Deployment Of Artificial Intelligence Research Articles

Rewards, risks and responsible deployment of artificial intelligence in water systems

Artificial intelligence (AI) is increasingly proposed to address deficiencies across water systems, which currently leave about 25% of the global population without clean water, about 50% without sanitation services and about 30% without hygiene facilities. AI is poised to enhance supply insights, catchment management and emergency response, improve treatment plant and distribution network design, operation and maintenance, and advance service availability, demand management and water justice. However, proliferation of this nascent technology could trigger serious and unexpected problems, including system-wide compromise owing to design errors, malfunction and cyberattacks as well as exposures to cascading socio-ecological, water–energy–food nexus and coupled critical infrastructure failures. In response, we make three recommendations for safe and responsible deployment of AI across potable water supply and sewage disposal systems: address gaps in foundational infrastructure and digital literacy; establish institutional, software and hardware mechanisms for trustworthy AI; and prioritize applications based on our proposed systematic benefit and risk assessment framework. In this Perspective, the authors survey potential water-system-wide benefits of AI applications from catchments to end-users and then highlight potential systemic barriers, direct risks and exposures to cascading failures, which may prove catastrophic for communities. In response, they make three recommendations for safe and responsible deployment of AI across potable water supply and sewage disposal systems.

Artificial intelligence (AI) competencies for organizational performance: A B2B marketing capabilities perspective

The deployment of Artificial Intelligence (AI) has been accelerating in several fields over the past few years, with much focus placed on its potential in Business-to-Business (B2B) marketing. Early reports highlight promising benefits of AI in B2B marketing such as offering important insights into customer behaviors, identifying critical market insight, and streamlining operational inefficiencies. Nevertheless, there is a lack of understanding concerning how organizations should structure their AI competencies for B2B marketing, and how these ultimately influence organizational performance. Drawing on AI competencies and B2B marketing literature, this study develops a conceptual research model that explores the effect that AI competencies have on B2B marketing capabilities, and in turn on organizational performance. The proposed research model is tested using 155 survey responses from European companies and analyzed using partial least squares structural equation modeling. The results highlight the mechanisms through which AI competencies influence B2B marketing capabilities, as well as how the later impact organizational performance.

Artificial Intelligence Accelerated Transformation in The Healthcare Industry

The healthcare industry was a pioneer in the deployment of artificial intelligence (AI) technology. Due to the nature of the services and the vulnerability of a sizable portion of end users, there has been a significant amount of research and discussion on the concept of artificial intelligence. A mixed-method approach has been used to pinpoint the components of moral AI in the healthcare sector and look into how it affects value creation and market performance. Since AI technology is still developing in India, analysis is conducted in an Indian context. The understanding of how various AI components supported healthcare organisations and deliver better patient-centered care and evidence-based medicine was aided by these in-depth studies and analyses of the patient perspective.

Artificial intelligence in medicine - dermatology compared to other medical specialties in FDA-cleared software as medical device.

Artificial intelligence (AI) and machine learning (ML) have occupied the center stage in healthcare as research groups and institutions investigate their capabilities and risks. Dermatology is often cited as one of the medical specialties most ripe for disruption with AI technology due to the heavy incorporation of visual information into clinical decisions and treatments. Although the literature on AI in dermatology is rapidly growing, there has been a noticeable absence of mature AI solutions utilized by dermatology departments or patients. This commentary provides insight into the regulatory challenges facing AI solutions for the specialty of dermatology and the unique considerations that should be factored into AI development and deployment.

Artificial Intelligence in Clinical Oncology: From Data to Digital Pathology and Treatment.

Recently, a wide spectrum of artificial intelligence (AI)-based applications in the broader categories of digital pathology, biomarker development, and treatment have been explored. In the domain of digital pathology, these have included novel analytical strategies for realizing new information derived from standard histology to guide treatment selection and biomarker development to predict treatment selection and response. In therapeutics, these have included AI-driven drug target discovery, drug design and repurposing, combination regimen optimization, modulated dosing, and beyond. Given the continued advances that are emerging, it is important to develop workflows that seamlessly combine the various segments of AI innovation to comprehensively augment the diagnostic and interventional arsenal of the clinical oncology community. To overcome challenges that remain with regard to the ideation, validation, and deployment of AI in clinical oncology, recommendations toward bringing this workflow to fruition are also provided from clinical, engineering, implementation, and health care economics considerations. Ultimately, this work proposes frameworks that can potentially integrate these domains toward the sustainable adoption of practice-changing AI by the clinical oncology community to drive improved patient outcomes.

