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ABSTRACT Artificial Intelligence (AI)‐driven chatbots, such as ChatGPT, have significantly impacted education, especially for English as a Foreign Language (EFL) learners. However, there is paucity of empirical evidence concerning the role of chatbots in psycho‐emotional constructs like well‐being and emotion regulation. It is important to address this issue because it can further our understanding of the ways through which using ChatGPT affects psycho‐emotional constructs in EFL learners. This study aimed to unpack the intersection between using ChatGPT and well‐being, with a focus on the mediating role of emotion regulation in the EFL context of Iran. Using convenience sampling, 492 EFL learners (205 males and 287 females) were invited to complete validated scales measuring ChatGPT use, well‐being, and emotion regulation. The outcomes of structural equation modelling revealed a strong mediation effect of emotion regulation in the relationship between using ChatGPT and well‐being. Additionally, significant positive correlations were found between using ChatGPT and both well‐being and emotion regulation. Besides, a significant positive relationship was established between emotion regulation and well‐being among the EFL learners. The results imply that the integration of ChatGPT into the Iranian EFL learning environment can be beneficial, considering its positive correlations with both well‐being and emotion regulation among the Iranian EFL learners. [ABSTRACT FROM AUTHOR]

This study compares how the generative AI (GenAI) large language model (LLM) ChatGPT performs in grading university exams compared to human teachers. Aspects investigated include consistency, large discrepancies and length of answer. Implications for higher education, including the role of teachers and ethics, are also discussed. Three Master's‐level exams were scored using ChatGPT 3.5, and the results were compared with the teachers' scoring and the grading teachers were interviewed. In total, 463 exam responses were graded. With each response being graded at least three times, a total of 1389 gradings were conducted. For the final exam scores, 70% of ChatGPT's gradings were within 10% of the teachers' gradings and 31% within 5%. ChatGPT tended to give marginally higher scores. The agreement on grades is 30%, but 45% of the exams received an adjacent grade. On individual questions, ChatGPT is more inclined to avoid very high or very low scores. ChatGPT struggles to correctly score questions closely related to the course lectures but performs better on more general questions. The AI can generate plausible scores on university exams that, at first glance, look similar to a human grader. There are differences but it is not unlikely that two different human graders could result in similar discrepancies. During the interviews, teachers expressed their surprise at how well ChatGPT's grading matched their own. Increased use of AI can lead to ethical challenges as exams are entrusted to a machine whose decision‐making criteria are not fully understood, especially concerning potential bias in training data. [ABSTRACT FROM AUTHOR]

This letter to the editor discusses the use of artificial intelligence chatbots for providing cancer treatment information. The authors refer to a study that evaluated the responses of a chatbot to common patient queries in dermatology, as well as other studies that assessed the accuracy and reliability of chatbot responses to physician questions and the effectiveness of chatbots in evaluating systemic therapies for plaque psoriasis. The authors emphasize the potential role of large language models in medical education and stress the importance of evaluating the reliability of medical information provided by chatbots. They provide additional resources on retinoids, the natural history of cutaneous propionibacteria, and evidence-based guidelines for acne treatment. [Extracted from the article]

A national curriculum cannot be uniformly applied in all classrooms. Educators frequently adapt the official curriculum to suit their particular circumstances. In exploring the interplay between artificial intelligence (AI) technologies and curriculum adaptation in education, this study bridges a significant gap in the literature by exploring how AI tools influence teachers' strategies for adapting curricula. Employing an explanatory sequential design, the research analyses both qualitative and quantitative data from 440 teachers, using the Curriculum Adaptation Patterns Scale and focus group semi‐structured interviews. Results indicate a balanced approach among teachers towards extending and revising the curriculum, with less emphasis on omission. Notably, curriculum adaptation practices evolve positively with increased professional experience, differ across disciplines, but remain constant across school levels and educational levels. Qualitatively, teachers reported positive experiences using AI, particularly ChatGPT, to make their lessons better fit students' needs. They've used it to omit parts that aren't needed, add more relevant and personalised content, and revise or replace the content. The findings highlight AI's transformative potential in curriculum adaptation, making education more engaging, relevant and personalised. The study contributes to understanding how AI can support effective curriculum implementation and enhance learning experiences in the digital age. [ABSTRACT FROM AUTHOR]

Motivation Objectives Methods Results and Conclusions This research investigates the transformative impact of integrating AI into mathematics education, aiming to enhance students' conceptual understanding and self‐efficacy. It addresses the crucial need for innovative teaching methods in response to contemporary challenges in education and aims to fill gaps in understanding the potential drawbacks and benefits of AI implementation.The primary goal of this study is to investigate the effects of AI tools, such as GeoGebra and ChatGPT, on students' conceptual understanding and self‐efficacy in a mathematics classroom setting, with a focus on collaborative learning, teacher‐led discussions, and problem‐solving.Employing a mixed‐methods approach, the research involves pre‐ and post‐implementation surveys, supplemented by qualitative data from interviews, focus groups, and classroom observations. The study emphasises the utilisation of AI in collaborative learning environments and explores the challenges and opportunities associated with its integration.The study reveals a noteworthy enhancement in students' conceptual understanding and self‐efficacy belief when AI tools are incorporated into mathematics education. However, it highlights concerns such as technological challenges, potential teacher replacement fears, biases in AI systems, and difficulties related to pacing and social interaction. To optimise the use of AI, the paper suggests strategies like adaptive learning platforms, real‐world simulations, intelligent tutoring systems, and data‐driven instruction, positioning AI as a complement rather than a substitute for traditional teaching methods. The study recommends addressing challenges through additional support resources and transparent communication, emphasising the need for a thoughtful integration of AI in education. [ABSTRACT FROM AUTHOR]

Aim Methods Results Conclusions Artificial intelligence, especially ChatGPT, has been used in various aspects of medicine; however, whether ChatGPT can be used in case report writing is unknown. This study aimed to provoke discussion and provide a platform for it.I wrote a theoretical case report where cyst aspiration cured a twisted ovarian cyst (Manuscript 4). I tasked ChatGPT with generating case reports by inputting information at three different levels: (1) key message and case profile, (2) addition of key introduction information (including known facts and problems to be solved), and (3) further addition of main discussion points. These inputs resulted in the creation of Manuscripts 1–3, which were subjected to analysis. Manuscript 3, generated by ChatGPT with the deepest information input, was compared with Manuscript 4, the human‐authored counterpart.With the least information, Manuscript 1 can stand on its own, but its content is superficial. The more detailed data input, the more readable and reasonable the manuscripts become. A human‐written manuscript involves personal experience and viewpoints other than obstetrics‐gynecology.Better input produced more reasonable and readable case reports. Human‐written paper, compared with ChatGPT‐generated one, can involve “human touch.” Whether such human touch enriches the case report awaits further discussion. Whether ChatGPT can be used in case report writing, and if it can, to what extent, should be worthy of further study. I encourage every doctor to form their own stance towards ChatGPT use in medical writing. [ABSTRACT FROM AUTHOR]

Correspondence and Opinion provide a ‘human voice,’ serving as the captivating elements that grab readers' attention and stimulate contemplation. The extent of ChatGPT's involvement in writing raises controversy. We tested ChatGPT's capability to generate readable Correspondence or Opinion, producing an English Opinion from a Japanese abstract. We also tasked ChatGPT with creating Correspondence and Reply for an English→German translated Opinion. Opinion, Correspondence, and Reply output here were found to be readable and reasonable. While preliminary, these results suggest that ChatGPT can generate such articles, prompting serious concern about AI taking over the ‘human voice.’ Some regulatory measures may be needed. [ABSTRACT FROM AUTHOR]

