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ChatGPT and generative artificial intelligence (Gen-AI) are transforming firms and supply chains. However, the empirical literature reporting the benefits, challenges, and outlook of these nascent technologies in operations and supply chain management (OSCM) is limited. This study surveys current projects and perceptions of these technologies in US (n = 119) and UK (n = 181) supply chains. We found that projects range from proof-of-concept to full implementation, with a main focus on operational gains, such as improved customer satisfaction, cost minimisation, and process efficiencies. The main challenges concern data, technological and organisational issues. Expected benefits are dominated by cost savings and enhanced customer experience, but also include increased automation and sustainability. Industries were found to cluster around six groups according to perceived benefits and implementation challenges. Our findings contribute to the emerging literature on Gen-AI use in OSCM, and to management practice by mapping the benefits, challenges, outlook, and maturity level of Gen-AI projects in supply chains. [ABSTRACT FROM AUTHOR]

Large language models (LLMs) like ChatGPT have potential applications in medical education such as helping students study for their licensing exams by discussing unclear questions with them. However, they require evaluation on these complex tasks. The purpose of this study was to evaluate how well publicly accessible LLMs performed on simulated UK medical board exam questions. 423 board-style questions from 9 UK exams (MRCS, MRCP, etc.) were answered by seven LLMs (ChatGPT-3.5, ChatGPT-4, Bard, Perplexity, Claude, Bing, Claude Instant). There were 406 multiple-choice, 13 true/false, and 4 "choose N" questions covering topics in surgery, pediatrics, and other disciplines. The accuracy of the output was graded. Statistics were used to analyze differences among LLMs. Leaked questions were excluded from the primary analysis. ChatGPT 4.0 scored (78.2%), Bing (67.2%), Claude (64.4%), and Claude Instant (62.9%). Perplexity scored the lowest (56.1%). Scores differed significantly between LLMs overall (p < 0.001) and in pairwise comparisons. All LLMs scored higher on multiple-choice vs true/false or "choose N" questions. LLMs demonstrated limitations in answering certain questions, indicating refinements needed before primary reliance in medical education. However, their expanding capabilities suggest a potential to improve training if thoughtfully implemented. Further research should explore specialty specific LLMs and optimal integration into medical curricula. [ABSTRACT FROM AUTHOR]

Background Patient information leaflets (PILs) can supplement a clinical consultation and provide additional information for a patient to read in their own time. A wide range of PILs are available for distribution by the British Association of Dermatologists (BAD) and undergo rigorous review ahead of publication. In the UK, 7.1 million adults are estimated to have the reading age of a 9-year-old child and 43% are unable to comprehend written health information. Objectives To determine whether artificial intelligence (AI) can produce PILs that include a similar degree of content to current BAD PILs. Methods Using the AI tool ChatGPT, 10 PILs were generated, and their contents compared with those of existing BAD PILs using an author-generated list of commonly included themes. Omissions were noted and a repeat series of PILs generated using targeted request phrasing. The readability of AI-generated PILs was also analysed. Results AI-generated PILs were found to include similar factual content to BAD PILs but excluded information that was felt to be more pertinent to patient concerns such as curability and heritability. Targeted request phrasing saw AI generate PILs including this content. The readability of AI-generated PILs was beyond that of a large number of UK adults. Conclusions Where a condition-specific PIL is not readily available, an AI-generated PIL can provide relevant information of lesser quality than existing BAD PILs, which may be inaccessible to some patients. Specific caution is advised regarding AI-generated medication-specific PILs. [ABSTRACT FROM AUTHOR]

