Artificial Intelligence Innovation Research Articles

Artificial intelligence innovation in healthcare: Relevance of reporting guidelines for clinical translation from bench to bedside.

Artificial intelligence (AI) and digital innovation are transforming healthcare. Technologies such as machine learning in image analysis, natural language processing in medical chatbots and electronic medical record extraction have the potential to improve screening, diagnostics and prognostication, leading to precision medicine and preventive health. However, it is crucial to ensure that AI research is conducted with scientific rigour to facilitate clinical implementation. Therefore, reporting guidelines have been developed to standardise and streamline the development and validation of AI technologies in health. This commentary proposes a structured approach to utilise these reporting guidelines for the translation of promising AI techniques from research and development into clinical translation, and eventual widespread implementation from bench to bedside.

Research on the Impact of Artificial Intelligence Innovation on Manufacturing Enterprise Idiosyncratic Risk

Under the background of the digital economy, the world is more closely connected, which also means the magnification of uncertainty and the elevation of the risk level of enterprise characteristics. How to make the enterprise have core competitiveness and reduce the enterprise's characteristic risk as much as possible, both the theoreticians and practitioners need to pay attention to and solve the problem. AI innovation is an important way for manufacturing enterprises to gain competitive advantage in the era of the digital economy. The existing data show that more scholars believe that technological innovation plays an important role in effectively reducing the risk of enterprise characteristics, and have a thorough discussion on it, it is found that not only the individual level of the enterprise factors will have an impact on the idiosyncratic risk, the internal resources of the enterprise will also have an impact on the idiosyncratic risk. As an important resource for enterprises to gain competitive advantage, redundant resources in this framework have attracted much attention from scholars. However, some studies have not revealed the mechanism and lack reliable empirical conclusions. Therefore, from the perspective of redundant resources to explore the impact of the digital economy era to reduce enterprise risk factors and their mechanisms have significant theoretical and practical value.

Emotional Artificial Intelligence as a Tool for Human-Machine Interaction

Introduction. One of the trends of global importance is the artificial intelligence (AI) and its innovations. One of such innovations has become emotional artificial intelligence (emotional AI/AI), it is called a revolutionary technology that can identify human emotions, process them in a timely manner and react “properly”. Experts consider emotional AI as an instrument that provides emotionally-oriented human–machine communication. The article discusses the specifics of emotional AI, achievements, potential opportunities, development prospects.Methodology and sources. The methodology of philosophical, socio-psychological, comparative and interdisciplinary approaches is used. The sources used in the article are: special literature of domestic and foreign authors (B. Goertzel, D. Goleman, R. Picard,D.I. Dubrovsky, E.M. Proidakov) scientific research, publications and websites devoted to emotional artificial intelligence, and its features (Aliya Green Emotional artificial intelligence: changing the human world for the better).Results and discussion. The relevance of the topic of emotional AI determined the need to refer to the concept of ”emotional intelligence” (EI) as the basic basis of emotional artificial intelligence, which allowed us to show the essential characteristics of human emotional intelligence, its difference from AI. Emotional artificial intelligence is an innovation of modern AI, its main actors are anthropomorphic robots, text, voice chatbots and video bots, which are already actively demonstrating to the public their knowledge and skills in the field of psychology of emotions, which are being improved within the framework of the current AI.Conclusion. Currently, there is a gradual process of teaching emotional AI to interact with a person, and although these achievements are not great yet, EII is gradually developing in accordance with the challenges of new realities within the specifics of modern applied AI. However, in the digital age, human–machine and machine-to-human communication is an interconnected process that should be aimed at building both utilitarian–useful and partnership relations in the practices of their interaction, which meets the requirements of the era and leads to further progress of AI – to the creation of a new, common AI – “human AI the level” which is supposed to greatly expand the capabilities of a person and society as a whole.

Evaluating Europe's push to enact AI regulations: how will this influence global norms?

