Artificial Intelligence Innovation Research Articles

Connected Health: Revolutionizing Patient Care Through Artificial Intelligence Innovations

Connected Health: Revolutionizing Patient Care Through Artificial Intelligence Innovations

Organizing public sector AI adoption: Navigating between separation and integration

Artificial Intelligence (AI) has the potential to improve public governance, but the use of AI in public organizations remains limited. In this qualitative study, we explore how public organizations strategically manage the adoption of AI. Managing AI adoption in the public sector is complex because of the inherent tension between public organizations' identity, characterized by formal and rigid structures, and the demands of AI innovation that require experimentation and flexibility. Our findings show that public organizations navigate this tension either by creating separate departments for data science teams, or by integrating data science teams into already existing operational departments. The case studies reveal that separation improves the technical expertise and capabilities of the organization, whereas integration improves the alignment between AI and primary processes. The findings also show that both approaches are characterized by different AI adoption barriers. We empirically identify the processes and routines public organizations develop to overcome these barriers.

Does artificial intelligence kill employment growth: the missing link of corporate AI posture.

An intense debate has been on-going about how artificial intelligence (AI) technology investments have an impact on employment. The debate has often focused on the potential of AI for human task automation, omitting the strategic incentive for firms to cooperate with their workers as to exploit AI technologies for the most relevant benefit of new product and service innovation. We calibrate an empirical probit regression model of how changes in employment relate to AI diffusion, based on formalizing a game-theoretical model of a firm exploiting the twin role of AI innovation and AI automation for both absolute and competitive advantage. The theoretical game-theory prediction is that employment following AI technology adoption is not negative, and ultimately depends on how AI leads to new success in innovation, competition which defines the competitive reward of innovation and profit sharing between workers and firms. Our estimation, is based on a global survey of 3,000 large companies across 10 countries, demonstrates that a firm employment growth depends on two strategic postures, that is, the firm relative maturity of AI adoption as well as its relative bias toward AI-based product innovation. The contribution of this research is to highlight the twin role of firm and workers in shaping how technology will affect employment. AI in particular marries the potential of task automation with even more potential for expansion.

Synergizing Solar Cell Technology, Radio Waves, AI, and Business: A Comprehensive Review

This thorough review article reveals the amazing possibilities for innovation, sustainability, and connectedness by examining the revolutionary synergy of solar cell technology, radio waves, artificial intelligence (AI), and business. A new era of possibilities has emerged from the merging of these dynamic sectors, ranging from using AI to optimize solar energy generation to utilizing 5G technology and the Internet of Things (IoT) to revolutionize wireless communication. These technologies' convergence provides an ideal environment for entrepreneurship, as demonstrated by successful examples like Verizon's 5G-enabled smart cities and Sun Power’s AI-enhanced solar panels. Although this convergence offers ground-breaking potential, it also brings with it difficult problems. These include the effective management of electromagnetic spectrum, ethical AI concerns, and regulatory difficulties. In order to effectively manage this constantly changing environment, stakeholders need to support international collaboration, STEM education, flexible regulatory frameworks, and interdisciplinary research. To fully realize the revolutionary potential of these technologies, workforce development, incentives for sustainable habits, and sustainable technology research are crucial. In addition, in order to spur innovation in green AI, energy-efficient wireless communication, and renewable energy, governments and organizations need to make investments in green technology. It is critical to comprehend the main lessons in light of the changing environment at the nexus of these dynamic disciplines. These include the necessity of inclusivity, the value of sustainability, and the importance of ethical AI. Adopting these tenets will direct us toward a future marked by continuous progress, convergence of technology, and a flourishing innovation environment. Lessons from this review article will act as a compass for people who want to create positive change, maximize synergy, and take advantage of the endless opportunities that lie ahead as we navigate this environment. By working together, we can build a more affluent, connected, and sustainable future for everybody.

