Search

This editorial introduces the special issue "The Interplay Between Artificial Intelligence, Production Systems, and Operations Management Resilience.' We selected twelve papers, encompassing many angles that illuminate the advances and challenges dealing with artificial intelligence tools and approaches in the production systems and operations management resilience domains. This editorial presents the papers with a smart view, highlighting the essentials of each article, such as full paper title, background, theory/literature scope, methodology design/analysis approach, and the main findings/contributions. Finally, the conclusions, future pathways, and research directions are presented. [ABSTRACT FROM AUTHOR]

This review analyzes the most influential artificial intelligence (AI) studies in health and life sciences from the past three years, delineating the evolving role of AI in these fields. We identified and analyzed the top 50 cited articles on AI in biomedicine, revealing significant trends and thematic categorizations, including Drug Development, Real-World Clinical Implementation, and Ethical and Regulatory Aspects, among others. Our findings highlight a predominant focus on AIs application in clinical settings, particularly in diagnostics, telemedicine, and medical education, accelerated by the COVID-19 pandemic. The emergence of AlphaFold marked a pivotal moment in protein structure prediction, catalyzing a cascade of related research and signifying a broader shift towards AI-driven approaches in biological research. The review underscores AIs pivotal role in disease subtyping and patient stratification, facilitating a transition towards more personalized medicine strategies. Furthermore, it illustrates AIs impact on biology, particularly in parsing complex genomic and proteomic data, enhancing our capabilities to disentangle complex, interconnected molecular processes. As AI continues to permeate the health and life sciences, balancing its rapid technological advancements with ethical stewardship and regulatory vigilance will be crucial for its sustainable and effective integration into healthcare and research.

Manuscript writing support services using AI technology have become increasingly available in recent years. In keeping with this trend, we need to sort out issues related to authorship in academic writing. Authorship is attached to the contribution of researchers who report innovative research, the originality of which forms the core of their identity. The most important originality is demonstrated in the discussion of study findings. In the discussion section of this paper, we argue that if a researcher uses AI-based manuscript writing support to draft the discussion section, this does not necessarily diminish the researcher’s originality. Rather, AI support may allow the researcher to perform creative work in a more refined fashion. Presumably, selecting which AI support to use or evaluating and properly adjusting AI would still remain an important aspect of research for researchers. It is thus reasonable to view a researcher as a cooperative existence realized through a network of cooperative work that includes the use of AI. Discussions on this topic will be scientifically and socially important as AI technology advances in the future.

The irruption of artificial intelligence (AI) in all areas of our lives is a reality to which the university, as an institution of higher education, must respond prudently, but also with no hesitation. This paper discusses the potential that resources based on AI presents as potential reviewers of scientific articles in a hypothetical peer review of already published articles. Using different models (GPT-3.5 and GPT-4) and platforms (ChatPDF and Bing), we obtained three full reviews, both qualitative and quantitative, for each of the five articles examined, thus being able to delineate and contrast the results of all of them in terms of the human reviews that these same articles received at the time. The evidence found highlights the extent to which we can and should rely on generative language models to support our decisions as qualified experts in our field. Furthermore, the results also corroborate the hallucinations inherent in these models while pointing out one of their current major shortcomings: the context window limit. On the other hand, the study also points out the inherent benefits of a model that is in a clear expansion phase, providing a detailed view of the potential and limitations that these models offer as possible assistants to the review of scientific articles, a key process in the communication and dissemination of academic research. [ABSTRACT FROM AUTHOR]

This review analyzes the most influential artificial intelligence (AI) studies in health and life sciences from the past three years, delineating the evolving role of AI in these fields. We identified and analyzed the top 50 cited articles on AI in biomedicine, revealing significant trends and thematic categorizations, including Drug Development, Real-World Clinical Implementation, and Ethical and Regulatory Aspects, among others. Our findings highlight a predominant focus on AIs application in clinical settings, particularly in diagnostics, telemedicine, and medical education, accelerated by the COVID-19 pandemic. The emergence of AlphaFold marked a pivotal moment in protein structure prediction, catalyzing a cascade of related research and signifying a broader shift towards AI-driven approaches in biological research. The review underscores AIs pivotal role in disease subtyping and patient stratification, facilitating a transition towards more personalized medicine strategies. Furthermore, it illustrates AIs impact on biology, particularly in parsing complex genomic and proteomic data, enhancing our capabilities to disentangle complex, interconnected molecular processes. As AI continues to permeate the health and life sciences, balancing its rapid technological advancements with ethical stewardship and regulatory vigilance will be crucial for its sustainable and effective integration into healthcare and research. [ABSTRACT FROM AUTHOR]