Chatbots are computer programs that mimic human conversation using text or voice or both. Users' acceptance of chatbots is highly influenced by their persona. Users develop a sense of familiarity with chatbots as they use them, so they become more approachable, and this encourages them to interact with the chatbots more readily by fostering favorable opinions of the technology. In this study, we examine the moderating effects of persona traits on students' acceptance and use of chatbot technology at higher educational institutions in the UK. We use an Extended Unified Theory of Acceptance and Use of Technology (Extended UTAUT2). Through a self-administrated survey using a questionnaire, data were collected from 431 undergraduate and postgraduate computer science students. This study employed a Likert scale to measure the variables associated with chatbot acceptance. To evaluate the gathered data, Structural Equation Modelling (SEM) coupled with multi-group analysis (MGA) using SmartPLS3 were used. The estimated Cronbach's alpha highlighted the accuracy and legitimacy of the findings. The results showed that the emerging factors that influence students' adoption and use of chatbot technology were habit, effort expectancy, and performance expectancy. Additionally, it was discovered that the Extended UTAUT2 model did not require grades or educational level to moderate the correlations. These results are important for improving user experience and they have implications for academics, researchers, and organizations, especially in the context of native chatbots. [ABSTRACT FROM AUTHOR]

Assess whether ChatGPT 4.0 is accurate enough to perform research evaluations on journal articles to automate this time-consuming task. Test the extent to which ChatGPT-4 can assess the quality of journal articles using a case study of the published scoring guidelines of the UK Research Excellence Framework (REF) 2021 to create a research evaluation ChatGPT. This was applied to 51 of my own articles and compared against my own quality judgements. ChatGPT-4 can produce plausible document summaries and quality evaluation rationales that match the REF criteria. Its overall scores have weak correlations with my self-evaluation scores of the same documents (averaging r=0.281 over 15 iterations, with 8 being statistically significantly different from 0). In contrast, the average scores from the 15 iterations produced a statistically significant positive correlation of 0.509. Thus, averaging scores from multiple ChatGPT-4 rounds seems more effective than individual scores. The positive correlation may be due to ChatGPT being able to extract the author's significance, rigour, and originality claims from inside each paper. If my weakest articles are removed, then the correlation with average scores (r=0.200) falls below statistical significance, suggesting that ChatGPT struggles to make fine-grained evaluations. The data is self-evaluations of a convenience sample of articles from one academic in one field. Overall, ChatGPT does not yet seem to be accurate enough to be trusted for any formal or informal research quality evaluation tasks. Research evaluators, including journal editors, should therefore take steps to control its use. This is the first published attempt at post-publication expert review accuracy testing for ChatGPT. [ABSTRACT FROM AUTHOR]

This study analyses the discursive representation of Artificial Intelligence (AI) and ChatGPT in UK news media headlines from January to May 2023. A total of 671 headlines were collected and analysed using inductive thematic analysis, theoretically informed by Agenda-Setting theory and Framing theory. The results offer an initial picture of how recent technological advances in the fields of AI have been communicated to the public. The results show that there is a complex and at times paradoxical portrayal of AI in general and ChatGPT as well as other Large Language Models (LLMs), oscillating between promising potential for solving societal challenges while simultaneously warning of imminent and systemic dangers. Further to this, the analysis provides evidence for the claim that media representations of AI are often sensationalised and tend to focus more on warnings and caution to readers, as only a minority of headlines were related to helpful, useful, or otherwise positive applications of AI, ChatGPT, and other Large Language Models (LLMs). These findings underscore the pivotal role of media discourse in shaping public perceptions of AI. The study prompts reflections on news media practices in the United Kingdom and encourages future research to further examine the influence of social, cultural, and political contexts on AI representation during a period of technological change. This research provides relevant insights for policymakers, AI developers, and educators to support public engagement with AI technologies. [ABSTRACT FROM AUTHOR]