Background Methods Results Conclusion Accurate conflict of interest (COI) information is essential for promoting transparency and trust in research. We aim to assess COI disclosure patterns in monoclonal antibodies (MABs) research for chronic rhinosinusitis with nasal polyposis (CRSwNP) using the Open Payments Database (OPD).Studies on FDA‐approved MABs for CRSwNP (dupilumab, omalizumab, mepolizumab) published between 2019 and 2021 with at least one US author were identified through PubMed. Industry‐reported payments from the manufacturers (Sanofi, Regeneron, Genentech, Novartis, and GlaxoSmithKline) between 2018 and 2021 in OPD's General Payments category were collected. Authors were cross‐checked against OPD metadata using a previously published ChatGPT‐based algorithm. Additionally, this novel algorithm analyzed COI statements for relevant author‒company specific disclosures, identifying disclosed and undisclosed payments made 3‒15 months prior to publication.A total of 214 unique authors from 76 studies were included. Of 30 articles that received at least one relevant payment, 21 (70%) were found to have an undisclosed COI, with a mean total undisclosed payment of $4890 and a median of $10,331. Fifty‐six authors had relevant OPD payments and 40 (71.4%) authors did not declare a potential COI. Interestingly, 158 authors had no relevant payments and 62 (39.2%) declared a potential COI. Author order was not significantly associated with potential under‐ or over‐disclosure.This study characterizes COI disclosure patterns in rhinosinusitis‐relevant MABs research using a novel automated approach. Given the discrepancy between disclosures and industry‐reported payments, our findings suggest a need for improved disclosure education and practices. [ABSTRACT FROM AUTHOR]

Summary Large language models (LLMs) have significantly impacted various fields with their ability to understand and generate human‐like text. This study explores the potential benefits and limitations of integrating LLMs, such as ChatGPT, into haematology practices. Utilizing systematic review methodologies, we analysed studies published after 1 December 2022, from databases like PubMed, Web of Science and Scopus, and assessing each for bias with the QUADAS‐2 tool. We reviewed 10 studies that applied LLMs in various haematology contexts. These models demonstrated proficiency in specific tasks, such as achieving 76% diagnostic accuracy for haemoglobinopathies. However, the research highlighted inconsistencies in performance and reference accuracy, indicating variability in reliability across different uses. Additionally, the limited scope of these studies and constraints on datasets could potentially limit the generalizability of our findings. The findings suggest that, while LLMs provide notable advantages in enhancing diagnostic processes and educational resources within haematology, their integration into clinical practice requires careful consideration. Before implementing them in haematology, rigorous testing and specific adaptation are essential. This involves validating their accuracy and reliability across different scenarios. Given the field's complexity, it is also critical to continuously monitor these models and adapt them responsively. [ABSTRACT FROM AUTHOR]

This paper critically explores the implications of generative artificial “intelligence” (GAI) technologies for literacy theory and practice through a case study of the author's use of OpenAI's ChatGPT. The study opens with an overview of recent literature surrounding the pedagogical implications of using GAI with a focus on issues of racial justice, outlining an abolitionist political ecology approach to literacy that extends relational theories of mediation to machine‐aided writing. The framework is then applied to data from a cognitive autoethnography of GAI use over a 6‐month period, which included a digital ethnography of ChatGPT and an extended semistructured “interview” with the GAI chatbot. Racial justice issues were found, especially linguistic and other biases. As such, soon‐to‐be ubiquitous artificial intelligence (AI) technologies require profound reconsideration of the productive value of literacy exploited by GAI, which will inevitably be pursued through an acquiescence or fundamental rupture with the dystopian visions of the technology's creators. [ABSTRACT FROM AUTHOR]

This chapter explores the potential of integrating conversational AI tools such as ChatGPT with data visualization (DV) tools such as Power BI in higher education settings. A brief history of chatbots is summarized and challenges and opportunities in higher education are outlined. The highlights include AI's prospects for enhancing data‐informed decision‐making while needing safeguards to mitigate risks. Through a pioneering exercise, we integrated ChatGPT's conversational capabilities with Power BI's interface via API and tested functionality. Suggestions for good practice and implications for higher education are discussed. Practical Takeaways: The importance of data security, recognizing current limitations in accuracy, and good practices for integrating DV tools with ChatGPT. The exercise shows that AI tools have the potential to aid higher education leaders, but still face many challenges, and human judgment remains essential.Integration of ChatGPT with Power BI in higher education aims to enable natural language interaction with data visualizations, facilitating dynamic discussions and insights for leaders.Considerations include data security protocols, technical challenges, financial constraints, and the potential and limitations of AI. Technical challenges include limitations in ChatGPT's interpretation of complex queries, API performance issues, and data processing inefficiencies.Affordability of enterprise AI versions poses a barrier to adoption for institutions with budget constraints despite recognizing potential benefits.ChatGPT represents a significant advancement in AI capabilities but has limitations, including generating inaccurate information and lacking awareness of its limitations. Human oversight and critical evaluation are crucial in navigating the evolving AI landscape and leveraging its benefits effectively while mitigating risks. [ABSTRACT FROM AUTHOR]

This study explored the perceived impact of integrating Generative Artificial Intelligence Systems (GenAI) such as ChatGPT into a postgraduate Master of Education program, drawing on the dual perspectives of participants who were students and educators teaching students at different levels of education in England. Using the Situated Expectancy‐Value Theory and Activity‐Centered Analysis and Design theoretical frameworks, the findings revealed opportunities, benefits, and challenges. Benefits included personalized learning and efficiencies in course design while the challenges consisted of ethical concerns, holistic skill development and problem solving. The surprising outcome of this study was the strong emotive anxiety around using AI and GenAI in participants' academic and personal lives. A balanced, critically reflective approach can help realize ChatGPT's educational potential while navigating implementation barriers and associated challenges. Participants insights can add to the dialogue negotiating ways of effectively integrating GenAI into the higher education curriculum. Practical Takeaways: Foster collaboration: Educators and students should work together to develop critical evaluation skills, ethical frameworks, and adaptive strategies for responsible GenAI use.Prioritize ethical considerations: Promote ongoing reflection on the personal and academic implications of GenAI, including potential biases and impacts on equity.Balance innovation with traditional skills: Emphasize that GenAI augments, rather than replaces, core academic skills like critical thinking, source verification, and effective communication.Align with student values: To sustain engagement, connect GenAI's use with students' immediate goals, future aspirations, and the potential to improve their learning experiences and outcomes. [ABSTRACT FROM AUTHOR]

The adoption of large language models (LLMs) in education holds much promise. However, like many technological innovations before them, adoption and access can often be inequitable from the outset, creating more divides than they bridge. In this paper, we explore the magnitude of the country and language divide in the leading open‐source and proprietary LLMs with respect to knowledge of K‐12 taxonomies in a variety of countries and their performance on tagging problem content with the appropriate skill from a taxonomy, an important task for aligning open educational resources and tutoring content with state curricula. We also experiment with approaches to narrowing the performance divide by enhancing LLM skill tagging performance across four countries (the USA, Ireland, South Korea and India–Maharashtra) for more equitable outcomes. We observe considerable performance disparities not only with non‐English languages but with English and non‐US taxonomies. Our findings demonstrate that fine‐tuning GPT‐3.5 with a few labelled examples can improve its proficiency in tagging problems with relevant skills or standards, even for countries and languages that are underrepresented during training. Furthermore, the fine‐tuning results show the potential viability of GPT as a multilingual skill classifier. Using both an open‐source model, Llama2‐13B, and a closed‐source model, GPT‐3.5, we also observe large disparities in tagging performance between the two and find that fine‐tuning and skill information in the prompt improve both, but the closed‐source model improves to a much greater extent. Our study contributes to the first empirical results on mitigating disparities across countries and languages with LLMs in an educational context. Practitioner notesWhat is already known about this topic Recent advances in generative AI have led to increased applications of LLMs in education, offering diverse opportunities.LLMs excel predominantly in English and exhibit a bias towards the US context.Automated content tagging has been studied using English‐language content and taxonomies.What this paper adds Investigates the country and language disparities in LLMs concerning knowledge of educational taxonomies and their performance in tagging content.Presents the first empirical findings on addressing disparities in LLM performance across countries and languages within an educational context.Improves GPT‐3.5's tagging accuracy through fine‐tuning, even for non‐US countries, starting from zero accuracy.Extends automated content tagging to non‐English languages using both open‐source and closed‐source LLMs.Implications for practice and/or policy Underscores the importance of considering the performance generalizability of LLMs to languages other than English.Highlights the potential viability of ChatGPT as a skill tagging classifier across countries. [ABSTRACT FROM AUTHOR]

