AI-based Technologies Research Articles

Can democracy at work deliver mutual gains in the digital economy?

Digital and AI-based technologies provide new tools to discipline workers, intensify monitoring, and deskill jobs. This article asks under what conditions these technologies can instead be used to generate mutual gains for employers, workers, and the broader public. Two developments are discussed, which provide opportunities for new coalitions in support of strengthened collective worker voice in technology adoption and deployment. First, the growing use of these tools in a range of service occupations provides opportunities for coalitions with customers or service users focusing on technologies' impact on service quality. Second, the importance of worker knowledge and skills as both inputs to and output of new AI-based technologies provides opportunities for a more collaborative approach to improving their accuracy and performance. To realize these opportunities for mutual gains, governments and labor unions first need to place institutional constraints on employers that strengthen bargaining rights and that protect minimum employment standards, workers' privacy and control over data, and job security.

Unlocking the potential of big data and AI in medicine: insights from biobanking.

Big data and artificial intelligence are key elements in the medical field as they are expected to improve accuracy and efficiency in diagnosis and treatment, particularly in identifying biomedically relevant patterns, facilitating progress towards individually tailored preventative and therapeutic interventions. These applications belong to current research practice that is data-intensive. While the combination of imaging, pathological, genomic, and clinical data is needed to train algorithms to realize the full potential of these technologies, biobanks often serve as crucial infrastructures for data-sharing and data flows. In this paper, we argue that the 'data turn' in the life sciences has increasingly re-structured major infrastructures, which often were created for biological samples and associated data, as predominantly data infrastructures. These have evolved and diversified over time in terms of tackling relevant issues such as harmonization and standardization, but also consent practices and risk assessment. In line with the datafication, an increased use of AI-based technologies marks the current developments at the forefront of the big data research in life science and medicine that engender new issues and concerns along with opportunities. At a time when secure health data environments, such as European Health Data Space, are in the making, we argue that such meta-infrastructures can benefit both from the experience and evolution of biobanking, but also the current state of affairs in AI in medicine, regarding good governance, the social aspects and practices, as well as critical thinking about data practices, which can contribute to trustworthiness of such meta-infrastructures.

Exploring the influence of ChatGPT on tourism behavior using the technology acceptance model

Purpose The present study’s aims are twofold: 1) to contribute to theory development by accounting for both personality and trust in the conceptualization of technology acceptance using the technology acceptance model (TAM) as the theoretical framework; and 2) to explore the influence of ChatGPT-integrated chatbots on tourism behavior. Design/methodology/approach The target population for this study was travelers who previously used technology (website/ app) to plan their holiday abroad. An online survey questionnaire created with Google Forms was distributed via a panel company (iPanel). A screening question was included to filter out respondents who have not previously used technological means to plan their holiday abroad. A panel company (iPanel) was hired to collect data from a convenience sample of 305 Israeli tourists who met the above criterion between August 22 and 27, 2023, and were at least 18. Findings A significant and positive relationship was observed between trust in ChaptGPT and perceived usefulness. Furthermore, a significant and positive association was observed between perceived ease of use and intentions to use ChatGPT-integrated chatbots to plan future holidays. Post hoc analyses suggest that perceived ease of use mediates the relationship between extraversion and trust, trust mediates the relationship between perceived ease of use and perceived usefulness and age moderates the relationship between perceived ease of use and behavioral intentions. Research limitations/implications Data was collected from a convenience sample of Israeli travelers. Hence, generalizations to other countries, nationalities and cultures should be treated carefully; the study is cross-sectional and thus represents respondents’ beliefs and behavioral intentions at a particular time; and the study is based on one of several theoretical frameworks that can be used to conceptualize behaviors associated with using AI by tourists. Practical implications The findings of the present study point to the importance of accounting for tourists’ personal factors, such as personality and age, in developing AI products in the tourism industry. chief executive officers and relevant shareholders would benefit from conducting market research to obtain insights into the factors that may enhance or hamper tourists’ adoption of AI-based technology for planning their holidays abroad. Originality/value Previous work falls short of accounting for personality traits and trust in a single model using the TAM framework. To the best of the authors’ knowledge, this is the first study empirically investigating tourism behavior related to ChatGPT based chatbots as a tool to plan future holidays abroad. Furthermore, the possible role of age as a moderating variable was overlooked in past research.