The Revolutionary Computer Technology Artificial Intelligence that is Changing Our World & its Impact on Different Jobs

Abstract: This research paper provides an in-depth analysis of the impact of Artificial Intelligence (AI) on various job sectors. It explores the history of AI and highlights the top AI tools that are being used today. The primary focus of the research is on the effects of AI on different job sectors, including healthcare, finance, and transportation. Additionally, the paper examines the potential benefits and challenges of AI adoption, including the concerns about job displacement and the future of work. Ultimately, the research concludes that a responsible approach to AI adoption is necessary, with the need for ethical frameworks and investment in research and development to maximise the benefits while mitigating the risks.Artificial Intelligence (AI) is one of the most revolutionary computer technologies that is changing our world. With AI-powered tools, organisations can automate routine and repetitive tasks, enabling workers to focus on more complex and higher-value tasks. Moreover, AI is transforming the nature of work, enabling more flexible and remote work arrangements. While AI has the potential to drive innovation and enhance efficiency, there are also concerns about the impact of AI on different job sectors, including job displacement, the future of work, and ethical concerns.The research paper examines the impact of AI on different job sectors, including healthcare, finance, and transportation. In healthcare, AI-powered tools are being used to improve patient outcomes and enhance the efficiency of medical procedures. In finance, AI is revolutionizing the way financial institutions analyse data and make investment decisions. In transportation, AI-powered autonomous vehicles are transforming the way we travel and move goodsThe paper explores the potential benefits and challenges of AI adoption. While AI has the potential to enhance efficiency and innovation, there are concerns about job displacement, particularly in sectors where tasks are highly repetitive and routine. In such cases, investment in reskilling and up skilling programs is essential to prepare workers for the changing job market. Moreover, the paper highlights the importance of developing ethical frameworks for AI development and deployment to address concerns about bias, transparency, and accountability.

Intelligence artificielle et application non contentieuse du droit : une réactualisation du problème du design institutionnel de Lon L. Fuller

Cet article propose de rapprocher les enjeux normatifs engendrés par l’intelligence artificielle dans la sphère du juridique à la problématique plus fondamentale du « design institutionnel » de Lon L. Fuller. L’optimisation des ressources étatiques promise par le déploiement de l’intelligence artificielle s’opérera en contrepartie d’une application « non contentieuse » du droit plus préventive et unilatérale, autrement dit managériale. En scindant le concept de la gouvernance étatique en ses ramifications « juridique » et « économique », Fuller entendait conjuguer celle-ci et celle-là dans un projet aspirant à la cohésion et au bon fonctionnement sociétal. Une réactualisation des écrits « fullériens » permettra de situer la portée de l’intelligence artificielle au sein de cette classification et offrira un éclairage favorable à l’élucidation des enjeux normatifs soulevés par l’intelligence artificielle étatique.

Toward fairness in artificial intelligence for medical image analysis: identification and mitigation of potential biases in the roadmap from data collection to model deployment.

To recognize and address various sources of bias essential for algorithmic fairness and trustworthiness and to contribute to a just and equitable deployment of AI in medical imaging, there is an increasing interest in developing medical imaging-based machine learning methods, also known as medical imaging artificial intelligence (AI), for the detection, diagnosis, prognosis, and risk assessment of disease with the goal of clinical implementation. These tools are intended to help improve traditional human decision-making in medical imaging. However, biases introduced in the steps toward clinical deployment may impede their intended function, potentially exacerbating inequities. Specifically, medical imaging AI can propagate or amplify biases introduced in the many steps from model inception to deployment, resulting in a systematic difference in the treatment of different groups. Our multi-institutional team included medical physicists, medical imaging artificial intelligence/machine learning (AI/ML) researchers, experts in AI/ML bias, statisticians, physicians, and scientists from regulatory bodies. We identified sources of bias in AI/ML, mitigation strategies for these biases, and developed recommendations for best practices in medical imaging AI/ML development. Five main steps along the roadmap of medical imaging AI/ML were identified: (1)data collection, (2)data preparation and annotation, (3)model development, (4)model evaluation, and (5)model deployment. Within these steps, or bias categories, we identified 29 sources of potential bias, many of which can impact multiple steps, as well as mitigation strategies. Our findings provide a valuable resource to researchers, clinicians, and the public at large.