Revolutionizing Agriculture: A Case Study of IBM's AI Innovations

Background / Purpose: The development of computer systems that can carry out tasks that traditionally require human intelligence is referred to as artificial intelligence (AI). It entails the development of intelligent machines that can reason, learn, solve issues, and make judgements. A fast-developing topic, AI has enormous ramifications for many different businesses and facets of society. By leveraging advanced algorithms and data analysis techniques, AI systems can process and interpret large amounts of information in real-time, enabling them to extract valuable insights and patterns that may be difficult for humans to perceive.AI technologies have a wide range of applications across multiple domains, including healthcare, finance, transportation, manufacturing, education, entertainment, including agriculture. When referring to AI in the context of agriculture, we mean the use of advanced analytics and computational algorithms to analyse massive volumes of agricultural data, anticipate the future, and give farmers and stakeholders useful information. The main goal of using AI to agriculture is to increase efficiency, sustainability, and productivity across a range of farming operations, also to create smart and efficient systems that can monitor, analyze, and control water resources in real-time, leading to improved water management and sustainable agricultural practices, thereby addressing the challenges faced by the agricultural sector.AI offers significant potential to optimize water usage, enhance crop productivity, and mitigate environmental impact. In this paper, IBM, a significant provider of services in the sector of agriculture in recent years, is examined. Objective: In this case study, artificial intelligence is the main topic with particular emphasis on IBM's agricultural technology. Design/Methodology/Approach: Academic works published in a variety of peer-reviewed journals, conferences, and business websites provided the necessary information and specifics for this case study on IBM. Findings/Result: This study is primarily concerned with the usefulness and significance of AI in the modern world. The demand for and necessity of the numerous resources. provided by IBM, discussion topics include the company's business plan, varied results, top clientele, and numerous service types. Originality/Value: The analysis gives a concise description of IBM, the types of data collected and managed, information on artificial intelligence (AI), and the numerous AI services offered by IBM. Paper Type: Case study on the importance of storage and computing requirements for AI services offered by different service. providers, with a focus on IBM.

Crisis or Opportunity? The Disruption and Innovation of Artificial Intelligence in School Education— Starting With ChatGPT

Crisis or Opportunity? The Disruption and Innovation of Artificial Intelligence in School Education— Starting With ChatGPT

Artificial Intelligence and Postoperative Monitoring in Plastic Surgery

Technological innovation has fueled an evolving landscape in plastic surgery. Recently, artificial intelligence (AI) has demonstrated tremendous potential in enhancing our diagnostic ability, automating data acquisition for research purposes, and supplementing our intraoperative decision-making. Over the last two decades, advancements in AI enhanced pre- and intraoperative management of plastic surgery patients. However, the demand to keep plastic surgery patients out of hospital during the COVID-19 pandemic has recently inspired important AI innovations in postoperative care, such as telemedicine and remote patient monitoring. As we transition into the post-COVID era of medicine, these novel technologies will be critical in enhancing patient safety and satisfaction, while reducing rising healthcare costs. Herein, we review the basic principles of AI in plastic surgery and illustrate its significance in remote postoperative monitoring.

UTILIZATION OF ARTIFICAL INTELLIGENCE IN MARKETING CATALOG INNOVATION OF JAMBI SOUVE PRODUCTS

In the 5.0 era, the focus is on human mindset based on technology, one of which is utilizing marketing innovation to increase economic growth in Indonesia. Development of a computer system that is capable of being run by humans in completing life tasks at the financial level of the economy, namely being able to apply an Artificial Intelligence (AI) system which is unique because it is able to work according to certain computer system programming algorithms, programming algorithms are an artificial intelligence framework for processing various types of data . AI has many benefits in various aspects of life, especially in the economic aspect in Indonesia. Economic growth in Indonesia has experienced a decline due to Covid-19, one of which is Jambi Province which has experienced a decline in economic growth. Based on data obtained from the Bank Indonesia Representative for Jambi Province, it shows that economic growth in Jambi City in 2019 was 4.35%, in 2020 it changed to -0.51% from 11,000 MSMEs during the Covid-19 period which resulted in bankruptcy. In 2021, 8,300 MSMEs were registered in Jambi City, amounting to 3.69%. In 2022 it will increase to 5.13% with the number of MSMEs now reaching more than 60,000, with an average of dominant businesses in the culinary food and beverage sector. With the increase in MSME players every year, marketing competitiveness is getting stronger. This requires MSME players to be able to adapt to keep up with the times, while consumer behavior currently prefers to shop online considering the time and affordability of cheap prices compared to shopping in person. The research method used is a qualitative descriptive approach. Data collection techniques were carried out through observation, interviews and news on the WEB. This research uses the Manual Data Analysis Procedure (MDAP) analysis technique to analyze transcription data from interviews that have been obtained directly from the field by informants who follow the purposive sampling technique. These findings use the theory of utilizing AI and business marketing innovations which make it easier for producers and consumers to obtain the right profits.