ChatGPT is an emerging tool that can be employed in many activities including in learning/teaching in universities. Like many other tools, it has its benefits and its drawbacks. If used properly, it can improve learning, and if used irresponsibly, it can have a negative impact on learning. The aim of this research is to study how ChatGPT can be used in academia to improve teaching/learning activities. In this paper, we study students' opinions about how the tool can be used positively in learning activities. A survey is conducted among 430 students of an MSc degree in computer science at the University of Hertfordshire, UK, and their opinions about the tool are studied. The survey tries to capture different aspects in which the tool can be employed in academia and the ways in which it can harm or help students in learning activities. The findings suggest that many students are familiar with the tool but do not regularly use it for academic purposes. Moreover, students are skeptical of its positive impacts on learning and think that universities should provide more vivid guidelines and better education on how and where the tool can be used for learning activities. The students' feedback responses are analyzed and discussed and the authors' opinions regarding the subject are presented. This study shows that ChatGPT can be helpful in learning/teaching activities, but better guidelines should be provided for the students in using the tool. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of the paper "Bibliometric Analysis of ChatGPT's Applications in Medicine: A Comprehensive Assessment of its Impact and Potential" is to conduct a systematic evaluation of how ChatGPT, an AI language model, has been applied in the field of medicine. The study aims to assess the impact of ChatGPT's applications in medicine and explore its potential contributions to the healthcare domain. Materials and Methods: The Scopus database was selected and the search query (All (ChatGPT AND Medicine) was developed on 11, July 2023 to retrieve all the bibliographic records on the domine of interest. From each record, we retrieved data on the title, author, organizations, journals, publications type, source, country, collaboration, etc. They were exported to MS-Excel for bibliometric analysis and VOSviewer software was used for analyzing Co-Occurrence networks and the data and descriptive analysis were classified, collected one by one, and loaded into the Bibliometric R-package program to create science maps and statistical graphs. Results: ChatGPT - related Medicine papers in the Scopus database constitute 532 papers. these publications received 1046 citations, these authored by 1771 authors published 203 article type and etc. Average citations per document 1966 received and 111 (20.86%) received extremal funding support from 62+ research agencies supporting research in this area. selected 65 significant keywords appearing in ChatGPT and Medicine. USA and UK were contributed the highest papers. Medicine is the broad subject of the study were published highest number of papers (312). Conclusion: The findings from this study can help researchers, practitioners, and policymakers better understand the impact and potential of ChatGPT in advancing medical knowledge, patient care, and healthcare practices. Additionally, the paper may contribute to identifying knowledge gaps and guide further research in this rapidly evolving field at the intersection of AI and medicine. [ABSTRACT FROM AUTHOR]

A documentary review was carried out on the production and publication of research papers related to the study of the variables CHATGPT, TEACHING, NATURAL LANGUAGE and ARTIFICIAL INTELLIGENCE. The purpose of the bibliometric analysis proposed in this document was to know the main characteristics of the volume of publications registered in the Scopus database during the first semester of 2023, achieving the identification of 10 publications. The information provided by this platform was organized through graphs and figures categorizing the information by, Country of Origin, Area of Knowledge and Type of Publication. Once these characteristics have been described, the position of different authors towards the proposed theme is referenced through a qualitative analysis. Among the main findings made through this research, it is found that the United Kingdom with 2 publications with the highest scientific production registered in the name of authors affiliated with institutions of that nation. The Area of Knowledge that made the greatest contribution to the construction of bibliographic material referring to the study of CHATGPT, TEACHING, NATURAL LANGUAGE and ARTIFICIAL INTELLIGENCE was Social Sciences with 8 published documents, and the Type of Publication most used during the period indicated above were Journal Articles with 70% of the total scientific production. [ABSTRACT FROM AUTHOR]