Characterizing browser-based medical imaging AI with serverless edge computing: towards addressing clinical data security constraints.

Artificial intelligence (AI) has been widely introduced to various medical imaging applications ranging from disease visualization to medical decision support. However, data privacy has become an essential concern in clinical practice of deploying the deep learning algorithms through cloud computing. The sensitivity of patient health information (PHI) commonly limits network transfer, installation of bespoke desktop software, and access to computing resources. Serverless edge-computing shed light on privacy preserved model distribution maintaining both high flexibility (as cloud computing) and security (as local deployment). In this paper, we propose a browser-based, cross-platform, and privacy preserved medical imaging AI deployment system working on consumer-level hardware via serverless edge-computing. Briefly we implement this system by deploying a 3D medical image segmentation model for computed tomography (CT) based lung cancer screening. We further curate tradeoffs in model complexity and data size by characterizing the speed, memory usage, and limitations across various operating systems and browsers. Our implementation achieves a deployment with (1) a 3D convolutional neural network (CNN) on CT volumes (256×256×256 resolution), (2) an average runtime of 80 seconds across Firefox v.102.0.1/Chrome v.103.0.5060.114/Microsoft Edge v.103.0.1264.44 and 210 seconds on Safari v.14.1.1, and (3) an average memory usage of 1.5 GB on Microsoft Windows laptops, Linux workstation, and Apple Mac laptops. In conclusion, this work presents a privacy-preserved solution for medical imaging AI applications that minimizes the risk of PHI exposure. We characterize the tools, architectures, and parameters of our framework to facilitate the translation of modern deep learning methods into routine clinical care.

Recent Advances in Artificial Intelligence Sensors

AbstractSignificant growth in the development and deployment of artificial intelligence (AI) is being witnessed. Driven by the great versatility of emerging computer science and material science, various AI sensors provide cost‐effective approaches for a wide range of monitoring applications toward the realization of smart homes and personal healthcare. Advanced AI sensors have multiple sensors capable of detecting multidimensional information and human‐brain‐like computation device for data processing. Herein, this review outlines the recent advances in the development of AI sensors. This review first introduces the materials, fabrication methods, and algorithms of current AI sensors and their applications, i.e., complementary metal oxide semiconductor image sensors for computer vision, microelectromechanical systems, microphone sensors for voice recognition, and wearable sensors for gesture recognition. Then, the recent advances in AI wearables sensors and self‐powered sensor systems are highlighted. Next, the current developments of neuromorphic computing systems, multimodality, and digital twins are reviewed. Last, a perspective on future directions for further research development is also provided. In summary, the trend of advanced AI sensors is the complementary between edge computing and cloud computing, which will show great potential in the applications of smart buildings, individual healthcare, the Internet of things, etc.

Hospitality education in Asia: is it sufficiently equipped to prepare future hospitality leaders?

PurposeThe hospitality industry is rapidly evolving; aspects like technology, digitisation, artificial intelligence, eco-friendly, best practices, green hotels and sustainability are significantly impacting change. It is evident that the hospitality industry is trying its best to embrace change but do hospitality graduates have sufficient exposure to the importance of embracing these changes? Further, are academics updating their curriculum in line with industry requirements? This also prompts the question: How often do industry and academia come together to discuss the future? Is it the sole responsibility of academic institutions to consider the changes needed or should industry take a lead and guide academia? This theme issue explores these questions and concerns and considers the way forward.Design/methodology/approachThis viewpoint article explains why the theme issue question is important and outlines the approach taken with reference to the hospitality industry and hospitality education. The authors explore our theme issue question with particular reference to developments in Asia.FindingsThe hospitality industry impacts the environment in numerous ways and it is important for the industry to learn and train employees for a sustainable future and it is equally important to understand the challenges faced by the industry in adopting sustainable practices. The deployment of artificial intelligence, robotics and automation is increasingly impacting global standards and operating procedures and clearly technology has a key role to play in sustainable development. The authors also consider ways in which hospitality education could be better synced with industry and how it could evolve in response to future requirements.Originality/valueThis theme issue on hospitality education and sustainability was conceived with the aim of addressing concerns like the challenges faced by academics in upgrading the curriculum/programme structure so that it is better aligned with industry norms. It further examines sustainable practices adopted by hotels in Asia as compared to their counterparts in other parts of the world and it explores the gaps between hospitality industry operations and the programme delivery of hospitality institutions.

