Work In Artificial Intelligence Research Articles

Patients' Attitudes Toward the Use of Artificial Intelligence as a Diagnostic Tool in Radiology in Saudi Arabia: Cross-Sectional Study.

Artificial intelligence (AI) is widely used in various medical fields, including diagnostic radiology as a tool for greater efficiency, precision, and accuracy. The integration of AI as a radiological diagnostic tool has the potential to mitigate delays in diagnosis, which could, in turn, impact patients' prognosis and treatment outcomes. The literature shows conflicting results regarding patients' attitudes to AI as a diagnostic tool. To the best of our knowledge, no similar study has been conducted in Saudi Arabia. The objectives of this study are to examine patients' attitudes toward the use of AI as a tool in diagnostic radiology at King Khalid University Hospital, Saudi Arabia. Additionally, we sought to explore potential associations between patients' attitudes and various sociodemographic factors. This descriptive-analytical cross-sectional study was conducted in a tertiary care hospital. Data were collected from patients scheduled for radiological imaging through a validated self-administered questionnaire. The main outcome was to measure patients' attitudes to the use of AI in radiology by calculating mean scores of 5 factors, distrust and accountability (factor 1), procedural knowledge (factor 2), personal interaction and communication (factor 3), efficiency (factor 4), and methods of providing information to patients (factor 5). Data were analyzed using the student t test, one-way analysis of variance followed by post hoc and multivariable analysis. A total of 382 participants (n=273, 71.5% women and n=109, 28.5% men) completed the surveys and were included in the analysis. The mean age of the respondents was 39.51 (SD 13.26) years. Participants favored physicians over AI for procedural knowledge, personal interaction, and being informed. However, the participants demonstrated a neutral attitude for distrust and accountability and for efficiency. Marital status was found to be associated with distrust and accountability, procedural knowledge, and personal interaction. Associations were also found between self-reported health status and being informed and between the field of specialization and distrust and accountability. Patients were keen to understand the work of AI in radiology but favored personal interaction with a radiologist. Patients were impartial toward AI replacing radiologists and the efficiency of AI, which should be a consideration in future policy development and integration. Future research involving multicenter studies in different regions of Saudi Arabia is required.

Communicating About and With Artificial Intelligence Applications

This session provided attendees with an overview of the elements of artificial intelligence (AI) that medical communication professionals can use in their decision-making when communicating about and with AI applications. Knowing how to determine if an AI application is reliable and secure guides the professional in their assessment of applications they write about and their choice of applications they use in their practice. In turn, this ability to assess AI applications underpins professionals’ abilities to identify and apply best practices for developing and writing about AI. Taken together, this understanding of how AI works, how applications are developed, and how to identify and ethically apply best practices will guide professionals in their communication about and with AI applications. Editor's Note Developments in artificial intelligence (AI) will continue to be of critical importance to medical communicators for the foreseeable future. Accordingly, AMWA Journal expects to continue to feature AI-related articles in upcoming issues. Given how rapidly advancements are occurring in AI as they relate to medical communication, we are striving to be as timely as possible in bringing relevant articles to you. In this spirit, we are supplementing the Summer 2024 Digital Revolution theme issue with a timely article titled ‘Communicating About and With Artificial Intelligence Applications’ by J. Kelly Byram, based on a presentation made by the author at the most recent AMWA Medical Writing & Communication Conference.

Issues and Possibilities in Regulating Artificial Intelligence (AI) Related To Copyright in Indonesia

This paper discusses the challenges and opportunities of regulating artificial intelligence (AI) and its implications for copyright, with a focus on the Indonesian context. Internationally, the United States and the European Union have begun to develop AI regulations, although they are not yet fully comprehensive. Indonesia, while lacking detailed regulations, has developed a National Strategy for Artificial Intelligence 2020-2045 to guide the development of this technology. Key challenges include regulatory ambiguity, insufficient protection of personal data, and ethical issues. Copyright in Indonesia, under Law No. 28 of 2014, provides automatic protection for works that are original and embodied in tangible form. In the context of works created by AI, there are two views: first, AI works cannot be copyrighted because they lack personal characteristics and human creative process; second, AI works can be copyrighted because of their ability to create complex works and as an incentive for AI users. This article concludes that the government needs to rewrite copyright law to accommodate AI works, given their potential as a significant source of innovative and creative works.