Physicians’ and Machine Learning Researchers’ Perspectives on Ethical Issues in the Early Development of Clinical Machine Learning Tools: Qualitative Interview Study

Background Innovative tools leveraging artificial intelligence (AI) and machine learning (ML) are rapidly being developed for medicine, with new applications emerging in prediction, diagnosis, and treatment across a range of illnesses, patient populations, and clinical procedures. One barrier for successful innovation is the scarcity of research in the current literature seeking and analyzing the views of AI or ML researchers and physicians to support ethical guidance. Objective This study aims to describe, using a qualitative approach, the landscape of ethical issues that AI or ML researchers and physicians with professional exposure to AI or ML tools observe or anticipate in the development and use of AI and ML in medicine. Methods Semistructured interviews were used to facilitate in-depth, open-ended discussion, and a purposeful sampling technique was used to identify and recruit participants. We conducted 21 semistructured interviews with a purposeful sample of AI and ML researchers (n=10) and physicians (n=11). We asked interviewees about their views regarding ethical considerations related to the adoption of AI and ML in medicine. Interviews were transcribed and deidentified by members of our research team. Data analysis was guided by the principles of qualitative content analysis. This approach, in which transcribed data is broken down into descriptive units that are named and sorted based on their content, allows for the inductive emergence of codes directly from the data set. Results Notably, both researchers and physicians articulated concerns regarding how AI and ML innovations are shaped in their early development (ie, the problem formulation stage). Considerations encompassed the assessment of research priorities and motivations, clarity and centeredness of clinical needs, professional and demographic diversity of research teams, and interdisciplinary knowledge generation and collaboration. Phase-1 ethical issues identified by interviewees were notably interdisciplinary in nature and invited questions regarding how to align priorities and values across disciplines and ensure clinical value throughout the development and implementation of medical AI and ML. Relatedly, interviewees suggested interdisciplinary solutions to these issues, for example, more resources to support knowledge generation and collaboration between developers and physicians, engagement with a broader range of stakeholders, and efforts to increase diversity in research broadly and within individual teams. Conclusions These qualitative findings help elucidate several ethical challenges anticipated or encountered in AI and ML for health care. Our study is unique in that its use of open-ended questions allowed interviewees to explore their sentiments and perspectives without overreliance on implicit assumptions about what AI and ML currently are or are not. This analysis, however, does not include the perspectives of other relevant stakeholder groups, such as patients, ethicists, industry researchers or representatives, or other health care professionals beyond physicians. Additional qualitative and quantitative research is needed to reproduce and build on these findings.

TDM copyright for AI in Europe: a view from Portugal

Abstract The development of artificial intelligence (AI) justified the introduction at the level of the European Union (EU) of a new copyright exception regarding text and data mining (TDM) for purposes of scientific research conducted by research organizations and entities responsible for cultural heritage. This exception is binding and mandatory, as Member States have to provide it in their domestic laws and it cannot be excluded by contract nor by technical measures. In addition to this exception for scientific purposes, a general TDM exception is provided, which is also binding but only applies by default, meaning that it can be excluded by contract and/or by technical measures. The default TDM exception also applies to computer programs, raising the question whether acts allowed under the Software Directive can now be excluded by contract and/or technical measures. It is far from clear whether the new regime extends or, on the contrary, restricts freedom of innovation in AI and whether and how the TDM copyright will contribute to the development of AI and encourage ‘machine learning’ in Europe. The Portuguese implementation of the new ‘TDM copyright’ has been faithful to EU law, and therefore, AI development in Portugal will be carried out under the copyright terms of the Digital Single Market Directive, which seeks a fair balance between the principle of high-level protection of rightholders, the rights and freedoms of users and the good functioning of the digital market in the EU.