Automation and AI-Enabled Customer Journey: A Bibliometric Analysis

This article aims to scrutinize the dynamics of scientific publications devoted to optimizing the customer journey in the retail sector through the application of artificial intelligence (AI) and determines the status of existing research on the role of AI in the retail sector. The research objective was to analyse the pool of scientific publications from the Scopus database to analyse how emerging technologies have improved customer journey (pre-purchase, purchase, post-purchase). The research employed a publication search by article title, abstract and keywords to collect the data set from the Scopus database. The selected publications were visualized using the software VOSviewer 1.6.16.0, and Biblioshiny to perform bibliometric analyses. An initial sample of 356 research articles was collected. After reading the abstract and full article, a final corpus of 149 scientific publications over two decades (1989–2021) was analysed in the study. Our findings suggest the prominence of authors and journal publishing in implementing AI in retail to automate customer journey. The results highlight how the application of AI fosters in improving the different phases of customer journey among the retailing sectors. Banking and fashion are the retailing sectors that have major applications of AI in the customer journey. Further studies should be conducted to gain deeper insights into the emerging use of AI in other sectors of the economy. It was also found from the intensive literature review that in the purchasing and decision-making phase of the customer journey, the deployment of AI is the most. This study analysed the incorporation of AI into the customer journey to highlight its impact on retailing. This study contributes to the literature by adding the lenses of AI in key management areas in retailing.

Saudi Arabia Corporate Firms are Hesitant to Embrace Artificial Intelligence as of 2020 Despite the Numerous Benefits

The modern world has experienced numerous advancements, particularly in the Artificial Intelligence (AI) sector which is slowly shaping the mode of business in Saudi Arabia. This research paper focuses on why there has been hesitation in the establishment of AI in Saudi Arabia firms despite the numerous potential advantages of AI. Saudi Arabia has, in recent times, undergone diversification in its economy. Its AI is experienced in the sectors such as robotics, industry, finance, and banking, especially in forecasting and improvement of business processes alongside provisions of solutions to complicated functionalities. However, AI is halfway implemented in the country due to various constraints explored in this publication. This research article examines companies hesitant to invest in AI and elaborates on its deployment in companies in Saudi Arabia to utilize such technologies in entire sectors. Moreover, our research also analyzes the total investment associated with AI to realize the desired change in the organizational operating activities. Our primary research involved over 2000 senior executives regarding the adoption of AI, the firm’s prospects for deployment, and its effects on the markets, governments, and persons. The results of this study showcase that the new AI generation is primarily based on the digitalization platform. Lastly, it is noted that AI deployment and acquisitions require a large number of funds hence forcing struggling firms to evade its adoption.

Artificial Intelligence for Diabetic Retinopathy Screening Using Color Retinal Photographs: From Development to Deployment.