ABSTRACT Artificial intelligence (AI) policy, innovation, and practice are moving ahead in rapid fashion. There is a growing mismatch between technological innovations in AI, which are advancing at a rapid rate, and normative and regulatory frameworks, which are lagging, particularly when it comes to protecting democratic values and human rights principles. National governments and multilateral institutions are attempting to catch up. At least 175 countries, firms and other organizations have produced documents listing ethical principles for AI. These efforts have proceeded in a somewhat fragmented manner, yet there are emerging signs of consolidation as the United States, Europe, and other countries begin to coalesce around shared principles. Europe, in particular, has raced ahead to draft comprehensive legislation, the Artificial Intelligence Act (AIA), to oversee these technologies and systems. What has motivated the European Union to pursue this approach? And how will this effort influence AI norms globally? This paper describes how Europe’s AI norm-building process represents an effort to ensure EU priorities are reflected in the AI governance landscape. Europe’s approach faces uncertainty. While it is likely that the AIA will meaningfully influence global AI norms, several factors may hinder its global diffusion and adoption.

Exploring Stakeholder Requirements to Enable Research and Development of Artificial Intelligence Algorithms in a Hospital-Based Generic Infrastructure: Results of a Multistep Mixed Methods Study.

Legal, controlled, and regulated access to high-quality data from academic hospitals currently poses a barrier to the development and testing of new artificial intelligence (AI) algorithms. To overcome this barrier, the German Federal Ministry of Health supports the "pAItient" (Protected Artificial Intelligence Innovation Environment for Patient Oriented Digital Health Solutions for developing, testing and evidence-based evaluation of clinical value) project, with the goal to establish an AI Innovation Environment at the Heidelberg University Hospital, Germany. It is designed as a proof-of-concept extension to the preexisting Medical Data Integration Center. The first part of the pAItient project aims to explore stakeholders' requirements for developing AI in partnership with an academic hospital and granting AI experts access to anonymized personal health data. We designed a multistep mixed methods approach. First, researchers and employees from stakeholder organizations were invited to participate in semistructured interviews. In the following step, questionnaires were developed based on the participants' answers and distributed among the stakeholders' organizations. In addition, patients and physicians were interviewed. The identified requirements covered a wide range and were conflicting sometimes. Relevant patient requirements included adequate provision of necessary information for data use, clear medical objective of the research and development activities, trustworthiness of the organization collecting the patient data, and data should not be reidentifiable. Requirements of AI researchers and developers encompassed contact with clinical users, an acceptable user interface (UI) for shared data platforms, stable connection to the planned infrastructure, relevant use cases, and assistance in dealing with data privacy regulations. In a next step, a requirements model was developed, which depicts the identified requirements in different layers. This developed model will be used to communicate stakeholder requirements within the pAItient project consortium. The study led to the identification of necessary requirements for the development, testing, and validation of AI applications within a hospital-based generic infrastructure. A requirements model was developed, which will inform the next steps in the development of an AI innovation environment at our institution. Results from our study replicate previous findings from other contexts and will add to the emerging discussion on the use of routine medical data for the development of AI applications. RR2-10.2196/42208.

Legal and Judicial Dealings with Artificial Intelligence as an Inventor

In this article associated with an opportunity for the court to consider whether artificial intelligence can be an inventor. In this case, the development of AI technology in the United States where AI has already become inventors and innovators. In this article, there are appropriate explanations for the development of artificial intelligence technology in which artificial intelligence technology has been used in various aspects. In this scenario, the detailed concept of artificial intelligence, which was used as an advanced technology in human life, took place. In this context, a legal and judicial approach was taken regarding the innovation of artificial intelligence, and the importance of the research is due to the fact that artificial intelligence has become a reality to a large extent in human life, and it was necessary to clarify whether it should be considered an inventor or not. The research aims to define artificial intelligence and Clarify whether artificial intelligence can be considered an inventor or not, and indicate how the law deals with the innovation of artificial intelligence, and indicate whether artificial intelligence should be considered an inventor? The research adopts the critical analysis approach with the concept of legal personality, artificial intelligence systems, and arguments related to whether artificial intelligence can be considered an inventor or not.