ChatGPT is a language model that was trained on a large dataset including medical literature. Several studies have described the performance of ChatGPT on medical exams. In this study, we examine its performance in answering factual knowledge questions regarding clinical pharmacy. Questions were obtained from a Dutch application that features multiple‐choice questions to maintain a basic knowledge level for clinical pharmacists. In total, 264 clinical pharmacy‐related questions were presented to ChatGPT and responses were evaluated for accuracy, concordance, quality of the substantiation, and reproducibility. Accuracy was defined as the correctness of the answer, and results were compared to the overall score by pharmacists over 2022. Responses were marked concordant if no contradictions were present. The quality of the substantiation was graded by two independent pharmacists using a 4‐point scale. Reproducibility was established by presenting questions multiple times and on various days. ChatGPT yielded accurate responses for 79% of the questions, surpassing pharmacists' accuracy of 66%. Concordance was 95%, and the quality of the substantiation was deemed good or excellent for 73% of the questions. Reproducibility was consistently high, both within day and between days (>92%), as well as across different users. ChatGPT demonstrated a higher accuracy and reproducibility to factual knowledge questions related to clinical pharmacy practice than pharmacists. Consequently, we posit that ChatGPT could serve as a valuable resource to pharmacists. We hope the technology will further improve, which may lead to enhanced future performance. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the quality of ChatGPT responses to common issues in obstetrics and assess its ability to provide reliable responses to pregnant individuals. The study aimed to examine the responses based on expert opinions using predetermined criteria, including "accuracy," "completeness," and "safety." Methods: We curated 15 common and potentially clinically significant questions that pregnant women are asking. Two native English‐speaking women were asked to reframe the questions in their own words, and we employed the ChatGPT language model to generate responses to the questions. To evaluate the accuracy, completeness, and safety of the ChatGPT's generated responses, we developed a questionnaire with a scale of 1 to 5 that obstetrics and gynecology experts from different countries were invited to rate accordingly. The ratings were analyzed to evaluate the average level of agreement and percentage of positive ratings (≥4) for each criterion. Results: Of the 42 experts invited, 20 responded to the questionnaire. The combined score for all responses yielded a mean rating of 4, with 75% of responses receiving a positive rating (≥4). While examining specific criteria, the ChatGPT responses were better for the accuracy criterion, with a mean rating of 4.2 and 80% of the questions received a positive rating. The responses scored less for the completeness criterion, with a mean rating of 3.8 and 46.7% of questions received a positive rating. For safety, the mean rating was 3.9 and 53.3% of questions received a positive rating. There was no response with an average negative rating below three. Conclusion: This study demonstrates promising results regarding potential use of ChatGPT's in providing accurate responses to obstetric clinical questions posed by pregnant women. However, it is crucial to exercise caution when addressing inquiries concerning the safety of the fetus or the mother. Synopsis: ChatGPT demonstrated the ability to provide accurate and comprehensive responses to common obstetric questions. [ABSTRACT FROM AUTHOR]

Large Language Models (LLMs) are transformer‐based neural networks with billions of parameters trained on very large text corpora from diverse sources. LLMs have the potential to improve healthcare due to their capability to parse complex concepts and generate context‐based responses. The interest in LLMs has not spared digestive disease academics, who have mainly investigated foundational LLM accuracy, which ranges from 25% to 90% and is influenced by the lack of standardized rules to report methodologies and results for LLM‐oriented research. In addition, a critical issue is the absence of a universally accepted definition of accuracy, varying from binary to scalar interpretations, often tied to grader expertise without reference to clinical guidelines. We address strategies and challenges to increase accuracy. In particular, LLMs can be infused with domain knowledge using Retrieval Augmented Generation (RAG) or Supervised Fine‐Tuning (SFT) with reinforcement learning from human feedback (RLHF). RAG faces challenges with in‐context window limits and accurate information retrieval from the provided context. SFT, a deeper adaptation method, is computationally demanding and requires specialized knowledge. LLMs may increase patient quality of care across the field of digestive diseases, where physicians are often engaged in screening, treatment and surveillance for a broad range of pathologies for which in‐context learning or SFT with RLHF could improve clinical decision‐making and patient outcomes. However, despite their potential, the safe deployment of LLMs in healthcare still needs to overcome hurdles in accuracy, suggesting a need for strategies that integrate human feedback with advanced model training. [ABSTRACT FROM AUTHOR]

Introduction: ChatGPT, a large‐scale language model, is a notable example of AI's potential in health care. However, its effectiveness in clinical settings, especially when compared to human physicians, is not fully understood. This study evaluates ChatGPT's capabilities and limitations in answering questions for Japanese internal medicine specialists, aiming to clarify its accuracy and tendencies in both correct and incorrect responses. Methods: We utilized ChatGPT's answers on four sets of self‐training questions for internal medicine specialists in Japan from 2020 to 2023. We ran three trials for each set to evaluate its overall accuracy and performance on nonimage questions. Subsequently, we categorized the questions into two groups: those ChatGPT consistently answered correctly (Confirmed Correct Answer, CCA) and those it consistently answered incorrectly (Confirmed Incorrect Answer, CIA). For these groups, we calculated the average accuracy rates and 95% confidence intervals based on the actual performance of internal medicine physicians on each question and analyzed the statistical significance between the two groups. This process was then similarly applied to the subset of nonimage CCA and CIA questions. Results: ChatGPT's overall accuracy rate was 59.05%, increasing to 65.76% for nonimage questions. 24.87% of the questions had answers that varied between correct and incorrect in the three trials. Despite surpassing the passing threshold for nonimage questions, ChatGPT's accuracy was lower than that of human specialists. There was a significant variance in accuracy between CCA and CIA groups, with ChatGPT mirroring human physician patterns in responding to different question types. Conclusion: This study underscores ChatGPT's potential utility and limitations in internal medicine. While effective in some aspects, its dependence on question type and context suggests that it should supplement, not replace, professional medical judgment. Further research is needed to integrate Artificial Intelligence tools like ChatGPT more effectively into specialized medical practices. [ABSTRACT FROM AUTHOR]

Objectives: Evaluate and compare the ability of large language models (LLMs) to diagnose various ailments in otolaryngology. Methods: We collected all 100 clinical vignettes from the second edition of Otolaryngology Cases—The University of Cincinnati Clinical Portfolio by Pensak et al. With the addition of the prompt "Provide a diagnosis given the following history," we prompted ChatGPT‐3.5, Google Bard, and Bing‐GPT4 to provide a diagnosis for each vignette. These diagnoses were compared to the portfolio for accuracy and recorded. All queries were run in June 2023. Results: ChatGPT‐3.5 was the most accurate model (89% success rate), followed by Google Bard (82%) and Bing GPT (74%). A chi‐squared test revealed a significant difference between the three LLMs in providing correct diagnoses (p = 0.023). Of the 100 vignettes, seven require additional testing results (i.e., biopsy, non‐contrast CT) for accurate clinical diagnosis. When omitting these vignettes, the revised success rates were 95.7% for ChatGPT‐3.5, 88.17% for Google Bard, and 78.72% for Bing‐GPT4 (p = 0.002). Conclusions: ChatGPT‐3.5 offers the most accurate diagnoses when given established clinical vignettes as compared to Google Bard and Bing‐GPT4. LLMs may accurately offer assessments for common otolaryngology conditions but currently require detailed prompt information and critical supervision from clinicians. There is vast potential in the clinical applicability of LLMs; however, practitioners should be wary of possible "hallucinations" and misinformation in responses. Level of Evidence: 3 Laryngoscope, 134:3997–4002, 2024 [ABSTRACT FROM AUTHOR]

The article critically evaluates ChatGPT as a language learning tool, focusing on its features and effectiveness. Topics include ChatGPT's ability to provide personalized language practice and immediate feedback, its user-friendly interface and accessibility options, and the differences between free and paid plans, including enhanced features for paid users.