Special Issue introduction: AI and media ecology

This short work serves an introduction for Explorations in Media Ecology’s first special issue on artificial intelligence (AI). AI work is nothing new in the field, and the article begins by overviewing some of the conversations from the media ecology lead voices on AI, including Postman, Ong and McLuhan. After, the article attempts to guide readers to an understanding of AI through a media ecological lens, concluding that generative AI (GenAI) tools are media. The article ends by giving a brief overview of the articles that have been selected for the special issue.

Application of Artificial Intelligence in Justice: Current Trends and Future Prospects

Abstract Background Modern digital technologies are actively being introduced into spheres of society, including justice. Electronic justice at the present stage of development of information technology has moved to a qualitatively new level. It has become obvious that artificial intelligence (AI) is our present, not our future. Objective This article analyzes foreign experience in the implementation and use of AI in justice, identifies possible areas for the use of AI in justice, examines the legal personality of AI and the scope of its competence, and evaluates the prospects for the development of AI in justice. Results The authors identified the following areas of use of AI in justice: organizational sphere of activity of courts; assessment of evidence and establishment of legally significant circumstances; consideration of the case by AI. Also in this study, the stages of introducing AI into justice: short-term perspective, medium-term perspective (5–10 years), long-term perspective. It is noted that at present the work of AI is possible only in conjunction with a human judge. Conclusions The authors noted the feasibility of using AI in resolving issues that require processing a large amount of information and documents in electronic form. This will ensure procedural savings and reduction of time for consideration of disputes on the merits through speed and error-free calculations.

Assessing the Copyright Infringement Risk of Generative AI Created Works

The rapid evolution of the Internet and big data technology has ushered in significant advancements in data production and utilization. However, this progress has brought to the forefront the intricate issue of copyright protection. This is particularly evident with the widespread adoption of generative artificial intelligence (AI), where concerns regarding data source protection, data processing standards, and the boundaries of data application have garnered considerable attention from both academia and industry. Big data mining techniques coupled with generative AI algorithms offer robust support for data collection and processing, but they also introduce risks of data infringement and pose challenges regarding algorithmic compliance and ownership rights over generated works. Addressing these issues is imperative. This paper recommends enhancing legal compliance standards throughout each stage of the generative AI process, recalibrating the scope of acceptable copyright use, and establishing a regulatory framework for generative AI works. These measures are crucial for fostering the sustainable and orderly advancement of the generative AI industry.

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

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

Artificial Intelligence Methods in Natural Language Processing: A Comprehensive Review

The rapid evolution of Artificial Intelligence (AI) since its inception in the mid-20th century has significantly influenced the field of Natural Language Processing (NLP), transforming it from a rule-based system to a dynamic and adaptive model capable of understanding the complexities of human language. This paper aims to offer a comprehensive review of the various applications and methodologies of AI in NLP, serving as a detailed guide for future research and practical applications. In the early sections, the paper elucidates the indispensable role of AI in NLP, highlighting its transition from symbolic reasoning to a focus on machine learning and deep learning, and its extensive applications in sectors such as healthcare, transportation, and finance. It emphasizes the symbiotic relationship between AI and NLP, facilitated by platforms like AllenNLP, which aid in the development of advanced language understanding models. Further, the paper explores specific AI techniques employed in NLP, including machine learning, Naive Bayes, and Support Vector Machines, and identifies pressing challenges and avenues for future research. It delves into the applications of AI in NLP, showcasing its transformative potential in tasks such as machine translation, facilitated by deep learning methods, and the development of chatbots and virtual assistants that have revolutionized human-technology interaction. The paper also highlights other fields impacted by AI techniques, including text summarization, sentiment analysis, and named entity recognition, emphasizing the efficiency and accuracy brought about by the integration of AI in these areas. In conclusion, the paper summarizes the remarkable advancements and persistent challenges in NLP, such as language ambiguity and contextual understanding, and underscores the need for diverse and representative labeled data for training. Looking forward, it identifies promising research avenues including Explainable AI, Few-shot and Zero-shot Learning, and the integration of NLP with other data modalities, aiming for a holistic understanding of multimodal data. The paper calls for enhanced robustness and security in NLP systems, especially in sensitive applications like content moderation and fake news detection, to foster trust and reliability in AI technologies. It advocates for continual learning in NLP models to adapt over time without losing previously acquired knowledge, paving the way for a future where AI and NLP work synergistically to understand and generate human language more effectively and efficiently.