Machine learning based load prediction in smart‐grid under different contract scenario

AbstractMany progressed information scientific strategies, particularly Artificial Intelligence (AI) and profound learning methods, have been proposed and tracked down wide applications in our general public. This proposition creates information driven arrangements by utilizing the most recent profound learning and AI innovation, including outfit learning, meta‐learning and move learning, for energy the executives framework issues. Genuine world datasets are tried on proposed models contrasted and best in class plans, which exhibit the predominant presentation of the proposed model. In this proposition, the engineering of the Smart Grid testbed is additionally planned and created by using ML calculations and true remote correspondence frameworks to such an extent that constant plan necessities of Smart Grid testbed is met by this reconfigurable system with stacking of full convention in medium access control (MAC) and physical layers (PHY). The proposed engineering has the reconfiguration property in view of the organization of remote correspondence and trend setting innovations of Information and communication technologies (ICT) which incorporates Artificial Intelligence (AI) calculation. The fundamental plan objectives of the Smart Grid testbed is to make it simple to construct, reconfigure and scale to address the framework level prerequisites and to address the ongoing necessities.

Artefacts, routines, and co-production: a pioneering case of artificial intelligence-based health services in Argentina

ABSTRACT The article presents innovation in artificial intelligence (AI)-based health services as a co-production process between actors and digital artefacts that increase automation and augmentative routine dynamics. The co-production process is analysed from the routine dynamics and contributions of science and technology studies and complexity theory. An in-depth single case of co-production based on AI is analysed to illustrate the innovation process at the micro level. The analysis reveals four findings: (1) innovation in services based on AI-technological solutions emerges from sociotechnical assemblages enacted by actors, artefacts, and routine dynamics; (2) technological solutions based on AI are emergent properties; (3) co-production of technological solutions based on AI are contextualised on situated action and embedded in a cognitive distribution system that leads to automated and augmentative routine dynamics; and (4) the adoption of standardised AI-based technological solutions transforms institutional arrangements. Implications for policymakers and future research agendas are also outlined.

AI-tocracy

Abstract Recent scholarship has suggested that artificial intelligence (AI) technology and autocratic regimes may be mutually reinforcing. We test for a mutually reinforcing relationship in the context of facial-recognition AI in China. To do so, we gather comprehensive data on AI firms and government procurement contracts, as well as on social unrest across China since the early 2010s. We first show that autocrats benefit from AI: local unrest leads to greater government procurement of facial-recognition AI as a new technology of political control, and increased AI procurement indeed suppresses subsequent unrest. We show that AI innovation benefits from autocrats’ suppression of unrest: the contracted AI firms innovate more both for the government and commercial markets and are more likely to export their products; noncontracted AI firms do not experience detectable negative spillovers. Taken together, these results suggest the possibility of sustained AI innovation under the Chinese regime: AI innovation entrenches the regime, and the regime’s investment in AI for political control stimulates further frontier innovation.

Artificial Intelligence in FMCG Retail Sector

The paper presents theoretical research on the diffusion of artificial intelligence (AI) in FMCG. As a result, the four main areas of AI innovation were discovered: customer experience, logistics, manufacturing, and business model. The relationship of AI with the proven technology for customer relationship management – CRM – is also revealed. Current benefits and opportunities for future optimizing business processes through digital transformation are indicated. Emphasis is placed on machine learning. Examples of using artificial intelligence among leading companies in the FMCG sector are given, as well as an example of service transformation in a supermarket in Germany during Covid-19, etc.

Artificial Intelligence Applications Featuring Ease and Safety Factors at the Two Holy Mosques

To ensure that millions of Muslim visitors from around the world receive high-quality services, leadership, and the latest administrative practices at the Grand Mosque, the Presidency of the Two Holy Mosques is committed to providing the best services, leadership, and administrative practices. In addition, reducing physical effort and enhancing smart technology will improve visitors' daily services to keep up with Vision 2030 and the presidency's strategic plan for 2024. Artificial Intelligence (AI) represents the biggest project applied by the presidency to keep pace with vision, where the presidency provided several applications based on AI to enhance ease and safety factors for pilgrims and worshipers at the Two Holy Mosques. This research, entitled "Artificial intelligence applications featuring ease and safety factors at the Two Holy Mosques" is important in highlighting the aspects of Saudi Arabia's interest in artificial intelligence in the Two Holy Mosques to achieve safety and ease and to achieve global competition in this side. To achieve these aspects, the descriptive and analytical approach was used. I dealt with it through the following demands: presenting some literary reviews and presenting some applications that contribute to safety and ease. And I concluded the most important results: achieving artificial intelligence innovations in the Two Holy Mosques for security and ease and demonstrating aspects of diversity in these applications.