Objective: To review the current literature on the application, accuracy, and performance of Chatbot Generative Pre‐Trained Transformer (ChatGPT) in Otolaryngology–Head and Neck Surgery. Data Sources: PubMED, Cochrane Library, and Scopus. Review Methods: A comprehensive review of the literature on the applications of ChatGPT in otolaryngology was conducted according to Preferred Reporting Items for Systematic Reviews and Meta‐analyses statement. Conclusions: ChatGPT provides imperfect patient information or general knowledge related to diseases found in Otolaryngology–Head and Neck Surgery. In clinical practice, despite suboptimal performance, studies reported that the model is more accurate in providing diagnoses, than in suggesting the most adequate additional examinations and treatments related to clinical vignettes or real clinical cases. ChatGPT has been used as an adjunct tool to improve scientific reports (referencing, spelling correction), to elaborate study protocols, or to take student or resident exams reporting several levels of accuracy. The stability of ChatGPT responses throughout repeated questions appeared high but many studies reported some hallucination events, particularly in providing scientific references. Implications for Practice: To date, most applications of ChatGPT are limited in generating disease or treatment information, and in the improvement of the management of clinical cases. The lack of comparison of ChatGPT performance with other large language models is the main limitation of the current research. Its ability to analyze clinical images has not yet been investigated in otolaryngology although upper airway tract or ear images are an important step in the diagnosis of most common ear, nose, and throat conditions. This review may help otolaryngologists to conceive new applications in further research. [ABSTRACT FROM AUTHOR]

Background Methods Results Conclusions This study evaluated the accuracy, clinical concordance, and readability of the chatbot interface generative pretrained transformer (ChatGPT) 3.5 as a source of breast cancer information for patients.Twenty questions that patients are likely to ask ChatGPT were identified by breast cancer advocates. These were posed to ChatGPT 3.5 in July 2023 and were repeated three times. Responses were graded in two domains: accuracy (4‐point Likert scale, 4 = worst) and clinical concordance (information is clinically similar to physician response; 5‐point Likert scale, 5 = not similar at all). The concordance of responses with repetition was estimated using intraclass correlation coefficient (ICC) of word counts. Response readability was calculated using the Flesch Kincaid readability scale. References were requested and verified.The overall average accuracy was 1.88 (range 1.0–3.0; 95% confidence interval [CI], 1.42–1.94), and clinical concordance was 2.79 (range 1.0–5.0; 95% CI, 1.94–3.64). The average word count was 310 words per response (range, 146–441 words per response) with high concordance (ICC, 0.75; 95% CI, 0.59–0.91; p < .001). The average readability was poor at 37.9 (range, 18.0–60.5) with high concordance (ICC, 0.73; 95% CI, 0.57–0.90; p < .001). There was a weak correlation between ease of readability and better clinical concordance (−0.15; p = .025). Accuracy did not correlate with readability (0.05; p = .079). The average number of references was 1.97 (range, 1–4; total, 119). ChatGPT cited peer‐reviewed articles only once and often referenced nonexistent websites (41%).Because ChatGPT 3.5 responses were incorrect 24% of the time and did not provide real references 41% of the time, patients should be cautioned about using ChatGPT for medical information. [ABSTRACT FROM AUTHOR]

Aims Methods Results Conclusions The aim of this study was to assess the ChatGPT‐4 (ChatGPT) large language model (LLM) on tasks relevant to community pharmacy.ChatGPT was assessed with community pharmacy‐relevant test cases involving drug information retrieval, identifying labelling errors, prescription interpretation, decision‐making under uncertainty and multidisciplinary consults. Drug information on rituximab, warfarin, and St. John's wort was queried. The decision‐support scenarios consisted of a subject with swollen eyelids and a maculopapular rash in a subject on lisinopril and ferrous sulfate. The multidisciplinary scenarios required the integration of medication management with recommendations for healthy eating and physical activity/exercise.The responses from ChatGPT for rituximab, warfarin, and St. John's wort were satisfactory and cited drug databases and drug‐specific monographs. ChatGPT identified labeling errors related to incorrect medication strength, form, route of administration, unit conversion, and directions. For the patient with inflamed eyelids, the course of action developed by ChatGPT was comparable to the pharmacist's approach. For the patient with the maculopapular rash, both the pharmacist and ChatGPT placed a drug reaction to either lisinopril or ferrous sulfate at the top of the differential. ChatGPT provided customized vaccination requirements for travel to Brazil, guidance on management of drug allergies and recovery from a knee injury. ChatGPT provided satisfactory medication management and wellness information for a diabetic on metformin and semaglutide.LLMs have the potential to become a powerful tool in community pharmacy. However, rigorous validation studies across diverse pharmacist queries, drug classes and populations, and engineering to secure patient privacy will be needed to enhance LLM utility. [ABSTRACT FROM AUTHOR]

Existing systems for automating the assessment of risk‐of‐bias (RoB) in medical studies are supervised approaches that require substantial training data to work well. However, recent revisions to RoB guidelines have resulted in a scarcity of available training data. In this study, we investigate the effectiveness of generative large language models (LLMs) for assessing RoB. Their application requires little or no training data and, if successful, could serve as a valuable tool to assist human experts during the construction of systematic reviews. Following Cochrane's latest guidelines (RoB2) designed for human reviewers, we prepare instructions that are fed as input to LLMs, which then infer the risk associated with a trial publication. We distinguish between two modelling tasks: directly predicting RoB2 from text; and employing decomposition, in which a RoB2 decision is made after the LLM responds to a series of signalling questions. We curate new testing data sets and evaluate the performance of four general‐ and medical‐domain LLMs. The results fall short of expectations, with LLMs seldom surpassing trivial baselines. On the direct RoB2 prediction test set (n = 5993), LLMs perform akin to the baselines (F1: 0.1–0.2). In the decomposition task setup (n = 28,150), similar F1 scores are observed. Our additional comparative evaluation on RoB1 data also reveals results substantially below those of a supervised system. This testifies to the difficulty of solving this task based on (complex) instructions alone. Using LLMs as an assisting technology for assessing RoB2 thus currently seems beyond their reach. [ABSTRACT FROM AUTHOR]

Background Objectives Methods Findings Conclusions Active Learning with AI‐tutoring in Higher Education tackles dropout rates.To investigate teaching‐learning methodologies preferred by students. AHP is used to evaluate a ChatGPT‐based studented learning methodology which is compared to another active learning methodology and a traditional methodology. Study with Learning Analytics to evaluate alternatives, and help students elect the best strategies according to their preferences.Comparative study of three learning methodologies in a counterbalanced Single‐Group with 33 university students. It follows a pre‐test/post‐test approach using AHP and SAM. HRV and GSR used for the estimation of emotional states.Criteria related to in‐class experiences valued higher than test‐related criteria. Chat‐GPT integration was well regarded compared to well‐established methodologies. Student emotion self‐assessment correlated with physiological measures, validating used Learning Analytics.Proposed model AI‐Tutoring classroom integration functions effectively at increasing engagement and avoiding false information. AHP with the physiological measuring allows students to determine preferred learning methodologies, avoiding biases, and acknowledging minority groups. [ABSTRACT FROM AUTHOR]