Revisiting the role of HR in the age of AI: bringing humans and machines closer together in the workplace.

The functions of human resource management (HRM) have changed radically in the past 20 years due to market and technological forces, becoming more cross-functional and data-driven. In the age of AI, the role of HRM professionals in organizations continues to evolve. Artificial intelligence (AI) is transforming many HRM functions and practices throughout organizations creating system and process efficiencies, performing advanced data analysis, and contributing to the value creation process of the organization. A growing body of evidence highlights the benefits AI brings to the field of HRM. Despite the increased interest in AI-HRM scholarship, focus on human-AI interaction at work and AI-based technologies for HRM is limited and fragmented. Moreover, the lack of human considerations in HRM tech design and deployment can hamper AI digital transformation efforts. This paper provides a contemporary and forward-looking perspective to the strategic and human-centric role HRM plays within organizations as AI becomes more integrated in the workplace. Spanning three distinct phases of AI-HRM integration (technocratic, integrated, and fully-embedded), it examines the technical, human, and ethical challenges at each phase and provides suggestions on how to overcome them using a human-centric approach. Our paper highlights the importance of the evolving role of HRM in the AI-driven organization and provides a roadmap on how to bring humans and machines closer together in the workplace.

Computer Vision-Aided Intelligent Monitoring of Coffee: Towards Sustainable Coffee Production

Earlier, researchers have employed computer vision-based automatic fruit detection to estimate coffee yield at the time of harvest, however, studies on the on-plant quantification of the coffee fruit are scarce. In this study, the latest version of the state-of-the-art algorithm YOLOv7 (You Only Look Once) was used for the first time. YOLOv7 was trained with 324 annotated images of fruit bearing coffee branches followed by its evaluation with 82 annotated images as validation data (supervised method) and then tested through raw images (unannotated) as test data. Meanwhile, the K-means models were trained which led to machine-generated color classes of coffee fruit for semi-supervised image annotation. Consequently, the developed model efficiently analyzed the test data with an mAP@.5 (mean average precision) of 0.89. Strikingly, our innovative semi-supervised method with an mAP@.5 of 0.77 for multi-class mode surpassed the supervised method which had mAP@.5 of only 0.60, leading to faster and more accurate annotation. While testing the yield estimation of the model in two plots, an average error of 3.78% was recorded between the predicted (pre-harvest) and ground truth (harvest) data for binary class mode. The average error for multi-class data was recorded as 3.87% for green, 3.445% for green-yellow, 5.09% for cherry-raisin, and 2.51% for dry fruits. This AI-based technology when integrated with other tools such as UAV would efficiently remotely monitor coffee field for informed decision about irrigation, fertilizer application and other measures of timely field management, hence advancing precision agriculture. Moreover, this machine learning intelligent model can be tailored for various other fruit farming.

Comprehensiveness, Accuracy, and Readability of Exercise Recommendations Provided by an AI-Based Chatbot: Mixed Methods Study.

Regular physical activity is critical for health and disease prevention. Yet, health care providers and patients face barriers to implement evidence-based lifestyle recommendations. The potential to augment care with the increased availability of artificial intelligence (AI) technologies is limitless; however, the suitability of AI-generated exercise recommendations has yet to be explored. The purpose of this study was to assess the comprehensiveness, accuracy, and readability of individualized exercise recommendations generated by a novel AI chatbot. A coding scheme was developed to score AI-generated exercise recommendations across ten categories informed by gold-standard exercise recommendations, including (1) health condition-specific benefits of exercise, (2) exercise preparticipation health screening, (3) frequency, (4) intensity, (5) time, (6) type, (7) volume, (8) progression, (9) special considerations, and (10) references to the primary literature. The AI chatbot was prompted to provide individualized exercise recommendations for 26 clinical populations using an open-source application programming interface. Two independent reviewers coded AI-generated content for each category and calculated comprehensiveness (%) and factual accuracy (%) on a scale of 0%-100%. Readability was assessed using the Flesch-Kincaid formula. Qualitative analysis identified and categorized themes from AI-generated output. AI-generated exercise recommendations were 41.2% (107/260) comprehensive and 90.7% (146/161) accurate, with the majority (8/15, 53%) of inaccuracy related to the need for exercise preparticipation medical clearance. Average readability level of AI-generated exercise recommendations was at the college level (mean 13.7, SD 1.7), with an average Flesch reading ease score of 31.1 (SD 7.7). Several recurring themes and observations of AI-generated output included concern for liability and safety, preference for aerobic exercise, and potential bias and direct discrimination against certain age-based populations and individuals with disabilities. There were notable gaps in the comprehensiveness, accuracy, and readability of AI-generated exercise recommendations. Exercise and health care professionals should be aware of these limitations when using and endorsing AI-based technologies as a tool to support lifestyle change involving exercise.