Artificial Intelligence strategy of the Uzbekistan: Policy framework, Preferences, and challenges

Artificial intelligence strategies refer to the plans and actions taken by governments to develop and apply AI technologies to achieve specific goals. This article explores Uzbekistan's policies and preferences regarding the development and implementation of artificial intelligence (AI) technologies. The study examines the country's national strategies and regulatory frameworks for AI, as well as the challenges it faces in realizing its AI ambitions. The analysis reveals that Uzbekistan sees AI as a key enabler of economic growth, social development, and modernization, and aims to become a regional leader in AI by 2030. To achieve this goal, the government has launched several initiatives, such as establishing AI research centers, promoting entrepreneurship and innovation, and investing in digital infrastructure. However, the article also identifies several obstacles, such as a lack of skilled workforce, limited funding, and ethical and legal concerns. The study concludes by providing recommendations for how Uzbekistan can address these challenges and strengthen its AI ecosystem.

Artificial Intelligence, Augmented Reality, and Virtual Reality Advances and Applications in Interventional Radiology.

Artificial intelligence (AI) uses computer algorithms to process and interpret data as well as perform tasks, while continuously redefining itself. Machine learning, a subset of AI, is based on reverse training in which evaluation and extraction of data occur from exposure to labeled examples. AI is capable of using neural networks to extract more complex, high-level data, even from unlabeled data sets, and better emulate, or even exceed, the human brain. Advances in AI have and will continue to revolutionize medicine, especially the field of radiology. Compared to the field of interventional radiology, AI innovations in the field of diagnostic radiology are more widely understood and used, although still with significant potential and growth on the horizon. Additionally, AI is closely related and often incorporated into the technology and programming of augmented reality, virtual reality, and radiogenomic innovations which have the potential to enhance the efficiency and accuracy of radiological diagnoses and treatment planning. There are many barriers that limit the applications of artificial intelligence applications into the clinical practice and dynamic procedures of interventional radiology. Despite these barriers to implementation, artificial intelligence in IR continues to advance and the continued development of machine learning and deep learning places interventional radiology in a unique position for exponential growth. This review describes the current and possible future applications of artificial intelligence, radiogenomics, and augmented and virtual reality in interventional radiology while also describing the challenges and limitations that must be addressed before these applications can be fully implemented into common clinical practice.

Artificial intelligence in hospitality and tourism. State of the art and future research avenues

ABSTRACT The hospitality and tourism industry has recently undergone a profound transformation driven by information and communication technologies developments thanks to innovations in Artificial Intelligence and its tools. However, although the literature on AI and tourism is growing, scientific research on these themes is still fuzzy and fragmented. This article aims to explore the current state of the art and possible future developments of AI and its tools in tourism. The study develops a bibliometric analysis using the ISI database and applies social network analysis to map its intellectual structure. Then, the study develops a qualitative literature review to investigate the main research themes and propose future research avenues. The findings identify some main research strands such as: Big Data for demand forecasting and customer satisfaction; Augmented Reality and Virtual Reality for co-creation processes; the COVID-19 pandemic, social distances issues and service robots; and the smart tourism trends.

Research on Impact of Artificial Intelligence Orientation on Enterprise Green Innovation

As the core technology to lead the digital future and promote the industrial revolution, artificial intelligence (AI) technology can empower the whole chain of manufacturing enterprises, accelerate the fundamental transformation of production mode and enterprise form, guide enterprises to integrate artificial intelligence with green innovative technology, and improve energy efficiency. Based on the background of green transformation faced by the manufacturing industry, we discuss the mechanism of artificial intelligence to realize green innovation of enterprises through dynamic capabilities. AI orientation refers to the tendency and strategy of enterprises in introducing and applying AI technology. Based on the strategic management theory and the resource-based view, a questionnaire survey for manufacturing enterprises was conducted. Using regression analysis method to conduct empirical research on 204 valid questionnaire data, to test the hypothesis. The result shows that AI has a significant positive effect on the green innovation of enterprises. Both strategic sensing capacity and timely decision-making capacity positively affect the green innovation of enterprises. Strategic sensing capacity and timely decision-making capacity play a partial mediating role between AI and green innovation. AI gives the light to enterprise development to clarify the direction of progress of the production technology. Enterprises need to establish AI-orientated strategies in many aspects to realize green innovation and improve environmental performance.