Gynecologic cancer requires personalized care to improve outcomes. Large language models (LLMs) hold the potential to provide intelligent question‐answering with reliable information about medical queries in clear and plain English, which can be understood by both healthcare providers and patients. We aimed to evaluate two freely available LLMs (ChatGPT and Google's Bard) in answering questions regarding the management of gynecologic cancer. The LLMs' performances were evaluated by developing a set questions that addressed common gynecologic oncologic findings from a patient's perspective and more complex questions to elicit recommendations from a clinician's perspective. Each question was presented to the LLM interface, and the responses generated by the artificial intelligence (AI) model were recorded. The responses were assessed based on the adherence to the National Comprehensive Cancer Network and European Society of Gynecological Oncology guidelines. This evaluation aimed to determine the accuracy and appropriateness of the information provided by LLMs. We showed that the models provided largely appropriate responses to questions regarding common cervical cancer screening tests and BRCA‐related questions. Less useful answers were received to complex and controversial gynecologic oncology cases, as assessed by reviewing the common guidelines. ChatGPT and Bard lacked knowledge of regional guideline variations, However, it provided practical and multifaceted advice to patients and caregivers regarding the next steps of management and follow up. We conclude that LLMs may have a role as an adjunct informational tool to improve outcomes. [ABSTRACT FROM AUTHOR]

Objective Methods Results Conclusion To analyze the knowledge of ChatGPT about cervical cancer (CC).Official websites of professional health institutes, and websites created by patients and charities underwent strict screening. Using CC‐related keywords, common inquiries by the public and comments about CC were searched in social media applications with these data, a list of frequently asked questions (FAQs) was prepared. When preparing question about CC, the European Society of Gynecological Oncology (ESGO), European Society for Radiotherapy and Oncology (ESTRO), and European Society of Pathology (ESP) guidelines were used. The answers given by ChatGPT were scored according to the Global Quality Score (GQS).When all ChatGPT answers to FAQs about CC were evaluated with regard to GQS, 68 ChatGPT answers were classified as score 5, and none of ChatGPT answers for FAQs were scored as 2 or 1. Moreover, ChatGPT answered 33 of 53 (62.3%) CC‐related questions based on ESGO, ESTRO, and ESP guidelines with completely accurate and satisfactory responses (GQS 5). In addition, eight answers (15.1%), seven answers (13.2%), four answers (7.5%), and one answer (1.9%) were categorized as GQS 4, GQS 3, GQS 2, and GQS 1, respectively. The reproducibility rate of ChatGPT answers about CC‐related FAQs and responses about those guideline‐based questions was 93.2% and 88.7%, respectively.ChatGPT had an accurate and satisfactory response rate for FAQs about CC with regards to GQS. However, the accuracy and quality of ChatGPT answers significantly decreased for questions based on guidelines. [ABSTRACT FROM AUTHOR]

We live in an age of artificial intelligence (AI). Open AI, an American artificial intelligence organization, claims that generative chatbots are here to stay. Although computer technology remains unevenly distributed, the presence of AI continues to be felt in many areas of daily life. Further, AI development happens in few countries whose cultural practices and beliefs tend to be embedded in AI output and functionality. Western and Global North perspectives tend to dominate conversations about AI. How might AI technology function equitably in an ethno‐racially and linguistically diverse world? This paper explores intersections of technology, coloniality, and bias to contextualize the user‐experience for individuals in groups characterized as high, medium, and low‐resourced languages. For whom does AI function in our globalizing world? Engaging with this question through a languacultural (Agar, 1994) lens, I employ a language and culture perspective to guide inquiry into AI’s output and impact, based on a children’s storybook. Specifically, I deploy Kenyan, Indonesian, and Taiwanese languacultural perspectives of three participants in a culturally cognizant reading (Washington, 2023) of a product of chatbot‐human composing, ‘Alice and Sparkle’, a children’s book illustrated by MidJourney and co‐authored by ChatGPT and Ammaar Reshi. Overall, through scrutiny of encoded cultural tropes and values inscribed within text and images, this paper proposes considerations for literacy pedagogy and research that stem from a pursuit of inclusive AI. [ABSTRACT FROM AUTHOR]

Background and aims Method Results Conclusions Alcohol use disorder (AUD) is characterized by low levels of engagement with effective treatments. Enhancing awareness of AUD treatments and how to navigate the treatment system is crucial. Many individuals use online sources (e.g. search engines) for answers to health‐related questions; web‐based results include a mix of high‐ and low‐quality information. Artificial intelligence may improve access to quality health information by providing concise, high‐quality responses to complex health‐related questions. This study evaluated the quality of ChatGPT‐4 responses to AUD‐related queries.A comprehensive list of 64 AUD‐related questions was developed through a combination of Google Trends analysis and expert consultation. ChatGPT‐4 was prompted with each question, followed by a request to provide 3–5 peer‐reviewed scientific citations supporting each response. Responses were evaluated for whether they were evidence‐based, provided a referral and provided supporting documentation.ChatGPT‐4 responded to all AUD‐related queries, with 92.2% (59/64) of responses being fully evidence‐based. Although only 12.5% (8/64) of responses included referrals to external resources, all responses (100%; 5/5) to location‐specific (‘near me’) queries directed individuals to appropriate resources like the NIAAA Treatment Navigator. Most (85.9%; 55/64) responses to the follow‐up question provided supporting documentation.ChatGPT‐4 responds to alcohol use disorder‐related questions with evidence‐based information and supporting documentation. ChatGPT‐4 could be promoted as a reasonable resource for those looking online for alcohol use disorder‐related information. [ABSTRACT FROM AUTHOR]

Paraphrase generation is a fundamental natural language processing (NLP) task that refers to the process of generating a well‐formed and coherent output sentence that exhibits both syntactic and/or lexical diversity from the input sentence, while simultaneously ensuring that the semantic similarity between the two sentences is preserved. However, the availability of high‐quality paraphrase datasets has been limited, particularly for machine‐generated sentences. In this paper, we present ParaGPT, a new paraphrase dataset of 81,000 machine‐generated sentence pairs, including 27,000 reference sentences (ChatGPT‐generated sentences), and 81,000 paraphrases obtained by using three different large language models (LLMs): ChatGPT, GPT‐3, and T5. We used ChatGPT to generate 27,000 sentences that cover a diverse array of topics and sentence structures, thus providing diverse inputs for the models. In addition, we evaluated the quality of the generated paraphrases using various automatic evaluation metrics. Furthermore, we provide insights into the strengths and drawbacks of each LLM in generating paraphrases by conducting a comparative analysis of the paraphrasing performance of the three LLMs. According to our findings, ChatGPT's performance, as per the evaluation metrics provided, was deemed impressive and commendable, owing to its higher‐than‐average scores for semantic similarity, which implies a higher degree of similarity between the generated paraphrase and the reference sentence, and its relatively lower scores for syntactic diversity, indicating a greater diversity of syntactic structures in the generated paraphrase. ParaGPT is a valuable resource for researchers working on NLP tasks like paraphrasing, text simplification, and text generation. We make the ParaGPT dataset publicly accessible to researchers, and as far as we are aware, this is the first paraphrase dataset produced based on ChatGPT. [ABSTRACT FROM AUTHOR]

ABSTRACT Generative AI including large language models (LLMs) has recently gained significant interest in the geoscience community through its versatile task‐solving capabilities including programming, arithmetic reasoning, generation of sample data, time‐series forecasting, toponym recognition, or image classification. Existing performance assessments of LLMs for spatial tasks have primarily focused on ChatGPT, whereas other chatbots received less attention. To narrow this research gap, this study conducts a zero‐shot correctness evaluation for a set of 76 spatial tasks across seven task categories assigned to four prominent chatbots, that is, ChatGPT‐4, Gemini, Claude‐3, and Copilot. The chatbots generally performed well on tasks related to spatial literacy, GIS theory, and interpretation of programming code and functions, but revealed weaknesses in mapping, code writing, and spatial reasoning. Furthermore, there was a significant difference in the correctness of results between the four chatbots. Responses from repeated tasks assigned to each chatbot showed a high level of consistency in responses with matching rates of over 80% for most task categories in the four chatbots. [ABSTRACT FROM AUTHOR]