Artificial intelligence for family medicine research in Canada: current state and future directions: Report of the CFPC AI Working Group.

To understand the current landscape of artificial intelligence (AI) for family medicine (FM) research in Canada, identify how the College of Family Physicians of Canada (CFPC) could support near-term positive progress in this field, and strengthen the community working in this field. Members of a scientific planning committee provided guidance alongside members of a CFPC staff advisory committee, led by the CFPC-AMS TechForward Fellow and including CFPC, FM, and AI leaders. This initiative included 2 projects. First, an environmental scan of published and gray literature on AI for FM produced between 2018 and 2022 was completed. Second, an invitational round table held in April 2022 brought together AI and FM experts and leaders to discuss priorities and to create a strategy for the future. The environmental scan identified research related to 5 major domains of application in FM (preventive care and risk profiling, physician decision support, operational efficiencies, patient self-management, and population health). Although there had been little testing or evaluation of AI-based tools in practice settings, progress since previous reviews has been made in engaging stakeholders to identify key considerations about AI for FM and opportunities in the field. The round-table discussions further emphasized barriers to and facilitators of high-quality research; they also indicated that while there is immense potential for AI to benefit FM practice, the current research trajectory needs to change, and greater support is needed to achieve these expected benefits and to avoid harm. Ten candidate action items that the CFPC could adopt to support near-term positive progress in the field were identified, some of which an AI working group has begun pursuing. Candidate action items are roughly divided into avenues where the CFPC is well-suited to take a leadership role in tackling priority issues in AI for FM research and specific activities or initiatives the CFPC could complete. Strong FM leadership is needed to advance AI research that will contribute to positive transformation in FM.

Balancing Innovation and Regulation: A Comprehensive Analysis and Neural Network Approach to AI Copyright Challenges

This article explores emerging issues surrounding artificial intelligence (AI) and copyright through a two-pronged approach. First, it provides an extensive literature review analyzing government and industry strategies for addressing AI copyright concerns and evaluates their rationality. Second, it details experiments conducted using neural networks to examine relevant information and investigate image copyright challenges, assessing mainstream large language models’ efficacy in handling copyright matters.
 The literature review explores AI copyright perspectives of the United Kingdom, China, the European Union, and the United States. It finds that countries emphasize balanced regulation and innovation (UK), ethical content creation (China), regulating high-risk applications (EU), or principles like non-discrimination and privacy (US). However, comprehensive governance frameworks are needed to navigate AI’s ethical, social, and legal intricacies.
 The experimental portion trains a convolutional neural network on a dataset of 41 infringing and non-infringing image sets to identify copyright infringement. While achieving over 80% accuracy, enhancements through expanded training data, segmentation, and multi-domain detection could improve generalization. The paper concludes with an analysis advocating copyright adaptation for AI creations, measured protections for standalone AI works, and constructive policies from interdisciplinary dialogue.

The nexus between Artificial Intelligence and Sustainable Development Goals: A review

This article focuses on the link between Artificial Intelligence and Sustainable Development Goals and, more precisely, how the former can be applied to achieve the latter. The solution to SDG-related issues may be one goal of AI applicability. There have been suggestions that the current status of artificial intelligence (AI) might serve hundreds of millions of people in both rich and developing countries by helping to resolve problems related to all 17 UN Sustainable Development Goals. Examples include helping those who are blind or visually handicapped navigate their environment, detecting victims of sexual exploitation online, supporting relief operations in the event of a disaster, helping to detect diabetes early, and using image scanning to diagnose skin cancer. These are but a few examples of how AI works and how AI innovation can be advantageous. It also reviews how Artificial Intelligence can be applied to protect the environment, food production, the energy industry, and healthcare development.