Manuscript writing support services using AI technology have become increasingly available in recent years. In keeping with this trend, we need to sort out issues related to authorship in academic writing. Authorship is attached to the contribution of researchers who report innovative research, the originality of which forms the core of their identity. The most important originality is demonstrated in the discussion of study findings. In the discussion section of this paper, we argue that if a researcher uses AI-based manuscript writing support to draft the discussion section, this does not necessarily diminish the researcher's originality. Rather, AI support may allow the researcher to perform creative work in a more refined fashion. Presumably, selecting which AI support to use or evaluating and properly adjusting AI would still remain an important aspect of research for researchers. It is thus reasonable to view a researcher as a cooperative existence realized through a network of cooperative work that includes the use of AI. Discussions on this topic will be scientifically and socially important as AI technology advances in the future. [ABSTRACT FROM AUTHOR]

Artificial intelligence is more present than ever, both in our society in general and in science. At the center of this development has been the concept of deep learning, the use of artificial neural networks that are many layers deep and can often reproduce human‐like behavior much better than other machine‐learning techniques. The articles in this collection are some recent examples of its application for X‐ray photon science and crystallography that have been published in Journal of Applied Crystallography. [ABSTRACT FROM AUTHOR]

This study employs a bibliometric approach to analyze the landscape of artificial intelligence (AI) components used in performance measurement. As organizations increasingly leverage AI for optimizing processes and decision-making, understanding the trends in AI components becomes imperative. The identified AI components are classified based on their roles in enhancing performance measurement, offering insights into the prevalent methodologies and emerging technologies. The bibliometric analysis encompasses a comprehensive review of scholarly articles, conference papers, and patents, systematically exploring the evolving field. In this research, the methodology involves data extraction from reputable academic databases and patent repositories, followed by applying bibliometric techniques to quantify and visualize key aspects. The findings of this study contribute to the existing knowledge by mapping the intellectual structure of AI components for performance measurement. [ABSTRACT FROM AUTHOR]

ChatGPT is a generative language model tool launched by OpenAI on November 30, 2022, enabling the public to converse with a machine on a broad range of topics. In January 2023, ChatGPT reached over 100 million users, making it the fastest-growing consumer application to date. This interview with ChatGPT is part 2 of a larger interview with ChatGPT. It provides a snapshot of the current capabilities of ChatGPT and illustrates the vast potential for medical education, research, and practice but also hints at current problems and limitations. In this conversation with Gunther Eysenbach, the founder and publisher of JMIR Publications, ChatGPT generated some ideas on how to use chatbots in medical education. It also illustrated its capabilities to generate a virtual patient simulation and quizzes for medical students; critiqued a simulated doctor-patient communication and attempts to summarize a research article (which turned out to be fabricated); commented on methods to detect machine-generated text to ensure academic integrity; generated a curriculum for health professionals to learn about artificial intelligence (AI); and helped to draft a call for papers for a new theme issue to be launched in JMIR Medical Education on ChatGPT. The conversation also highlighted the importance of proper "prompting." Although the language generator does make occasional mistakes, it admits these when challenged. The well-known disturbing tendency of large language models to hallucinate became evident when ChatGPT fabricated references. The interview provides a glimpse into the capabilities and limitations of ChatGPT and the future of AI-supported medical education. Due to the impact of this new technology on medical education, JMIR Medical Education is launching a call for papers for a new e-collection and theme issue. The initial draft of the call for papers was entirely machine generated by ChatGPT, but will be edited by the human guest editors of the theme issue. [ABSTRACT FROM AUTHOR]

In recent years, the healthcare industry has faced numerous challenges in ensuring efficient and reliable supply chain management. The emergence of novel supply chain-based frameworks has presented promising solutions to these challenges. This survey paper aims to explore and analyze the latest advancements in supply chain frameworks specifically tailored for the healthcare industry. We review the existing literature, identify key trends, and highlight the potential impact of these frameworks on improving patient care, optimizing inventory management, enhancing operational efficiency, and reducing costs. Additionally, we discuss the challenges and opportunities associated with the implementation of these frameworks and suggest future research directions. [ABSTRACT FROM AUTHOR]