Background Methods Results Conclusions Artificial intelligence (AI) and large language models (LLMs) transform how patients inform themselves. LLMs offer potential as educational tools, but their quality depends upon the information generated. Current literature examining AI as an informational tool in dermatology has been limited in evaluating AI's multifaceted roles and diversity of opinions. Here, we evaluate LLMs as a patient‐educational tool for Mohs micrographic surgery (MMS) in and out of the clinic utilizing an international expert panel.The most common patient MMS questions were extracted from Google and transposed into two LLMs and Google's search engine. 15 MMS surgeons evaluated the generated responses, examining their appropriateness as a patient‐facing informational platform, sufficiency of response in a clinical environment, and accuracy of content generated. Validated scales were employed to assess the comprehensibility of each response.The majority of reviewers deemed all LLM responses appropriate. 75% of responses were rated as mostly accurate or higher. ChatGPT had the highest mean accuracy. The majority of the panel deemed 33% of responses sufficient for clinical practice. The mean comprehensibility scores for all platforms indicated a required 10th‐grade reading level.LLM‐generated responses were rated as appropriate patient informational sources and mostly accurate in their content. However, these platforms may not provide sufficient information to function in a clinical environment, and complex comprehensibility may represent a barrier to utilization. As the popularity of these platforms increases, it is important for dermatologists to be aware of these limitations. [ABSTRACT FROM AUTHOR]

ABSTRACT In Parable of the Sower, Octavia Butler (1993) wrote: “Any Change may bear seeds of benefit. Seek them out. Any Change may bear seeds of harm. Beware” (p. 116). In this paper, we apply this command to a speculative examination of the consequences of text‐based generative AI (GAI) for adolescent writers, framing this examination within a socially situated “Writers‐in‐Community” model of writing (Graham, 2018), which considers writing as both an act of individual cognition and as situated within concentric circles representing nested social, material, and cultural contexts for writing. Through the lens of this model, we discuss representations of language‐related technologies in works by several well‐known authors of 20th‐century speculative fiction and contrast these speculative scenarios with examples from our recent research into student writers' use of ChatGPT and other GAI tools. Finally, we discuss (a) the limitations of these tools as lacking the ability to set goals and use these goals to compose a written work, which is a key component of an effective writing process and (b) what would be required to supporting students to write agentively in collaboration with these tools, despite these limitations. This discussion focuses on three principles: (1) centering human writers in collaborations with GAI; (2) setting writer goals to address historical, political, institutional, and social influences; and (3) critical agency in literacy with GAI. [ABSTRACT FROM AUTHOR]

With its increasing popularity, healthcare professionals and patients may use ChatGPT to obtain medication‐related information. This study was conducted to assess ChatGPT's ability to provide satisfactory responses (i.e., directly answers the question, accurate, complete and relevant) to medication‐related questions posed to an academic drug information service. ChatGPT responses were compared to responses generated by the investigators through the use of traditional resources, and references were evaluated. Thirty‐nine questions were entered into ChatGPT; the three most common categories were therapeutics (8; 21%), compounding/formulation (6; 15%) and dosage (5; 13%). Ten (26%) questions were answered satisfactorily by ChatGPT. Of the 29 (74%) questions that were not answered satisfactorily, deficiencies included lack of a direct response (11; 38%), lack of accuracy (11; 38%) and/or lack of completeness (12; 41%). References were included with eight (29%) responses; each included fabricated references. Presently, healthcare professionals and consumers should be cautioned against using ChatGPT for medication‐related information. [ABSTRACT FROM AUTHOR]

Background: The emergence of Generative Artificial Intelligence has brought a number of ethical and practical issues to higher education. Solid experimental evidence is yet inadequate to set the functional rules for the new technology. Objectives: The objective of this study is to analyse the experience of undergraduate students' interaction with ChatGPT and contribute to identifying the problems arising from the widespread use of artificial intelligence. Methods: Junior university students (N = 25) were assigned the task of working on their seminar essays with the aid of ChatGPT. Most students were novices with this tool (the study was conducted in the spring of 2023). Their essays were analysed qualitatively, according to the principles of the general inductive approach. Results and Conclusions: The initial attitudes towards artificial intelligence were almost equally distributed from enthusiastic to indifferent and cautious, with one student refusing to interact with the chatbot on ideological grounds. After the first experience, most of the students declared themselves adopters of the new technology. We have found some evidence for enhancing critical thinking competence when using ChatGPT, as well as examples of unquestioned reliance on its outputs. The tendency to personification of the chatbot was apparent in the students' essays. Implications: The findings show how easily the students embrace artificial intelligence and suggest a failure of any attempts for its strict regulation. This, on the other hand, underlines the need for emphasis on personal and research‐oriented approaches in teaching and learning. Lay Description: What is currently known about this topic: Tools such as ChatGPT have spread rapidly in the university environment, but exactly how students use them is not yet well understood. What does this paper add: We analyzed students' interactions with ChatGPT and identified a few patterns such as personification and overreliance on the AI outputs. Implications for practice or policy: Changes to education introduced by AI tools emphasize the need for personal and research‐oriented approaches in teaching and learning. [ABSTRACT FROM AUTHOR]

Aims: Recent advances in artificial intelligence, particularly with large language models like GPT‐4Vision (GPT‐4V)—a derivative feature of ChatGPT—have expanded the potential for medical image interpretation. This study evaluates the accuracy of GPT‐4V in image classification tasks of histopathological images and compares its performance with a traditional convolutional neural network (CNN). Methods: We utilised 1520 images, including haematoxylin and eosin staining and tau immunohistochemistry, from patients with various neurodegenerative diseases, such as Alzheimer's disease (AD), progressive supranuclear palsy (PSP) and corticobasal degeneration (CBD). We assessed GPT‐4V's performance using multi‐step prompts to determine how textual context influences image interpretation. We also employed few‐shot learning to enhance improvements in GPT‐4V's diagnostic performance in classifying three specific tau lesions—astrocytic plaques, neuritic plaques and tufted astrocytes—and compared the outcomes with the CNN model YOLOv8. Results: GPT‐4V accurately recognised staining techniques and tissue origin but struggled with specific lesion identification. The interpretation of images was notably influenced by the provided textual context, which sometimes led to diagnostic inaccuracies. For instance, when presented with images of the motor cortex, the diagnosis shifted inappropriately from AD to CBD or PSP. However, few‐shot learning markedly improved GPT‐4V's diagnostic capabilities, enhancing accuracy from 40% in zero‐shot learning to 90% with 20‐shot learning, matching the performance of YOLOv8, which required 100‐shot learning to achieve the same accuracy. Conclusions: Although GPT‐4V faces challenges in independently interpreting histopathological images, few‐shot learning significantly improves its performance. This approach is especially promising for neuropathology, where acquiring extensive labelled datasets is often challenging. [ABSTRACT FROM AUTHOR]