Transforming neonatal care with artificial intelligence: challenges, ethical consideration, and opportunities.

Artificial intelligence (AI) offers tremendous potential to transform neonatology through improved diagnostics, personalized treatments, and earlier prevention of complications. However, there are many challenges to address before AI is ready for clinical practice. This review defines key AI concepts and discusses ethical considerations and implicit biases associated with AI. Next we will review literature examples of AI already being explored in neonatology research and we will suggest future potentials for AI work. Examples discussed in this article include predicting outcomes such as sepsis, optimizing oxygen therapy, and image analysis to detect brain injury and retinopathy of prematurity. Realizing AI's potential necessitates collaboration between diverse stakeholders across the entire process of incorporating AI tools in the NICU to address testability, usability, bias, and transparency. With multi-center and multi-disciplinary collaboration, AI holds tremendous potential to transform the future of neonatology.

Artificial intelligence in neuro-oncology.

Artificial intelligence (AI) describes the application of computer algorithms to the solution of problems that have traditionally required human intelligence. Although formal work in AI has been slowly advancing for almost 70 years, developments in the last decade, and particularly in the last year, have led to an explosion of AI applications in multiple fields. Neuro-oncology has not escaped this trend. Given the expected integration of AI-based methods to neuro-oncology practice over the coming years, we set to provide an overview of existing technologies as they are applied to the neuropathology and neuroradiology of brain tumors. We highlight current benefits and limitations of these technologies and offer recommendations on how to appraise novel AI-tools as they undergo consideration for integration into clinical workflows.

The Impact of Artificial Intelligence on Hospitality Employees’ Work Outcomes

The aim of this systematic literature review is to analyze the existing literature on the impact of artificial intelligence (AI) on employee work outcomes in the hospitality industry context. This paper systematically reviews the association between AI and employee work outcomes through an extensive literature review of published peer-reviewed English articles. Eighteen articles have been found in 12 journals and analyzed through deductive approach. The findings were synthesized into three major themes: enablers or inhibitors of AI adoption, the type of AI-related technique, outcomes of AI adoption. Well-being, turnover intention, and job engagement were identified as the most significant and most commonly studied outcomes of AI adoption.

Artificial intelligence and socioeconomic perspective in Indonesia

Artificial Intelligence (AI) has begun to penetrate various social and economic activities in Indonesia. During the pandemic, social distancing activities were able to accelerate the application of AI, and promptly became a safety valve and economic driver in various sector. However, attention to AI implementation opens up space for intensive discourse. AI as a technological element also has social and economic impacts, especially social, economic and political inequality. In the midst of obtaining positive economic benefits, innovation excellence and efficiency, AI in Indonesia still faces problems with agricultural workforce (38.7 million people), ethical and legal aspects, and organizational culture. Solutions from a social and economic perspective anticipating AI include (i) affirmative policies aimed at empowering and increasing the productivity of agricultural workers, (ii) collaboration between IT experts and business practitioners, social science experts and legal practitioners to ensure AI works within a safety and legality framework; and (iii) building an organizational culture in business and public sector management to run a business that meets sustainability principles.JEL Classification J00; K20; O38

Artificial Intelligence and Philosophy of Humanism in Auditor Perceptions

This study aims to interpret the humanistic thinking of Chinese philosopher Confucius on the activity of integrating Artificial Intelligence (AI) into the process of auditing financial statements. The qualitative-interpretive method was used for research purposes through in-depth interview techniques which were addressed to informants from audit firms that had used AI. The validity of the information was tested using triangulation of data sources from different audit firm informants. The main findings show that as humans who have cognitive, moral and ethical abilities, auditors can collaborate with AI without worrying that the existence of this profession will be completely replaced by AI. However, excessive integration and tend to rely on auditors should be aware of so that high-tech assisted audit objectives such as AI work in harmony without eliminating the auditor's humanism such as skepticism and professional judgment that AI does not have. Social and ethical issues are challenges in the use of AI and solutions will continue to be sought. Therefore, the auditor always maintains critical thinking, especially on the elements contained in AI technology, namely system predictability, dependability, reliability, robustness, understanding, explanation of intent, usability, and user familiarity with AI technology.