Digital disruption: artificial intelligence and international trade policy

AbstractDigitalization of the global economy is occurring apace and has spurred a new wave of trade negotiations, as governments and technology firms vie to establish international rules and standards for the digital era. This article examines the ways that trade policy-makers are responding to artificial intelligence (AI), arguably the most disruptive of the new digital technologies. In a digitalized global economy, trade rules have implications for AI innovation, uptake, and governance, yet existing trade rules have significant shortcomings and need updating in order to assist with effective AI governance. Updating is happening but, so far, the changes focus on promoting AI and disproportionately reflect the interests of large technology firms, the major innovators and owners of AI. New digital trade rules include stringent intellectual property protections for source code and algorithms, and strong commitments to enable the free flow of data across borders. However, much less progress has been made in addressing cross-border risks and harms associated with AI, in areas such as competition policy; ethical, transparent, and accountable use of AI; personal data protection; and protections against the exploitative use of algorithms in consumer and labour markets.

Artificial Intelligence and Efficiency Operations in Abu Dhabi National Oil Company in UAE

This study was conducted to investigate the effect of Artificial intelligence on the efficiency operations in Abu Dhabi oil and gas industry in UAE. This most especially in relation to quality of data, process efficiency, product-cost efficiency, task efficiency, process flexibility and quality of operations. The researcher used the survey descriptive research design, structured questionnaire and sampling strategies such as simple and stratified random sampling to garner data from participants. Primary data for the research scientific study was collected from 253 respondents as sample size out of 10563 people as target population in Abu Dhabi oil and gas industry in UAE. The tools for data analysis that were used include descriptive statistics, Pearson Linear Correlation Coefficient, regression analysis and Analysis of Variance. Statistical Package for the Social Sciences (SPSS) was used as a software to enter data into the computer for analysis. It was found out that the adoption of technological innovation of artificial intelligence has positive effects on quality of data, process efficiency, product-cost efficiency, task efficiency, process flexibility and quality of operations at UAE oil and gas companies. It was concluded that all governments in the world must put more emphasis on artificial intelligence (AI) implementation strategies because it has a positive impact on quality of data, process efficiency, product-cost efficiency, task efficiency, process flexibility and quality of operation which may act as an impetus to growth and development in any country. It was recommended that the top management of UAE should make more proactive policies that can help guide government and company officials in the best ways to execute artificial intelligence (AI) activities and programs so as to be more effective in government and organisational settings.

“Facilitators” vs “substitutes”: the influence of artificial intelligence products’ image on consumer evaluation

PurposeThe rapid innovation of artificial intelligence (AI) technology promotes the prosperity of the AI product market. However, consumers seem to have negative attitudes (e.g. prejudice, aversion) toward AI products and services. Those negative attitudes are rooted in the fear that AI might replace humans. The authors thus propose that turning the image of AI from substitutes to facilitators can alleviate identity threat perception. This paper aims to examine how the image of AI products (facilitators vs substitutes) influences consumer evaluation and explores the underlying mechanism and boundary conditions.Design/methodology/approachThis study uses four experiments with between-subjects designs to investigate whether the image of AI products (facilitators vs substitutes) will affect consumer evaluation in specific consumption and service scenarios. The same products (or services) were manipulated as “substitute” or “facilitator” through advertisement slogans. Participants were randomly assigned to a condition and read the advertisement, then they reported their evaluation. The mediator perceived identity threat and the moderator preconceived perceptions of AI risks were measured by scales. The moderator, self-affirmation, was manipulated through the instruction of the experiment.FindingsThis study demonstrates that consumers give higher evaluation of AI products in the image of the facilitator than in the image of the substitute (Study 1). The underlying mechanism is that the perceived identity threat caused by “facilitator” products is lower than “substitute” products (Study 2). The effect of AI image is moderated by consumers’ preconceived perceptions of AI risks (Study 3) and self-affirmation (Study 4). Specifically, for consumers who have a strong AI risk-perception, this effect exists, but it disappears for consumers who have a weak AI risk perception. When consumers are given a strong self-affirmation, the negative impact of the “substitute” image disappears.Originality/valueThis paper analyzes the psychological root of consumers’ negative evaluation of AI technology from the perspective of AI’s image. The proposed typology of “substitutes” and “facilitators” helps expand the vision on brand/product image and enriches the research on consumer self-identity in today’s highly informatized market. The findings shed light on how to choose appropriate image for AI products, which will be crucial for increasing consumers’ acceptance of AI products.

