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Radiography education is pivotal in training skilled radiographers for diagnostic imaging and therapeutic applications. With technological advancements, interest in innovative educational tools to enhance traditional teaching methods is growing. This discussion paper explores the possibility of the integration of ChatGPT, a cutting-edge conversational AI language model, into radiography education. We report that ChatGPT offers interactive learning opportunities that can facilitate learning. It also provides self-paced learning, revision platforms, and supports educators in scenario creation, assessment development, group collaboration, and professional and research activities. Despite these benefits, it is important to carefully consider issues related to academic integrity and privacy, along with the opportunities and challenges presented by this new technology in radiography education. This paper highlights some of the prospects and limitations of the potential applications of ChatGPT in radiography education, underscoring the benefits for both students and educators. However, its implementation must be considered thoughtfully and ethically, taking into account its strengths and limitations. Integrating ChatGPT in radiography education has the potential to improve radiography education by improving digital literacy and graduate outcomes of students while streamlining the preparation process for educators. However, ethical implementation is vital for optimal outcomes. [ABSTRACT FROM AUTHOR]

In the rapid evolving landscape of artificial intelligence (AI), scientific publishing is experiencing significant transformations. AI tools, while offering unparalleled efficiencies in paper drafting and peer review, also introduce notable ethical concerns. This study delineates AI's dual role in scientific publishing: as a co-creator in the writing and review of scientific papers and as an ethical challenge. We first explore the potential of AI as an enhancer of efficiency, efficacy, and quality in creating scientific papers. A critical assessment follows, evaluating the risks vs. rewards for researchers, especially those early in their careers, emphasizing the need to maintain a balance between AI's capabilities and fostering independent reasoning and creativity. Subsequently, we delve into the ethical dilemmas of AI's involvement, particularly concerning originality, plagiarism, and preserving the genuine essence of scientific discourse. The evolving dynamics further highlight an overlooked aspect: the inadequate recognition of human reviewers in the academic community. With the increasing volume of scientific literature, tangible metrics and incentives for reviewers are proposed as essential to ensure a balanced academic environment. AI's incorporation in scientific publishing is promising yet comes with significant ethical and operational challenges. The role of human reviewers is accentuated, ensuring authenticity in an AI-influenced environment. As the scientific community treads the path of AI integration, a balanced symbiosis between AI's efficiency and human discernment is pivotal. Emphasizing human expertise, while exploit artificial intelligence responsibly, will determine the trajectory of an ethically sound and efficient AI-augmented future in scientific publishing. [ABSTRACT FROM AUTHOR]

AbstractOpenAI’s ChatGPT is an advanced large language model AI that has caused both excitement and concern in academia. TurnItIn and similar software programs are widely used in higher education to detect potential plagiarism. However, the extent to which such software can identify papers produced by ChatGPT remains unclear. The current study partially addressed this question by submitting five versions of short essays about criminological topics generated from ChatGPT to the TurnItIn software. Overall, the results indicated that TurnItIn adequately detected that the essays were not original works (mean percent plagiarized score = 31%). The analyses further illustrated that ChatGPT wrote at an exceedingly high level (mean Flesch-Kincaid Grade Level = 15.1) atypical of essays in higher education. Consequently, it appears that detection of AI-generated writing may be easier than initially assumed although the technology is bound to improve. Accordingly, possible strategies for approaching AI in higher education are proposed. [ABSTRACT FROM AUTHOR]

Background and Aims: ChatGPT represents the most popular and widely used generative artificial intelligence (AI) model that received significant attention in healthcare research. The aim of the current study was to assess the future trajectory of the needed research in this domain based on the recommendations of the top influential published records. Materials and Methods: A systematic search was conducted on Scopus, Web of Science, and Google Scholar (27–30 November 2023) to identify the top ten ChatGPT-related published records in healthcare across the three databases. Classification of the records as “top” denoting high influence in the field was based on citation counts. Results: A total of 22 unique records from 17 different journals representing 14 different publishers were identified as the top ChatGPT-related publications in healthcare subject. Based on the identified records’ recommendations, the following themes appeared as important areas to consider in future ChatGPT research in healthcare: improving healthcare education, improved efficiency of clinical processes (e.g., documentation), addressing ethical concerns (e.g., patient privacy and consent), supporting research tasks (e.g., data analysis, manuscript preparation), mitigating ChatGPT output biases, improving patient education and engagement, and developing standardized assessment protocols for ChatGPT utility in healthcare. Conclusions: The current review highlighted key areas to be prioritized in assessment of ChatGPT utility in healthcare. Interdisciplinary collaborations and standardizing methodologies are needed to synthesize robust evidence in these studies. Based on these recommendations and the promising potential of ChatGPT on healthcare, JMJ launched a call for papers for a special issue entitled “Evaluating Generative AI-Based Models in Healthcare”. [ABSTRACT FROM AUTHOR]

The aim of this study is to present the possibilities of nurse education in the use of the Chat Generative Pre-training Transformer (ChatGPT) tool to support the documentation process. The success of the nursing process is based on the accuracy of nursing diagnoses, which also determine nursing interventions and nursing outcomes. Educating nurses in the use of artificial intelligence in the nursing process can significantly reduce the time nurses spend on documentation. Discussion paper. We used a case study from Train4Health in the field of preventive care to demonstrate the potential of using Generative Pre-training Transformer (ChatGPT) to educate nurses in documenting the nursing process using generative artificial intelligence. Based on the case study, we entered a description of the patient's condition into Generative Pre-training Transformer (ChatGPT) and asked questions about nursing diagnoses, nursing interventions and nursing outcomes. We further synthesized these results. In the process of educating nurses about the nursing process and nursing diagnosis, Generative Pre-training Transformer (ChatGPT) can present potential patient problems to nurses and guide them through the process from taking a medical history, setting nursing diagnoses and planning goals and interventions. Generative Pre-training Transformer (ChatGPT) returned appropriate nursing diagnoses, but these were not in line with the North American Nursing Diagnosis Association – International (NANDA-I) classification as requested. Of all the nursing diagnoses provided, only one was consistent with the most recent version of the North American Nursing Diagnosis Association – International (NANDA-I). Generative Pre-training Transformer (ChatGPT) is still not specific enough for nursing diagnoses, resulting in incorrect answers in several cases. Using Generative Pre-training Transformer (ChatGPT) to educate nurses and support the documentation process is time-efficient, but it still requires a certain level of human critical-thinking and fact-checking. [ABSTRACT FROM AUTHOR]