AI-tools such as ChatGPT can assist researchers to improve the performance of the research process. This paper examines whether researchers could apply ChatGPT to develop and empirically validate new research scales. The study describes a process how to prompt ChatGPT to assist the scale development of a new construct, using the example of the construct of perceived value of ChatGPT-supported consumer behavior. The paper reports four main empirical studies (US: N = 148; Australia: N = 317; UK: N = 108; Germany: N = 51) that have been employed to validate the newly developed scale. The first study purifies the scale. The following studies confirm the adjusted factorial validity of the reduced scale. Although the empirical data imply a simplification of the initial multi-dimensional scale, the final three-dimensional operationalization is highly reliable and valid. The paper outlines the shortcomings and several critical notes to stimulate more research and discussion in this area. • This paper examines whether researchers could apply ChatGPT to develop and empirically validate new research scales. • The study describes a process how to prompt ChatGPT to assist the scale development of a new construct. • Four empirical studies establish the prognostic validity and the construct validity of the newly developed scale. • The paper outlines the shortcomings and several critical notes to stimulate more research and discussion in this area. [ABSTRACT FROM AUTHOR]

ChatGPT could allow students to plagiarize the content of their coursework with little risk of detection. Little is known about undergraduate willingness to use AI tools. In this study, psychology undergraduates (N = 160) from the United Kingdom, indicated their willingness to use, and history of using, ChatGPT to write university assignments. Almost a third (32%) indicated that they would use such tools; 15% indicated that they had used them already. Neither personality (conscientiousness, agreeableness, Machiavellianism, narcissism), academic performance, nor study skills self-efficacy could predict future use of AI tools. A novel Degree Apathy Scale was the only significant predictor. Willingness to use AI tools was greater when the risk of getting caught was low, and punishment was light, particularly for those high in degree apathy. Findings suggest that degree apathy is a key risk factor in academic misconduct. Wider research and pedagogical applications of degree apathy are discussed. • Thirty-two percent of students surveyed reported that they would use ChatGPT on future academic assignments. • Future use of AI tools was significantly predicted by a new Degree Apathy Scale. • Big Five and Dark Triad scores were not significant predictors, nor was previous academic performance. • Likelihood of cheating using ChatGPT was reduced with increasing risk of detection or severity of punishment. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: Chat Generative Pre-trained Transformer (ChatGPT) is an artificial intelligence (AI) tool which utilises machine learning to generate original text resembling human language. AI models have recently demonstrated remarkable ability at analysing and solving problems, including passing professional examinations. We investigate the performance of ChatGPT on some of the UK radiology fellowship equivalent examination questions., Methods: ChatGPT was asked to answer questions from question banks resembling the Fellowship of the Royal College of Radiologists (FRCR) examination. The entire physics part 1 question bank (203 5-part true/false questions) was answered by the GPT-4 model and answers recorded. 240 single best answer questions (SBAs) (representing the true length of the FRCR 2A examination) were answered by both GPT-3.5 and GPT-4 models., Results: ChatGPT 4 answered 74.8% of part 1 true/false statements correctly. The spring 2023 passing mark of the part 1 examination was 75.5% and ChatGPT thus narrowly failed. In the 2A examination, ChatGPT 3.5 answered 50.8% SBAs correctly, while GPT-4 answered 74.2% correctly. The winter 2022 2A pass mark was 63.3% and thus GPT-4 clearly passed., Conclusion: AI models such as ChatGPT are able to answer the majority of questions in an FRCR style examination. It is reasonable to assume that further developments in AI will be more likely to succeed in comprehending and solving questions related to medicine, specifically clinical radiology., Advances in Knowledge: Our findings outline the unprecedented capabilities of AI, adding to the current relatively small body of literature on the subject, which in turn can play a role medical training, evaluation and practice. This can undoubtedly have implications for radiology., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Artificial Intelligence (AI) chatbots are able to explain complex concepts using plain language. The aim of this study was to assess the accuracy of three AI chatbots answering common questions related to contact lens (CL) wear. Three open access AI chatbots were compared: Perplexity, Open Assistant and ChatGPT 3.5. Ten general CL questions were asked to all AI chatbots on the same day in two different countries, with the questions asked in Spanish from Spain and in English from the U.K. Two independent optometrists with experience working in each country assessed the accuracy of the answers provided. Also, the AI chatbots' responses were assessed if their outputs showed any bias towards (or against) any eye care professional (ECP). The answers obtained by the same AI chatbots were different in Spain and the U.K. Also, statistically significant differences were found between the AI chatbots for accuracy. In the U.K., ChatGPT 3.5 was the most and Open Assistant least accurate (p < 0.01). In Spain, Perplexity and ChatGPT were statistically more accurate than Open Assistant (p < 0.01). All the AI chatbots presented bias, except ChatGPT 3.5 in Spain. AI chatbots do not always consider local CL legislation, and their accuracy seems to be dependent on the language used to interact with them. Hence, at this time, although some AI chatbots might be a good source of information for general CL related questions, they cannot replace an ECP. [ABSTRACT FROM AUTHOR]