The state’s role in governing artificial intelligence: development, control, and promotion through national strategies

Numerous governments worldwide have issued national artificial intelligence (AI) strategies in the last five years to deal with the opportunities and challenges posed by this technology. However, a systematic understanding of the roles and functions that the governments are taking is lacking in the academic literature. Therefore, this research uses qualitative content analysis and Latent Dirichlet Allocation (LDA) topic modeling methodologies to investigate the texts of 31 strategies from across the globe. The findings of the qualitative content analysis highlight thirteen functions of the state, which include human capital, ethics, R&D, regulation, data, private sector support, public sector applications, diffusion and awareness, digital infrastructure, national security, national challenges, international cooperation, and financial support. We combine these functions into three general themes, representing the state’s role: development, control, and promotion. LDA topic modeling results are also reflective of these themes. Each general theme is present in every national strategy’s text, but the proportion they occupy in the text is different. The combined typology based on two methods reveals that the countries from the post-soviet bloc and East Asia prioritize the theme “development,” highlighting the high level of the state’s involvement in AI innovation. The countries from the EU focus on “control,” which reflects the union’s hard stance on AI regulation, whereas countries like the UK, the US, and Ireland emphasize a more hands-off governance arrangement with the leading role of the private sector by prioritizing “promotion.”

Defining the Future: The AI Act's Potential in equitably Safeguarding Fundamental Rights and Promoting AI Innovation

The recent advancements in the realm of Artificial Intelligence (AI) feature a landscape filled with exciting prospects and lurking dangers in equal measure, necessitating a nuanced, human-centred regulatory approach. The EU is the first organisation to have undertaken this challenge with binding law through its AI Act. This paper delves into the complex task of this founding stone of EU AI law to balance promoting innovation and safeguarding fundamental rights. It addresses both the Act’s pre-history – encompassing the relevant policy documents before the European Commission's proposal – and its history, which includes the negotiations and amendments leading up to the final trilogue session on the 6th of December 2023 and the consequential provisional agreement, followed by its approval by COREPER and the IMCO and LIBE committees and its final upvoting in the European Parliament on the 13th of March 2024. Keeping track of the Act’s legislative journey, including the context in which said journey evolved, shall be crucial in order to comprehensively understand, interpret, and apply what is expected to be one of Europe’s most impactful pieces of legislation to date. The paper aims to bridge a gap in scholarly discussion around the AI Act, which has so far focused solely on evaluating fundamental rights protection or assessing cost-efficiency, providing a practical approach that consolidates the legislative process and appraises the AI Act’s overall potential in protecting fundamental rights, while equitably maintaining AI innovation. The research question it seeks to answer is: How can the AI Act promise sufficient fundamental rights protection without compromising AI innovation and overburdening enterprises? The paper is structured to provide a comprehensive analysis of the Act in view of answering the research question. The Overview section (II.) will give the historical background of AI and track the main policy documents that led to the AI Act’s proposal offering a source also for retrospective research in future. It will also identify specific key points of the Commission’s original proposal that call for examination and establish its connection to fundamental rights protection, in view of being presented as a product safety regulation. The section on the Legislative State of the Art and Remaining Pitfalls (III.) will present the fruits the legislative process has yielded up until the confirmation of the agreed final draft by the IMCO and LIBE committees on the 13th of February 2024 and its subsequent approval in the Parliament a month later. It will enumerate the main points introduced with the negotiated amendments to then identify still existing potential pitfalls with regards to the Act’s scope, enforcement mechanism and future proofness, and how this creates weaknesses for innovation and fundamental rights. The Balancing section (IV.) offers suggestions to counter the weaknesses that were identified in the previous part, uncovering the balancing point between fundamental rights protection and innovation. Finally, the paper will conclude (V.) by summarising the findings and the answer to the research question while leaving room for future discussion.