Exploring patient perspectives on how they can and should be engaged in the development of artificial intelligence (AI) applications in health care

BackgroundArtificial intelligence (AI) is a rapidly evolving field which will have implications on both individual patient care and the health care system. There are many benefits to the integration of AI into health care, such as predicting acute conditions and enhancing diagnostic capabilities. Despite these benefits potential harms include algorithmic bias, inadequate consent processes, and implications on the patient-provider relationship. One tool to address patients’ needs and prevent the negative implications of AI is through patient engagement. As it currently stands, patients have infrequently been involved in AI application development for patient care delivery. Furthermore, we are unaware of any frameworks or recommendations specifically addressing patient engagement within the field of AI in health care.MethodsWe conducted four virtual focus groups with thirty patient participants to understand of how patients can and should be meaningfully engaged within the field of AI development in health care. Participants completed an educational module on the fundamentals of AI prior to participating in this study. Focus groups were analyzed using qualitative content analysis.ResultsWe found that participants in our study wanted to be engaged at the problem-identification stages using multiple methods such as surveys and interviews. Participants preferred that recruitment methodologies for patient engagement included both in-person and social media-based approaches with an emphasis on varying language modalities of recruitment to reflect diverse demographics. Patients prioritized the inclusion of underrepresented participant populations, longitudinal relationship building, accessibility, and interdisciplinary involvement of other stakeholders in AI development. We found that AI education is a critical step to enable meaningful patient engagement within this field. We have curated recommendations into a framework for the field to learn from and implement in future development.ConclusionGiven the novelty and speed at which AI innovation is progressing in health care, patient engagement should be the gold standard for application development. Our proposed recommendations seek to enable patient-centered AI application development in health care. Future research must be conducted to evaluate the effectiveness of patient engagement in AI application development to ensure that both AI application development and patient engagement are done rigorously, efficiently, and meaningfully.

ChatGPT for Teachers and Students in Science Learning: A Systematic Literature Review

In Indonesia Science education has not reached a portion that is in accordance with the needs of science education. Science learning should not only focus on knowledge but must also be balanced with application in everyday life. With this application, students will know that what they learn in science is applied and useful for life. That way, students will continue to try to experiment because of their high curiosity so that students are able to produce scientific products. Science problems are not only experienced by students, but teachers who teach science at school also have science problems they face. For this reason, Chat GPT (Generative Pre-Trainer Transformer) is used as one of the Open AI (Artificial Intelligence) innovations, a research company engaged in the development of artificial intelligence. The purpose of this research is to explain GPT Chat for Teachers and Students in Science Learning. A review is conducted on the state-of-the-art methods using the preferred reporting items for reviews and meta-analyses (PRISMA) guidelines. The results of this research show that Chat GPT has main functions, Chat GPT also has many uses for teachers and students, and science learning has five domains of science education, which must be supported by appropriate technological sophistication.

Reading Big Data by Machine Learning: The Used of Computer Science for Human Life

Machine learning (ML) models use big data to learn and improve predictability and performance automatically through experience and data, without being programmed to do so by humans. Artificial Intelligence (AI) techniques are being increasingly deployed in finance, in areas such as asset management, algorithmic trading, credit underwriting or blockchain-based finance, enabled by the abundance of available data and by affordable computing capacity. The purpose of this study is to describe in detail how the power of artificial intelligence with its complex system can help the needs of digital technology in the banking sector. The research method used is the elaboration of great thoughts and facts about artificial intelligence. Scientific data is interpreted with analytical power that is as precise as possible, so as to produce a description that meets the logic of structured thinking. The data is taken from relevant and up-to-date literature, the work of scientists who have been disseminated in various weighty scientific publications at the world level. The report can help policy makers to assess the implications of these new technologies and to identify the benefits and risks related to their use. It suggests policy responses that that are intended to support AI innovation in finance while ensuring that its use is consistent with promoting financial stability, market integrity and competition, while protecting financial consumers. Emerging risks from the deployment of AI techniques need to be identified and mitigated to support and promote the use of responsible AI. Existing regulatory and supervisory requirements may need to be clarified and sometimes adjusted, as appropriate, to address some of the perceived incompatibilities of existing arrangements with AI applications.