Cardiac arrest leaves witnesses, survivors, and their relatives with a multitude of questions. When a young or a public figure is affected, interest around cardiac arrest and cardiopulmonary resuscitation (CPR) increases. ChatGPT allows everyone to obtain human-like responses on any topic. Due to the risks of accessing incorrect information, we assessed ChatGPT accuracy in answering laypeople questions about cardiac arrest and CPR. We co-produced a list of 40 questions with members of Sudden Cardiac Arrest UK covering all aspects of cardiac arrest and CPR. Answers provided by ChatGPT to each question were evaluated by professionals for their accuracy, by professionals and laypeople for their relevance, clarity, comprehensiveness, and overall value on a scale from 1 (poor) to 5 (excellent), and for readability. ChatGPT answers received an overall positive evaluation (4.3 ± 0.7) by 14 professionals and 16 laypeople. Also, clarity (4.4 ± 0.6), relevance (4.3 ± 0.6), accuracy (4.0 ± 0.6), and comprehensiveness (4.2 ± 0.7) of answers was rated high. Professionals, however, rated overall value (4.0 ± 0.5 vs 4.6 ± 0.7; p = 0.02) and comprehensiveness (3.9 ± 0.6 vs 4.5 ± 0.7; p = 0.02) lower compared to laypeople. CPR-related answers consistently received a lower score across all parameters by professionals and laypeople. Readability was 'difficult' (median Flesch reading ease score of 34 [IQR 26–42]). ChatGPT provided largely accurate, relevant, and comprehensive answers to questions about cardiac arrest commonly asked by survivors, their relatives, and lay rescuers, except CPR-related answers that received the lowest scores. Large language model will play a significant role in the future and healthcare-related content generated should be monitored. [ABSTRACT FROM AUTHOR]

Background: The National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) has amassed a vast reservoir of genetic data since its inception in 2007. These public data hold immense potential for supporting pathogen surveillance and control. However, the lack of standardized metadata and inconsistent submission practices in SRA may impede the data's utility in public health., Methods: To address this issue, we introduce the Search-based Geographic Metadata Curation (SGMC) pipeline. SGMC utilized Python and web scraping to extract geographic data of sequencing institutions from NCBI SRA in the Cloud and its website. It then harnessed ChatGPT to refine the sequencing institution and location assignments. To illustrate the pipeline's utility, we examined the geographic distribution of the sequencing institutions and their countries relevant to polio eradication and categorized them., Results: SGMC successfully identified 7,649 sequencing institutions and their global locations from a random selection of 2,321,044 SRA accessions. These institutions were distributed across 97 countries, with strong representation in the United States, the United Kingdom and China. However, there was a lack of data from African, Central Asian, and Central American countries, indicating potential disparities in sequencing capabilities. Comparison with manually curated data for U.S. institutions reveals SGMC's accuracy rates of 94.8% for institutions, 93.1% for countries, and 74.5% for geographic coordinates., Conclusion: SGMC may represent a novel approach using a generative AI model to enhance geographic data (country and institution assignments) for large numbers of samples within SRA datasets. This information can be utilized to bolster public health endeavors., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Zhao, Farrell, Mashiku, Abay, Tang, Oberste and Burns.)