This paper investigates the complex interplay between generative artificial intelligence (AI) and human intellect in academic writing and publishing. It examines the 'organic versus synthetic' paradox, emphasizing the implications of using generative AI tools in educational and academic integrity contexts. The paper critiques the prevalent 'publish or perish' culture in academia, highlighting the need for systemic reevaluation due to generative AI's emerging role in academic writing and reporting. It delves into the legal and ethical challenges of authorship and ownership, especially in relation to copyright laws and AI-generated content. The paper discusses generative AI's diverse roles and advocates for transparent reporting to uphold academic integrity. Additionally, it calls for a broader examination of generative AI tools and stresses the need for new mechanisms to identify generative AI use and ensure adherence to academic integrity and ethics. The implications of generative AI are also explored, suggesting the need for innovative AI-inclusive strategies in academia. The paper concludes by emphasizing the significance of generative AI in various information-processing domains, highlighting the urgency to adapt and transform academic practices in an era of rapid generative AI-driven change. [ABSTRACT FROM AUTHOR]

There has been a rise in scepticism regarding the use of Artificial Intelligence (AI) in qualitative research tasks such as critical reviews, conceptualization, thematic and content analysis, and potentially theory development. Concerns have been raised over the possibility that researchers intentionally avoid discussing or even mentioning the use of AI in their studies for a variety of reasons, including the "fear" of criticism and rejection of their papers. The purpose of this paper, which is guided by critical perspective principles, is to examine the controversy surrounding the appropriate recognition of AI in theoretical discussions and qualitative research, including conceptual, critical reviews, empirical, and other types of studies of qualitative nature. Prior to a discussion of how to acknowledge the use of AI, the significance of notions of acknowledgment and academic integrity in the context of research are discussed. As the author of this paper, I acknowledge and document the use of both AI and the researcher's cognitive skills in the development of this theoretical critical perspective study through a four-phase process, while giving directions of when and how to acknowledge the use of AI in qualitative studies. [ABSTRACT FROM AUTHOR]

In recent decades, artificial intelligence has undergone transformative advancements, reshaping diverse sectors such as healthcare, transport, agriculture, energy, and the media. Despite the enthusiasm surrounding AI's potential, concerns persist about its potential negative impacts, including substantial energy consumption and ethical challenges. This paper critically reviews the evolving landscape of AI sustainability, addressing economic, social, and environmental dimensions. The literature is systematically categorized into "Sustainability of AI" and "AI for Sustainability", revealing a balanced perspective between the two. The study also identifies a notable trend towards holistic approaches, with a surge in publications and empirical studies since 2019, signaling the field's maturity. Future research directions emphasize delving into the relatively under-explored economic dimension, aligning with the United Nations' Sustainable Development Goals (SDGs), and addressing stakeholders' influence. [ABSTRACT FROM AUTHOR]

Across the world, artificial intelligence (AI) technologies are being more widely employed in public sector decision-making and processes as a supposedly neutral and an efficient method for optimizing delivery of services. However, the deployment of these technologies has also prompted investigation into the potentially unanticipated consequences of their introduction, to both positive and negative ends. This paper chooses to focus specifically on the relationship between gender bias and AI, exploring claims of the neutrality of such technologies and how its understanding of bias could influence policy and outcomes. Building on a rich seam of literature from both technological and sociological fields, this article constructs an original framework through which to analyse both the perpetuation and mitigation of gender biases, choosing to categorize AI technologies based on whether their input is text or images. Through the close analysis and pairing of four case studies, the paper thus unites two often disparate approaches to the investigation of bias in technology, revealing the large and varied potential for AI to echo and even amplify existing human bias, while acknowledging the important role AI itself can play in reducing or reversing these effects. The conclusion calls for further collaboration between scholars from the worlds of technology, gender studies and public policy in fully exploring algorithmic accountability as well as in accurately and transparently exploring the potential consequences of the introduction of AI technologies. [ABSTRACT FROM AUTHOR]

As a help to researchers for organizing their thoughts and providing data-driven outcomes, Artificial Intelligence (AI) has the potential to develop scientific writing. AI-powered technologies have been created by businesses like Semantic Scholar and Paper Digest to scan scientific texts and extract pertinent data. By expediting the publishing process and enabling academics to concentrate more on their own work, AI-based writing tools like GPT-3 can produce high-quality papers that closely resemble those of well-known authors. These tools can help with idea organization, creating rough drafts, and enhancing the general caliber of scientific work. For instance, ChatGPT is a useful tool in research and publishing since it may help scientists with material arrangement, draft generation, and proofreading. The drawbacks of AI must be understood, as well as the difficulties posed by prejudice, ethical issues, and the requirement for human innovation. We can improve the scientific writing process and increase scientific research by utilizing AI's potential while adding human knowledge. But there is still room for development, and it is essential to guarantee openness, morality, and reliability in AI-driven technology for academic study. [ABSTRACT FROM AUTHOR]