The effect of medical artificial intelligence innovation locus on consumer adoption of new products

The purpose of this study is to explore the effect of innovation locus on consumers' adoption of artificial intelligence products in healthcare. Across four experiments, we demonstrate that consumers are more likely to accept medical artificial intelligence for peripheral products than for core products. This effect is mediated by perceived risk and moderated by a tight-loose culture. Specifically, perceived risk significantly mediates the effect of innovation loci on consumers' adoption of medical artificial intelligence. Loose and tight culture moderates the direct and mediating effects. These findings are important theoretical contributions to the artificial intelligence and healthcare literature. We also provide some practical implications to promote the future development of medical artificial intelligence products and to improve consumers' acceptance of medical artificial intelligence.

Understanding Lived Experience: Bridging Artificial Intelligence and Natural Language Processing with Humanities and Social Sciences

True human-centered Artificial Intelligence (AI) is impossible without addressing the inherent and diverse aspects of humanness. Deep learning models have achieved remarkable success in some tasks in vision and language processing, and few can deny it. However, as it moves forward, the field cannot continue to pretend it can do it all by itself, especially when we advertise it as ‘human-centered AI’. It has come the time to open up the stage for methodological pluralism in the interest of critical and democratic science, and for the benefit of society. In this paper, I want to draw particular attention to the aspect of lived (subjective) experience, one research area highly misunderstood and hugely neglected in AI, and especially in Natural Language Processing (NLP). Our intensions, selfhood, autonomy, emotions, feelings, sensory knowledge, cultural history are integral components of our intelligence. Thus, the future AI and NLP models will need to more closely align with the embodied component of human intelligence. As we push the limit of creativity and innovation in AI, we need to develop a new way of looking at human experience, with a better scientific understanding of intelligence and its own practices, at the intersection of many disciplinary fields.

Analysis of Artificial Intelligence-Based Approaches Applied to Non-Invasive Imaging for Early Detection of Melanoma: A Systematic Review.

Melanoma, the deadliest form of skin cancer, poses a significant public health challenge worldwide. Early detection is crucial for improved patient outcomes. Non-invasive skin imaging techniques allow for improved diagnostic accuracy; however, their use is often limited due to the need for skilled practitioners trained to interpret images in a standardized fashion. Recent innovations in artificial intelligence (AI)-based techniques for skin lesion image interpretation show potential for the use of AI in the early detection of melanoma. The aim of this study was to evaluate the current state of AI-based techniques used in combination with non-invasive diagnostic imaging modalities including reflectance confocal microscopy (RCM), optical coherence tomography (OCT), and dermoscopy. We also aimed to determine whether the application of AI-based techniques can lead to improved diagnostic accuracy of melanoma. A systematic search was conducted via the Medline/PubMed, Cochrane, and Embase databases for eligible publications between 2018 and 2022. Screening methods adhered to the 2020 version of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Included studies utilized AI-based algorithms for melanoma detection and directly addressed the review objectives. We retrieved 40 papers amongst the three databases. All studies directly comparing the performance of AI-based techniques with dermatologists reported the superior or equivalent performance of AI-based techniques in improving the detection of melanoma. In studies directly comparing algorithm performance on dermoscopy images to dermatologists, AI-based algorithms achieved a higher ROC (>80%) in the detection of melanoma. In these comparative studies using dermoscopic images, the mean algorithm sensitivity was 83.01% and the mean algorithm specificity was 85.58%. Studies evaluating machine learning in conjunction with OCT boasted accuracy of 95%, while studies evaluating RCM reported a mean accuracy rate of 82.72%. Our results demonstrate the robust potential of AI-based techniques to improve diagnostic accuracy and patient outcomes through the early identification of melanoma. Further studies are needed to assess the generalizability of these AI-based techniques across different populations and skin types, improve standardization in image processing, and further compare the performance of AI-based techniques with board-certified dermatologists to evaluate clinical applicability.

Metaverse Wearables for Immersive Digital Healthcare: A Review.