Diabetic retinopathy (DR), a leading cause of preventable blindness, is expected to remain a growing health burden worldwide. Screening to detect early sight-threatening lesions of DR can reduce the burden of vision loss; nevertheless, the process requires intensive manual labor and extensive resources to accommodate the increasing number of patients with diabetes. Artificial intelligence (AI) has been shown to be an effective tool which can potentially lower the burden of screening DR and vision loss. In this article, we review the use of AI for DR screening on color retinal photographs in different phases of application, ranging from development to deployment. Early studies of machine learning (ML)-based algorithms using feature extraction to detect DR achieved a high sensitivity but relatively lower specificity. Robust sensitivity and specificity were achieved with the application of deep learning (DL), although ML is still used in some tasks. Public datasets were utilized in retrospective validations of the developmental phases in most algorithms, which require a large number of photographs. Large prospective clinical validation studies led to the approval of DL for autonomous screening of DR although the semi-autonomous approach may be preferable in some real-world settings. There have been few reports on real-world implementations of DL for DR screening. It is possible that AI may improve some real-world indicators for eye care in DR, such as increased screening uptake and referral adherence, but this has not been proven. The challenges in deployment may include workflow issues, such as mydriasis to lower ungradable cases; technical issues, such as integration into electronic health record systems and integration into existing camera systems; ethical issues, such as data privacy and security; acceptance of personnel and patients; and health-economic issues, such as the need to conduct health economic evaluations of using AI in the context of the country. The deployment of AI for DR screening should follow the governance model for AI in healthcare which outlines four main components: fairness, transparency, trustworthiness, and accountability.

Recent Advancements of Artificial Intelligence in Particle Therapy

We are in a golden age of progress in the field of artificial intelligence (AI). Radiotherapy is perfectly suited to benefit from AI to enhance accuracy and efficiency due to its technology-intensive nature and direct human–machine interactions. While large amount of AI research have recently been published in the field of photon therapy, applications of AI specifically targeted for particle therapy remain scarcely investigated. There are two distinct differences between the photon therapy and particle therapy: 1) beam interaction physics (photons versus charged particles) and 2) beam delivery mode (e.g., IMRT/VMAT versus pencil beam scanning). Consequently, different strategies of AI deployment are required for these two radiotherapy modalities. In this article, we aim to present a comprehensive survey of recent literature exclusively focusing on AI-powered particle therapy. Six major aspects are included: 1) treatment planning; 2) dose calculation; 3) range and dose verification; 4) image guidance; 5) quality assurance; and 6) adaptive replanning. A number of perspectives, as well as potential challenges and common pitfalls, are also discussed.

Evaluation of sisal fiber and aluminum waste concrete blend for sustainable construction using adaptive neuro-fuzzy inference system

This research study presents evaluation of aluminum waste-sisal fiber concrete’s mechanical properties using adaptive neuro-fuzzy inference system (ANFIS) to achieve sustainable and eco-efficient engineering works. The deployment of artificial intelligence (AI) tools enables the optimization of building materials combined with admixtures to create durable engineering designs and eliminate the drawbacks encountered in trial-and-error or empirical method. The features of the cement-AW blend's setting time were evaluated in the laboratory and the results revealed that 0–50% of aluminum-waste (AW) inclusion increased both the initial and final setting time from 51–165 min and 585–795 min respectively. The blended concrete mix's flexural strength tests also show that 10% sisal-fiber (SF) substitution results in a maximum flexural strength of 11.6N/mm2, while 50% replacement results in a minimum flexural strength of 4.11N/mm2. Moreover, compressive strength test results show that SF and AW replacements of 0.08% and 0.1%, respectively, resulted in peak outcome of 24.97N/mm2, while replacements of 0.5% and 0.45% resulted in a minimum response of 17.02N/mm2. The ANFIS-model was developed using 91 datasets obtained from the experimental findings on varying replacements of cement and fine-aggregates with AW and SF respectively ranging from 0 to 50%. The ANFIS computation toolbox in MATLAB software was adopted for the model simulation, testing, training and validation of the response function using hybrid method of optimization and grid partition method of FIS at 100 Epochs. The compressive strength behavior is the target response, and the mixture variations of cement-AW and fine aggregates-SF combinations were used as the independent variables. The ANFIS-model performance assessment results obtained using loss function criteria demonstrates MAE of 0.1318, RMSE of 0.412, and coefficient of determination value of 99.57% which indicates a good relationship between the predicted and actual results while multiple linear regression (MLR) model presents a coefficient of determination of 82.46%.

Human-centricity in AI governance: A systemic approach.