Developing Human Skills in the Era of Artificial Intelligence: Challenges and Opportunities for Education and Training

The rapid advancement and adoption of artificial intelligence (AI) presents significant challenges and opportunities for human skills in the 21st century. AI systems can do many things that people usually do, like looking at data, making decisions, and understanding language. However, AI also creates new demands for human skills that complement and enhance the capabilities of AI, such as creativity, critical thinking, communication, and collaboration. This paper conducts a comprehensive review of the literature pertaining to the impact of artificial intelligence on human abilities and its implications for education and training. It discusses the key human-centric skills that are important for working with and alongside AI and the current and future challenges of developing and assessing these skills. It also looks at how new pedagogical approaches and technologies can help humans and AI work together and learn. The paper concludes with some recommendations for policy makers, educators, and learners to foster human-centric skills in the era of AI.

Artificial intelligence (AI) awareness and work withdrawal: evaluating chained mediation through negative work-related rumination and emotional exhaustion

PurposeThis study aims to investigate whether and how artificial intelligence (AI) awareness affects work withdrawal.Design/methodology/approachThis survey garners participation from a total of 305 hotel employees in China. The proposed hypotheses are examined using Hayes’s PROCESS macro.FindingsThe results indicate that AI awareness could positively affect work withdrawal. Negative work-related rumination and emotional exhaustion respectively mediate this relationship. Furthermore, negative work-related rumination and emotional exhaustion act as chain mediators between AI awareness and work withdrawal.Practical implicationsGiven the growing adoption of AI technology in the hospitality industry, it is imperative that managers intensify their scrutiny of the psychological changes experienced by frontline service employees and allocate more resources to mitigating the impact of AI on their work withdrawal.Originality/valueThis study contributes to the burgeoning literature on AI by elucidating the chain mediating roles of negative work-related rumination and emotional exhaustion. It also makes a significant forward in examining mediating mechanisms, notably the chain-mediated mechanism, through which AI awareness impacts employee outcomes.

AI and the Accounting Profession: Views from Industry and Academia

ABSTRACT Anecdotal and empirical evidence indicates that the growing adoption of artificial intelligence (AI) within accounting firms and accounting departments leads to improvements in efficiency, a gradual increase in the share of AI workers, and a decrease in junior accounting employees. If this trend continues, would it signal the beginning of an era of diminishing demand for new accounting professionals and a shift in the required skill set of new accounting employees? The aim of the workshop, which, by happenstance, occurred the same week that OpenAI introduced ChatGPT, was to bring together Accounting Information Systems researchers and representatives from leading accounting firms for a conversation on the implications of AI for the accounting profession and related research opportunities. Although the panelists at the time had no way of knowing the capabilities of generative AI models like ChatGPT, their main message was timely and appropriate: Accountants with AI will replace accountants.

A Study of Human–AI Symbiosis for Creative Work: Recent Developments and Future Directions in Deep Learning

Recent advances in Artificial Intelligence (AI), particularly deep learning, are having an enormous impact on our society today. Record numbers of jobs previously held by people have been automated, from manufacturing to transportation to customer services. The concerns of AI replacing humans by taking over people's jobs need to be urgently addressed. This article investigates some promising different directions of AI development: Instead of using AI to replace people, we should use AI to team up with people so that both can work better and smarter. Human–AI symbiosis refers to people and AI working together to jointly solve problems and perform specific tasks. The recent developments in deep learning models and frameworks have significantly improved the efficiency and performance of human and AI collaborations. In this article, some research work on human–AI collaborative environments has been extensively studied and analyzed to reveal the progress in this field. Although the teaming of humans and machines includes many complex tasks, the development has been very promising. One of the main goals in this field is to develop additional capabilities in machines capable of being successful teammates with a human partner. The correctness of the outcomes is often determined by the underlying technology and how performance and human satisfaction are measured through the collaborative nature of the system. We conclude that the teaming of humans and AI, particularly deep learning, has the advantage of combining the power of AI with the human domain expertise to improve performance and create value. Human–AI symbiosis could be a promising future direction for AI's continuing integration into the world.