CMercury: Finding Returns in Precision Marketing

This case stems from a 2019 scenario at Caspar Technologies Pvt. Ltd., a rapidly growing digital marketing and automation service provider in India. CEO Jacob faced concern after presenting to a major client, Pragati, the founder of Villuvia.com, one of India’s top online jewellery brands. While email marketing had been a core strategy since the beginning, its return on investment had dwindled compared to social media advertising, exacerbated by a recent overhaul introducing a new digital marketing team. They excelled in social media but were new to email marketing software. Caspar Technologies unveiled cMercury, an AI-based technology to streamline email marketing campaigns. Although initial tests showed promise, it came at a five-fold cost compared to traditional email management software, which demanded extensive manual intervention. Jacob considered offering Villuvia a discount for the festive season but awaited Pragati’s decision. This would determine whether their budget would be allocated to email marketing, social media, or offline expansion. This case allows students to delve into email marketing intricacies, assess metrics and make quantitative evaluations. It also presents a platform for qualitative discussions on managerial dilemmas, like evaluating the digital team’s capabilities, prioritizing customer acquisition or retention and gauging the long-term viability of AI-powered email communication.

Revolutionizing Rehabilitation: Exploring the Integration of AI-Based Technology in Advancing Physiotherapy Practices for Enhanced Patient Outcomes

The field of physiotherapy has witnessed a transformative shift with the integration of Artificial Intelligence (AI)-based technologies. This research paper delves into the revolutionary impact of AI in advancing physiotherapy practices, focusing on enhancing patient outcomes. By exploring recent developments, applications, and challenges, this study aims to provide a comprehensive overview of the evolving landscape where technology intersects with rehabilitation. Through a mixed-methods approach, including a literature review, case studies, and interviews, the research analyzes the effectiveness of personalized treatment plans, real-time monitoring, and AI-driven decision support for physiotherapists. The findings reveal significant improvements in patient-reported outcomes, functional metrics, and treatment adherence, supported by positive physiotherapist experiences. Ethical considerations and challenges are discussed, emphasizing the potential of AI to revolutionize physiotherapy and shape the future of rehabilitative care.

Application of an artificial intelligence-based system in the diagnosis of breast ultrasound images obtained using a smartphone

BackgroundBreast ultrasound (US) is useful for dense breasts, and the introduction of artificial intelligence (AI)-assisted diagnoses of breast US images should be considered. However, the implementation of AI-based technologies in clinical practice is problematic because of the costs of introducing such approaches to hospital information systems (HISs) and the security risk of connecting HIS to the Internet to access AI services. To solve these problems, we developed a system that applies AI to the analysis of breast US images captured using a smartphone.MethodsTraining data were prepared using 115 images of benign lesions and 201 images of malignant lesions acquired at the Division of Breast Surgery, Gifu University Hospital. YOLOv3 (object detection models) was used to detect lesions on US images. A graphical user interface (GUI) was developed to predict an AI server. A smartphone application was also developed for capturing US images displayed on the HIS monitor with its camera and displaying the prediction results received from the AI server. The sensitivity and specificity of the prediction performed on the AI server and via the smartphone were calculated using 60 images spared from the training.ResultsThe established AI showed 100% sensitivity and 75% specificity for malignant lesions and took 0.2 s per prediction with the AI sever. Prediction using a smartphone required 2 s per prediction and showed 100% sensitivity and 97.5% specificity for malignant lesions.ConclusionsGood-quality predictions were obtained using the AI server. Moreover, the quality of the prediction via the smartphone was slightly better than that on the AI server, which can be safely and inexpensively introduced into HISs.