Objective: Safe home tracheostomy care requires engagement and troubleshooting by patients, who may turn to online, AI‐generated information sources. This study assessed the quality of ChatGPT responses to such queries. Methods: In this cross‐sectional study, ChatGPT was prompted with 10 hypothetical tracheostomy care questions in three domains (complication management, self‐care advice, and lifestyle adjustment). Responses were graded by four otolaryngologists for appropriateness, accuracy, and overall score. The readability of responses was evaluated using the Flesch Reading Ease (FRE) and Flesch–Kincaid Reading Grade Level (FKRGL). Descriptive statistics and ANOVA testing were performed with statistical significance set to p <.05. Results: On a scale of 1–5, with 5 representing the greatest appropriateness or overall score and a 4‐point scale with 4 representing the highest accuracy, the responses exhibited moderately high appropriateness (mean = 4.10, SD = 0.90), high accuracy (mean = 3.55, SD = 0.50), and moderately high overall scores (mean = 4.02, SD = 0.86). Scoring between response categories (self‐care recommendations, complication recommendations, lifestyle adjustments, and special device considerations) revealed no significant scoring differences. Suboptimal responses lacked nuance and contained incorrect information and recommendations. Readability indicated college and advanced levels for FRE (Mean = 39.5, SD = 7.17) and FKRGL (Mean = 13.1, SD = 1.47), higher than the sixth‐grade level recommended for patient‐targeted resources by the NIH. Conclusion: While ChatGPT‐generated tracheostomy care responses may exhibit acceptable appropriateness, incomplete or misleading information may have dire clinical consequences. Further, inappropriately high reading levels may limit patient comprehension and accessibility. At this point in its technological infancy, AI‐generated information should not be solely relied upon as a direct patient care resource. [ABSTRACT FROM AUTHOR]

Given the potential applications of generative AI (GenAI) in education and its rising interest in research, this systematic review mapped the thematic landscape of 407 publications indexed in the Web of Science, ScienceDirect and Scopus. Using EPPI Reviewer, publication type, educational level, disciplines, research areas and applications of GenAI were extracted. Eight discursive themes were identified, predominantly focused on 'application, impact and potential', 'ethical implication and risks', 'perspectives and experiences', 'institutional and individual adoption', and 'performance and intelligence'. GenAI was conceptualised as a tool for 'pedagogical enhancement', 'specialised training and practices', 'writing assistance and productivity', 'professional skills and development', and as an 'interdisciplinary learning tool'. Key gaps highlighted include a paucity of research and discussions on GenAI in K‐12 education; a limited exploration of GenAI's impact using experimental procedures; and a limited exploration of the potential and ethical concerns of GenAI from the lens of cultural dimensions. Promising opportunities for future research are highlighted. [ABSTRACT FROM AUTHOR]

Objective: This study aims to evaluate the accuracy of ChatGPT in answering allergic rhinitis (AR) and chronic rhinosinusitis (CRS) related questions. Study Design: This is a cross‐sectional study. Setting: Each question was inputted as a separate, independent prompt. Methods: Responses to AR (n = 189) and CRS (n = 242) related questions, generated by GPT‐3.5 and GPT‐4, were independently graded for accuracy by 2 senior rhinology professors, with disagreements adjudicated by a third reviewer. Results: Overall, ChatGPT demonstrated a satisfactory performance, accurately answering over 80% of questions across all categories. Specifically, GPT‐4.0's accuracy in responding to AR‐related questions significantly exceeded that of GPT‐3.5, but distinction not evident in CRS‐related questions. Patient‐originated questions had a significantly higher accuracy compared to doctor‐originated questions when utilizing GPT‐4.0 to respond to AR‐related questions. This discrepancy was not observed with GPT‐3.5 or in the context of CRS‐related questions. Across different types of content, ChatGPT excelled in covering basic knowledge, prevention, and emotion for AR and CRS. However, it experienced challenges when addressing questions about recent advancements, a trend consistent across both GPT‐3.5 and GPT‐4.0 iterations. Importantly, the accuracy of responses remained unaffected when questions were posed in Chinese. Conclusion: Our findings suggest ChatGPT's capability to convey accurate information for AR and CRS patients, and offer insights into its performance across various domains, guiding its utilization and improvement. [ABSTRACT FROM AUTHOR]

Objective Methods Results Conclusion There are no criteria for what type of manuscript and to what extent ChatGPT use is permissible in writing manuscripts. I aimed to determine which, human or ChatGPT, writes more readable letters to the editor and whether ChatGPT writes letters mimicking a certain person. I aimed to provide hints as to what makes the difference between humans and ChatGPT.This is a descriptive pilot study. I tasked ChatGPT (version 3.5) with generating a disagreement letter to my previous article (Letter 0). I wrote a letter involving three weaknesses of the addressed article (Letter 1). I provided ChatGPT with these three weaknesses and tasked it with generating a letter (Letter 2). Then, I supplied my authored letters and tasked ChatGPT with emulating my writing style (Letter 3). Eight professors evaluated the letters' readability and ChatGPT assessed which letter was more likely to be accepted.ChatGPT produced coherent letters (Letters 0 and 2). Professors rated the readability of Letters 1 and 2 similarly, finding Letter 3 less readable. ChatGPT determined that the human‐authored Letter 1 had a slightly higher acceptance likelihood than the ChatGPT‐generated Letter 2. Although ChatGPT identified personal writing styles, its mimicry did not enhance the letter's quality.This preliminary experiment indicates that human‐written letters are perceived to be as readable as, or no less readable than, ChatGPT‐generated ones. It suggests that human touch, with its inherent enthusiasm, is essential for effective letter writing. Further comprehensive investigations are warranted to ascertain the extent to which ChatGPT can be used in this domain. [ABSTRACT FROM AUTHOR]

Educators and researchers are interested in ways that ChatGPT and other generative AI tools might move beyond the role of “cheatbot” and become part of the network of resources students use for writing. We studied how high school students used ChatGPT as a writing support while writing arguments about topics like school mascots. We asked: What did students prompt ChatGPT to do? And how did students take up ChatGPT's responses to those prompts? We used Flower and Hayes' writing model to analyze screencasts of students interacting with ChatGPT and one another as they planned, drafted, and reviewed their arguments. Our data show that while planning and drafting, students primarily asked ChatGPT for ideas and then built upon those ideas to develop their own arguments. While reviewing, they generally used ChatGPT as they might use Grammarly or other editing tools. Students also compared their writing with that of ChatGPT, which allowed them to identify their unique writing voices and build meta‐level understandings of rhetorical choices and effects. Our study indicates that ChatGPT can become a part of a social, distributed model of writing, and that students can use ChatGPT as a resource for writing without sidestepping the processes of planning, drafting, and reviewing. [ABSTRACT FROM AUTHOR]

This document is a letter to the editor discussing a comparative study of two artificial intelligence tools, ChatGPT-4 and Google Lens, in the field of dermatology. The study aimed to evaluate the diagnostic capabilities of these tools compared to clinical diagnoses made by dermatologists. The study involved collecting 200 clinical photographs for each of 10 dermatological conditions, totaling 2,000 images. The results showed that ChatGPT-4 had an overall diagnostic accuracy of 70.15%, while Google Lens had an accuracy of 45.35%. The study suggests that integrating advanced AI technologies like ChatGPT-4 could enhance diagnostic processes in dermatology, particularly in resource-limited settings. However, further refinement and training may be needed for Google Lens to improve its diagnostic capabilities. [Extracted from the article]

Introduction Methods Results Conclusion Complex patient diagnoses in dentistry require a multifaceted approach which combines interpretations of clinical observations with an in‐depth understanding of patient history and presenting problems. The present study aims to elucidate the implications of ChatGPT (OpenAI) as a comprehensive diagnostic tool in the dental clinic through examining the chatbot's diagnostic performance on challenging patient cases retrieved from the literature.Our study subjected ChatGPT3.5 and ChatGPT4 to descriptions of patient cases for diagnostic challenges retrieved from the literature. Sample means were compared using a two‐tailed t‐test, while sample proportions were compared using a two‐tailed χ2 test. A p‐value below the threshold of 0.05 was deemed statistically significant.When prompted to generate their own differential diagnoses, ChatGPT3.5 and ChatGPT4 achieved a diagnostic accuracy of 40% and 62%, respectively. When basing their diagnostic processes on a differential diagnosis retrieved from the literature, ChatGPT3.5 and ChatGPT4 achieved a diagnostic accuracy of 70% and 80%, respectively.ChatGPT displays an impressive capacity to correctly diagnose complex diagnostic challenges in the field of dentistry. Our study paints a promising potential for the chatbot to 1 day serve as a comprehensive diagnostic tool in the dental clinic. [ABSTRACT FROM AUTHOR]

