization of a service; analyze the languages, frameworks, and integrated environments for the creation of Artificial Intelligence projects for FPGA implementation; propose a technique for modifiable FPGA project prototyping to ensure a long period of compatibility with integrated environments and target devices; propose a technique for the prototyping of FPGA services with high performance to improve the efficiency of FPGA based AI projects; propose a sequence of optimization of neural networks for FPGA implementation; and provide an example of the practical implementation of the research results. According to the tasks, the following results were obtained. Analysis of the biggest companies and vendors of FPGA technology is performed. Existing heterogeneous technologies and potential non-electronic mediums for AI computations are discussed. FPGA accelerator cards with a large amount of High Bandwidth Memory (HBM) on the same chip package for implementation of AI projects are analyzed and compared. Languages, frameworks, and technologies as well as the capabilities of libraries and integrated environments for prototyping of FPGA projects for the AI applications are analyzed in detail. The sequence of prototyping of FPGA projects that are stable to changes in the environment is proposed. The sequence of prototyping of highly efficient pipelined projects for data processing is proposed. The steps of optimization of neural networks for FPGA implementation of AI applications are provided. An example of practical use of the results of research, including the use of sequences is provided. Conclusions. One of the main contributions of this research is the proposed method of creation of FPGA based implementation of AI projects in the form of services. Proposed sequence of neural network optimization for FPGA allows the reduction of the complexity of the initial program model by more than five times for hardware implementation depending on the required accuracy. The described solutions allow the construction of completely scalable and modifiable FPGA implementations of AI projects to provide it as a service.

Organizational Implementation of AI: Craft and Mechanical Work

Recent years have brought major technological breakthroughs in artificial intelligence (AI), and firms are expected to invest nearly $98 B in 2023. However, many AI projects never leave the pilot phase, and many companies have difficulties extracting value from their AI initiatives. To explain this contradiction, this article reports on a study of 55 projects implementing AI in organizations. It shows that organizational challenges in implementing AI projects are a result of a paradoxical tension created by two different perspectives on data science work: craft and mechanical work. Executives, managers, and data scientists should actively manage this tension to enable and sustain value creation through AI.

How (not to) Run an AI Project in Investigative Journalism

ABSTRACT Data journalists are increasingly reliant on automation and artificial intelligence (AI) to process and analyse massive datasets. AI can contribute to journalism by creating visualizations, verifying accuracy of information, analysing historical data, monitoring social media, finding patterns and outliers, generating text and much more. However, the integration of AI into the newsroom comes with its own challenges. In this article, we take a practice-based approach to develop a deeper understanding of how to overcome such challenges. Our teams of data scientists, AI experts and journalists took on four projects incorporating data science and machine learning into investigative journalism. From those experiences, we found that access to data at scale, data quality and reworking the concept of “newsworthy” as a machine learning question were the most significant obstacles to deploying AI in the newsroom. We recommend closer collaborations between team members of different disciplines to create a truly trans-disciplinary approach, as well as some practical considerations for choosing projects to facilitate successful AI-assisted investigations.

Artificial Intelligence Ethics and Challenges in Healthcare Applications: A Comprehensive Review in the Context of the European GDPR Mandate

This study examines the ethical issues surrounding the use of Artificial Intelligence (AI) in healthcare, specifically nursing, under the European General Data Protection Regulation (GDPR). The analysis delves into how GDPR applies to healthcare AI projects, encompassing data collection and decision-making stages, to reveal the ethical implications at each step. A comprehensive review of the literature categorizes research investigations into three main categories: Ethical Considerations in AI; Practical Challenges and Solutions in AI Integration; and Legal and Policy Implications in AI. The analysis uncovers a significant research deficit in this field, with a particular focus on data owner rights and AI ethics within GDPR compliance. To address this gap, the study proposes new case studies that emphasize the importance of comprehending data owner rights and establishing ethical norms for AI use in medical applications, especially in nursing. This review makes a valuable contribution to the AI ethics debate and assists nursing and healthcare professionals in developing ethical AI practices. The insights provided help stakeholders navigate the intricate terrain of data protection, ethical considerations, and regulatory compliance in AI-driven healthcare. Lastly, the study introduces a case study of a real AI health-tech project named SENSOMATT, spotlighting GDPR and privacy issues.

Artificial intelligence: how leading companies define use cases, scale-up utilization, and realize value

Artificial intelligence (AI) is one of the most transformative information technologies of our century. Despite high expectations across all industries and corporate functions, companies have difficulties to realize the full potential of AI and struggle to move beyond AI pilots. This article provides empirical evidence from survey data and three case studies to better understand the value drivers of AI. Leading companies specify use cases from a business perspective and leverage domain expertise to tap into value pools by combining technology with human capabilities. They provide fundamental enablers for AI like digital and data platforms, a data-centric organization with solid governance, and a dedicated human resource base to facilitate AI value creation. Moreover, these firms assess the financial and business impact of AI initiatives and reprioritize projects if the expected value does not materialize. Based on these insights, recommendations to better scale and derive value from AI projects will be offered.

Interview with Chat GPT to Define Architectural Design Studio Work: Possibilities, Conflicts and Limits

Design studio forms the backbone of architectural education. It is the first-place students encounter complex design problems that they should understand the context, search for the reasons, structure the demands, and develop creative solutions in the design process. The continuity and integrity of design studios are important, so the instructors have great effort to define and explore the project subjects and themes for each grade to make the environment more productive, creative, and collaborative. The meetings, web and literature surveys, and thoughts and experience of instructors enable the definition of an architectural design studio work. This research is to seek another knowledge source that could have possible contributions to this process. Chat CPT is a newly released artificial intelligence project that people can experience for months. It is a language model the training and learning continue. By the objective of the research, an interview with chat GPT is structured and executed to define and explore possible issues and valuable knowledge for third-year architectural design studio work as a case study. The recordings are presented, and an evaluation survey, Delphi technique among experts, is held to emphasize artificial intelligence's possibilities, conflict, and limits in taking the stakeholder role. Prominent findings are that AI continuously responds and presents important issues for requirements and concepts. It has limits on reaching the maps and conflicts in distinguishing the countries and institutions.

Realising the promises of artificial intelligence in manufacturing by enhancing CRISP-DM

To support manufacturing firms in realising the value of Artificial Intelligence (AI), we embarked on a six-year process of research and practice to enhance the popular and widely used CRISP-DM methodology. We extend CRISP-DM into a continuous, active, and iterative life-cycle of AI solutions by adding the phase of ‘Operation and Maintenance’ as well as embedding a task-based framework for linking tasks to skills. Our key findings relate to the difficult trade-offs and hidden costs of operating and maintaining AI solutions and managing AI drift, as well as ensuring the presence of domain, data science, and data engineering competence throughout the CRISP-DM phases. Further, we show how data engineering is an essential but often neglected part of the AI workflow, provide novel insights into the trajectory of involvement of the three competences, and illustrate how the enhanced CRISP-DM methodology can be used as a management tool in AI projects.