DIY Assistive Software: End-User Programming for Personalized Assistive Technology

Existing assistive technologies often fail to support the unique and personalized needs of blind and visually impaired (BVI) people. Thus, BVI people have become domain experts in customizing and 'hacking' assistive technology in order to creatively suit their needs. However, current assistive technologies are difficult to hack and alter at the individual level due to the skill and resources required. Due to these challenges, new approaches to supporting Do-It-Yourself (DIY) technology creation are needed to make applying the DIY concept to high-tech assistive technologies for BVI people more feasible. Through my dissertation, I aim to better support DIY assistive software design and development, so that BVI people can design, create, and customize assistive software for themselves and their communities. To accomplish this, I plan to address three primary research goals. First, through qualitative interviews, I present an understanding of why and how BVI people engage in assistive technology customization and adaptation, and the challenges and opportunities that arise from this practice. Second, I aim to investigate how end-user programming can be applied as an approach to DIY assistive technology creation, by designing, building, and evaluating a prototype end-user programming tool. Finally, I aim to understand and improve the real-world feasibility of end-user programming in this domain, by designing tools to improve the accessibility of debugging visual technologies. By addressing these primary research questions, my planned dissertation contributes to the HCI and accessibility community's understanding of DIY assistive software, accessible end-user programming, and accessible debugging techniques. By supporting BVI in creating and modifying their assistive software, this research aims to promote the democratization of technology creation and support BVI people in having greater control over AI-based technologies in their lives.

Situation Awareness in AI-Based Technologies and Multimodal Systems: Architectures, Challenges and Applications

Situation Awareness in AI-Based Technologies and Multimodal Systems: Architectures, Challenges and Applications

Recognizing and Predicting the Risk of Malnutrition in the Elderly Using Artificial Intelligence: A Systematic Review

The global population is aging at an alarming rate, endangering conventional care models that have always relied on in-person supervision. Elderly malnutrition has been found to be a serious health problem associated with health deterioration, which has several, immediate effects on everyday activities and standard of living. Deficiency in nutrients is a frequent and serious concern in older adults and might have an impact on the emergence of geriatric diseases. The two most obvious indicators of malnutrition in the elderly include unintentional weight loss and a lower value of the body mass index (BMI). However, non-symptomatic or unnoticed insufficiencies, such as lower than expected level of different crucial micronutrients, which are quite challenging to diagnose and are typically disregarded among older residents of their communities. Artificial intelligence (AI) permeates every aspect of human existence. The current systematic review looks at how AI-based technology is currently being used and how it affects elderly malnutrition. Using the following keywords, computerized literature searches of many reliable data bases were used to compile relevant published articles: malnutrition, artificial intelligence (AI), machine learning (ML), elderly nutrition, risk factors of malnutrition among the elderly, chronic diseases, elderly. Prospective studies with original data were chosen, and their significant findings were integrated into the analysis of the state of malnutrition at the moment. In conclusion, it is challenging to deploy AI-based food and nutrient intake monitoring data because no single program is suitable for all international cuisines and eating customs. Geographic variations in the dietary habits of the population make it challenging to compile the necessary data sets for deep learning. Furthermore, even within a same region, hospital menus differ from patient to patient. It is suggested that meals served in hospitals under the same management be standardized to facilitate the procedure.

Integrating Artificial Intelligence into Scholarly Communications for Enhanced Human Cognitive Abilities: The War for Philosophy?

Este artículo explora la integración de la IA en la comunicación académica para mejorar las capacidades cognitivas humanas. La concepción de la comunicación hombre-máquina (CMM), que considera las tecnologías basadas en la IA no como objetos interactivos, sino como sujetos comunicativos, plantea cuestiones más filosóficas en la comunicación académica. Es un hecho conocido que existe una mayor interacción entre los humanos y las máquinas, especialmente consolidada por la pandemia COVID-19, que intensificó el desarrollo del Sistema de Aprendizaje Adaptativo Individual, por lo que necesariamente se requieren aportaciones de las IN para fortalecer la IA. Esto posicionó a la universidad en la yuxtaposición de asegurar la producción de individuos altamente talentosos que puedan continuar pensando más allá de lo pensable inteligentemente y permitir a los expertos en IA continuar construyendo algoritmos que puedan llevar el desarrollo de la tecnología al siguiente nivel. Para llevar a cabo tal tarea, la producción investigadora es primordial para conectar la universidad y la industria editorial. Este artículo examina de forma crítica componentes importantes e intenta hacer justicia a la emanación de preguntas críticas de los investigadores (NI), alimentando así el desarrollo de la tecnología (IA).

Health literacy in ChatGPT: exploring the potential of the use of artificial intelligence to produce academic text.