The recent exponential growth of metaverse technology has been instrumental in reshaping a myriad of sectors, not least digital healthcare. This comprehensive review critically examines the landscape and future applications of metaverse wearables toward immersive digital healthcare. The key technologies and advancements that have spearheaded the metamorphosis of metaverse wearables are categorized, encapsulating all-encompassed extended reality, such as virtual reality, augmented reality, mixed reality, and other haptic feedback systems. Moreover, the fundamentals of their deployment in assistive healthcare (especially for rehabilitation), medical and nursing education, and remote patient management and treatment are investigated. The potential benefits of integrating metaverse wearables into healthcare paradigms are multifold, encompassing improved patient prognosis, enhanced accessibility to high-quality care, and high standards of practitioner instruction. Nevertheless, these technologies are not without their inherent challenges and untapped opportunities, which span privacy protection, data safeguarding, and innovation in artificial intelligence. In summary, future research trajectories and potential advancements to circumvent these hurdles are also discussed, further augmenting the incorporation of metaverse wearables within healthcare infrastructures in the post-pandemic era.

Education on quality assurance and assessment in teaching quality of high school instructors

Research on language teaching quality has certainly stood out enough to be noticed as the momentum higher school teaching change proceeds to extend and develop. The way to further developing language teaching quality is to further develop teaching quality, and educator assessment is a significant instrument for doing as such. Accordingly, school administration necessitates the turn of events and refinement of a framework for assessing language teaching quality. Thus, hybrid learning technique for assessing the teaching quality of high school instructors should be created. We present an interesting model for assessing the quality of homeroom teaching involving artificial intelligence innovation in high schools, which depends on better hereditary calculations and neural networks. The fundamental thought is to utilize higher request factual elements (skewness, change, second and kurtosis), even vulnerability, Improved Independent Component Analysis (IICA), Holo-entropy based highlights to remove the underlying loads and limits of gathered data. The teaching quality assessment results were enhanced by further developing the neural network’s forecast accuracy and intermingling speed, bringing about a more down to earth plot for assessing high school language teaching quality. We have led simulation investigations and comparative analysis utilizing the Bi-directional Long Short Term Memory (Bi-LSTM) and Convolutional Neural Network (ConvNet/CNN) models. Then, an education quality assessment framework is laid out by hybrid optimizing model parameters which is Seagull Optimization Algorithm (SOA) and Red Colobuses Monkey (RCM).

Implications of AI innovation on economic growth: a panel data study

The application of artificial intelligence (AI) across firms and industries warrants a line of research focused on determining its overall effect on economic variables. As a general-purpose technology (GPT), for example, AI helps in the production, marketing, and customer acquisition of firms, increasing their productivity and consumer reach. Aside from these, other effects of AI include enhanced quality of services, improved work accuracy and efficiency, and increased customer satisfaction. Hence, this study aims to gauge the impact of AI on the economy, specifically on long-run economic growth. This study conjectures a positive relationship between AI and economic growth. To test this hypothesis, this study makes use of a panel dataset of countries from 1970 to 2019, and the number of AI patents as a measure of AI. A text search query is performed to distinguish AI patents from other types of innovations in a public database. Employing fixed effects and generalized method of moments (GMM) estimation, this paper finds a positive relationship between AI and economic growth, which is higher than the effect of the total population of patents on growth. Furthermore, other results indicate that AI’s influence on growth is more robust among advanced economies, and more evident towards the latter periods of the dataset.

How Can Radiomics Help the Clinical Management of Patients with Acute Ischemic Stroke?

Acute ischemic stroke (AIS) is the loss of neurological function due to a sudden reduction in cerebral blood flow and is a leading cause of disability and death worldwide. The field of radiological imaging has experienced growth in recent years, which could be boosted by the advent of artificial intelligence. One of the latest innovations in artificial intelligence is radiomics, which is based on the fact that a large amount of quantitative data can be extracted from radiological images, from which patterns can be identified and associated with specific pathologies. Since its inception, radiomics has been particularly associated with the field of oncology and has shown promising results in a wide range of clinical situations. The performance of radiomics in non-tumour pathologies has been increasingly explored in recent years, and the results continue to be promising. The aim of this review is to explore the potential applications of radiomics in AIS patients and to theorize how radiomics may change the paradigm for these patients in the coming years.