Human-centricity is considered a central aspect in the development and governance of artificial intelligence (AI). Various strategies and guidelines highlight the concept as a key goal. However, we argue that current uses of Human-Centered AI (HCAI) in policy documents and AI strategies risk downplaying promises of creating desirable, emancipatory technology that promotes human wellbeing and the common good. Firstly, HCAI, as it appears in policy discourses, is the result of aiming to adapt the concept of human-centered design (HCD) to the public governance context of AI but without proper reflection on how it should be reformed to suit the new task environment. Second, the concept is mainly used in reference to realizing human and fundamental rights, which are necessary, but not sufficient for technological emancipation. Third, the concept is used ambiguously in policy and strategy discourses, making it unclear how it should be operationalized in governance practices. This article explores means and approaches for using the HCAI approach for technological emancipation in the context of public AI governance. We propose that the potential for emancipatory technology development rests on expanding the traditional user-centered view of technology design to involve community- and society-centered perspectives in public governance. Developing public AI governance in this way relies on enabling inclusive governance modalities that enhance the social sustainability of AI deployment. We discuss mutual trust, transparency, communication, and civic tech as key prerequisites for socially sustainable and human-centered public AI governance. Finally, the article introduces a systemic approach to ethically and socially sustainable, human-centered AI development and deployment.

The Ethical Implications of Artificial Intelligence (AI) For Meaningful Work

The increasing workplace use of artificially intelligent (AI) technologies has implications for the experience of meaningful human work. Meaningful work refers to the perception that one’s work has worth, significance, or a higher purpose. The development and organisational deployment of AI is accelerating, but the ways in which this will support or diminish opportunities for meaningful work and the ethical implications of these changes remain under-explored. This conceptual paper is positioned at the intersection of the meaningful work and ethical AI literatures and offers a detailed assessment of the ways in which the deployment of AI can enhance or diminish employees’ experiences of meaningful work. We first outline the nature of meaningful work and draw on philosophical and business ethics accounts to establish its ethical importance. We then explore the impacts of three paths of AI deployment (replacing some tasks, ‘tending the machine’, and amplifying human skills) across five dimensions constituting a holistic account of meaningful work, and finally assess the ethical implications. In doing so we help to contextualise the meaningful work literature for the era of AI, extend the ethical AI literature into the workplace, and conclude with a range of practical implications and future research directions.

The Role of Artificial Intelligence in Optimizing Rubber Manufacturing Processes

This review article examines how Artificial Intelligence (AI) can be used to optimize rubber production processes. The main goals are to list rubber manufacturers' difficulties, investigate AI applications, highlight significant discoveries, and discuss the policy ramifications for effective AI integration. Using a secondary data-based methodology, the study gathers information about AI applications unique to the rubber manufacturing business by reviewing a large body of literature from conferences, peer-reviewed journals, and industry reports. The results show that artificial intelligence (AI) technologies in rubber manufacturing facilitate improved process optimization, predictive maintenance, quality control, and adaptive process control. Artificial intelligence (AI)-powered technologies enhance compounded formulations, automate shaping procedures, forecast equipment breakdowns, and maximize resource efficiency. The policy consequences encompass resolving data privacy issues, allocating resources toward workforce training, instituting moral AI governance structures, and offering monetary incentives to encourage the deployment of AI. In summary, artificial intelligence has revolutionary prospects for rubber producers to improve productivity, excellence, and environmental friendliness. Rubber manufacturing processes can be made more innovative and continuously enhanced by embracing AI-driven solutions and strategic plans.

Ethical And Societal Implications of AI and Machine Learning

The research paper "Ethical and societal implications of AI and machine learning" examines the ethical and societal implications of the increasing use of artificial intelligence (AI) and machine learning. The paper explores various ethical concerns such as bias, transparency, accountability and privacy, which arise in the development and deployment of these technologies. Additionally, the paper also analyses the societal impacts of AI and machine learning, including the implications for employment, economic inequality, and social cohesion. The paper also discusses the need for regulation and governance to ensure the responsible development and use of these technologies. In summary, the paper highlights the importance of considering ethical and societal implications in the development and deployment of AI and machine learning, and the need for responsible governance to mitigate negative impacts and promote positive outcomes. Keywords: AI ethics, Machine learning ethics, AI societal impact, Machine learning societal impact, AI and society, Machine learning and society, AI governance