The aim of this study was to identify and analyze the main constituent elements of text generated by ChatGPT in response to questions on an emerging topic in the academic literature in Portuguese - health literacy - and discuss how the evidence produced can contribute to improving our understanding of the limits and challenges of using artificial intelligence (AI) in academic writing. We conducted an exploratory descriptive study based on responses to five consecutive questions in Portuguese and English with increasing levels of complexity put to ChatGPT. Our findings reveal the potential of the use of widely available, unrestricted access AI-based technologies like ChatGPT for academic writing. Featuring a simple and intuitive interface, the tool generated structured and coherent text using natural-like language. Considering that academic productivism is associated with a growing trend in professional misconduct, especially plagiarism, there is a need too take a careful look at academic writing and scientific knowledge dissemination processes mediated by AI technologies.

Potentials of ChatGPT in Computer Programming: Insights from Programming Instructors

Potentials of ChatGPT in Computer Programming: Insights from Programming Instructors

The Impact of AI-based Technology such as Chat-GPT on Second Language (L2) Learners’ Academic Writing

This study examines how AI, more especially ChatGPT, can help second language (L2) and non-native scholar (NNS) learners with academic writing. To comprehend its acceptance, it uses the diffusion of innovation (DOI) theory. The study looks into how ChatGPT affects NNSs' confidence and writing abilities. The study supports a balanced strategy in incorporating ChatGPT to help NNSs in academics while noting problems such as plagiarism.

The use of AI in physical therapy

Following the invention of the steam engine, human social and economic systems saw unprecedented growth and development. More recently, artificially intelligent machines have emerged, enabling human cognitive capacity to recognize previously hidden patterns within massive data sets. These changes are having an impact on every facet of society, including but not limited to our legal, moral, educational, health, and social systems. In comparison to the steam engine-driven mechanical revolution, these changes may have a greater lasting effect on human progress. Physicians will soon have to deal with massive information networks, which will necessitate the use of artificial intelligence (AI) in patient care. This paper aims to describe the potential impacts and range of applications of AI-based technologies on the field of physiotherapy, as well as the resulting changes that physiotherapy education will need to undergo in order to produce graduates qualified to work in a 21st-century healthcare system.

Primary care clinicians should proactively take up latest AI-based technology: No

Primary care clinicians should proactively take up latest AI-based technology: No

A literacia em saúde no ChatGPT: explorando o potencial de uso de inteligência artificial para a elaboração de textos acadêmicos

Abstract The aim of this study was to identify and analyze the main constituent elements of text generated by ChatGPT in response to questions on an emerging topic in the academic literature in Portuguese - health literacy - and discuss how the evidence produced can contribute to improving our understanding of the limits and challenges of using artificial intelligence (AI) in academic writing. We conducted an exploratory descriptive study based on responses to five consecutive questions in Portuguese and English with increasing levels of complexity put to ChatGPT. Our findings reveal the potential of the use of widely available, unrestricted access AI-based technologies like ChatGPT for academic writing. Featuring a simple and intuitive interface, the tool generated structured and coherent text using natural-like language. Considering that academic productivism is associated with a growing trend in professional misconduct, especially plagiarism, there is a need too take a careful look at academic writing and scientific knowledge dissemination processes mediated by AI technologies.

A review on the effect of integrating AI-based technology into flipped learning

This mini-review mainly examines the effects of integrating the AI-based technology into flipped learning on students’ motivation and performance. As the research topic supported by artificial intelligence in the classroom is a relatively frontier topic, further exploration is needed. Exploring the integration of AI-based technology into flipped learning holds great potential for enhancing personalized learning experiences, automating tasks, and improving learning outcomes. This review concluded findings from a study between 2019 and 2023, articles were evaluated and screened using keyword flipped classroom and artificial technology, and duplicate papers were removed from the study. Finally, 12 empirical papers are selected according to the research focus. This paper summarizes the research methods, research fields, and data analysis. The results of this study show that in terms of technical challenges, the alignment of teaching objectives with AI techniques, ethical considerations, and human-computer interaction are important considerations for practitioners and researchers in integrating AI into the field of flipped learning. Addressing these limitations is critical to ensuring the successful implementation and ethical use of AI technologies. This review provides a foundation for future research to explore effective implementation strategies, evaluate the impact of AI integration on learning outcomes, and develop guidelines for the ethical and responsible use of AI in flipped learning environments.