Summary: Background: Interest in large language models (LLMs), such as OpenAI's ChatGPT, across multiple specialties has grown as a source of patient‐facing medical advice and provider‐facing clinical decision support. The accuracy of LLM responses for gastroenterology and hepatology‐related questions is unknown. Aims: To evaluate the accuracy and potential safety implications for LLMs for the diagnosis, management and treatment of questions related to gastroenterology and hepatology. Methods: We conducted a systematic literature search including Cochrane Library, Google Scholar, Ovid Embase, Ovid MEDLINE, PubMed, Scopus and the Web of Science Core Collection to identify relevant articles published from inception until January 28, 2024, using a combination of keywords and controlled vocabulary for LLMs and gastroenterology or hepatology. Accuracy was defined as the percentage of entirely correct answers. Results: Among the 1671 reports screened, we identified 33 full‐text articles on using LLMs in gastroenterology and hepatology and included 18 in the final analysis. The accuracy of question‐responding varied across different model versions. For example, accuracy ranged from 6.4% to 45.5% with ChatGPT‐3.5 and was between 40% and 91.4% with ChatGPT‐4. In addition, the absence of standardised methodology and reporting metrics for studies involving LLMs places all the studies at a high risk of bias and does not allow for the generalisation of single‐study results. Conclusions: Current general‐purpose LLMs have unacceptably low accuracy on clinical gastroenterology and hepatology tasks, which may lead to adverse patient safety events through incorrect information or triage recommendations, which might overburden healthcare systems or delay necessary care. [ABSTRACT FROM AUTHOR]

Aim Background Introduction Methods Results Discussion Conclusion Implications for nursing and health policy This study explores the influencing factors of attitudes and behaviors toward use of ChatGPT based on the Technology Acceptance Model among registered nurses in Taiwan.The complexity of medical services and nursing shortages increases workloads. ChatGPT swiftly answers medical questions, provides clinical guidelines, and assists with patient information management, thereby improving nursing efficiency.To facilitate the development of effective ChatGPT training programs, it is essential to examine registered nurses’ attitudes toward and utilization of ChatGPT across diverse workplace settings.An anonymous online survey was used to collect data from over 1000 registered nurses recruited through social media platforms between November 2023 and January 2024. Descriptive statistics and multiple linear regression analyses were conducted for data analysis.Among respondents, some were unfamiliar with ChatGPT, while others had used it before, with higher usage among males, higher‐educated individuals, experienced nurses, and supervisors. Gender and work settings influenced perceived risks, and those familiar with ChatGPT recognized its social impact. Perceived risk and usefulness significantly influenced its adoption.Nurse attitudes to ChatGPT vary based on gender, education, experience, and role. Positive perceptions emphasize its usefulness, while risk concerns affect adoption. The insignificant role of perceived ease of use highlights ChatGPT's user‐friendly nature.Over half of the surveyed nurses had used or were familiar with ChatGPT and showed positive attitudes toward its use. Establishing rigorous guidelines to enhance their interaction with ChatGPT is crucial for future training.Nurse managers should understand registered nurses’ attitudes toward ChatGPT and integrate it into in‐service education with tailored support and training, including appropriate prompt formulation and advanced decision‐making, to prevent misuse. [ABSTRACT FROM AUTHOR]

In recent years, AI technologies have been developed to promote students' self‐regulated learning (SRL) and proactive learning in digital learning environments. This paper discusses a comparative study between generative AI‐based (SRLbot) and rule‐based AI chatbots (Nemobot) in a 3‐week science learning experience with 74 Secondary 4 students in Hong Kong. The experimental group used SRLbot to maintain a regular study habit and facilitate their SRL, while the control group utilized rule‐based AI chatbots. Results showed that SRLbot effectively enhanced students' science knowledge, behavioural engagement and motivation. Quantile regression analysis indicated that the number of interactions significantly predicted variations in SRL. Students appreciated the personalized recommendations and flexibility of SRLbot, which adjusted responses based on their specific learning and SRL scenarios. The ChatGPT‐enhanced instructional design reduced learning anxiety and promoted learning performance, motivation and sustained learning habits. Students' feedback on learning challenges, psychological support and self‐regulation behaviours provided insights into their progress and experience with this technology. SRLbot's adaptability and personalized approach distinguished it from rule‐based chatbots. The findings offer valuable evidence for AI developers and educators to consider generative AI settings and chatbot design, facilitating greater success in online science learning. Practitioner notesWhat is already known about this topic AI technologies have been used to support student self‐regulated learning (SRL) across subjects.SRL has been identified as an important aspect of student learning that can be developed through technological support.Generative AI technologies like ChatGPT have shown potential for enhancing student learning by providing personalized guidance and feedback.What this paper adds This paper reports on a case study that specifically examines the effectiveness of ChatGPT in promoting SRL among secondary students.The study provides evidence that ChatGPT can enhance students' science knowledge, motivation and SRL compared to a rule‐based AI chatbot.The study offers insights into how ChatGPT can be used as a tool to facilitate SRL and promote sustained learning habits.Implications for practice and/or policy The findings of this study suggest that educators should consider the potential of ChatGPT and other generative AI technologies to support student learning and SRL.Educators and students should be aware of the limitations of AI technologies and ensure that they are used appropriately to generate desired responses.It is also important to equip teachers and students with AI competencies to enable them to use AI for learning and teaching. [ABSTRACT FROM AUTHOR]

There is no doubt that navigating academia is a formidable challenge, particularly for those from underrepresented backgrounds who face additional barriers at every turn. In such an environment, efforts to create learning and training environments that are diverse, equitable, and inclusive can feel like an uphill battle. We believe that harnessing the power of artificial intelligence (AI) tools can help in leveling the playing field. While AI cannot supplant the need for supportive mentorship, it can serve as a vital supplement, offering guidance and assistance to those who may lack access to adequate avenues of support. Embracing AI in this context should not be stigmatized, as it may represent a vital lifeline for underrepresented individuals who often face systemic biases while forging their own paths in pursuit of success and belonging in academia. AI tools should not be gatekept from these individuals, particularly by those in positions of power and privilege within the scientific community. Instead, we argue, institutions should make a strong commitment to educating their community members on how to ethically harness these tools. [ABSTRACT FROM AUTHOR]

Background: The optimal management of distal radius fractures remains a challenge for orthopaedic surgeons. The emergence of Artificial Intelligence (AI) and Large Language Models (LLMs), especially ChatGPT, affords significant potential in improving healthcare and research. This study aims to assess the accuracy and consistency of ChatGPT's knowledge in managing distal radius fractures, with a focus on its capability to provide information for patients and assist in the decision‐making processes of orthopaedic clinicians. Methods: We presented ChatGPT with seven questions on distal radius fracture management over two sessions, resulting in 14 responses. These questions covered a range of topics, including patient inquiries and orthopaedic clinical decision‐making. We requested references for each response and involved two orthopaedic registrars and two senior orthopaedic surgeons to evaluate response accuracy and consistency. Results: All 14 responses contained a mix of both correct and incorrect information. Among the 47 cited references, 13% were accurate, 28% appeared to be fabricated, 57% were incorrect, and 2% were correct but deemed inappropriate. Consistency was observed in 71% of the responses. Conclusion: ChatGPT demonstrates significant limitations in accuracy and consistency when providing information on distal radius fractures. In its current format, it offers limited utility for patient education and clinical decision‐making. [ABSTRACT FROM AUTHOR]

Background: Kidney transplantation is a complex process requiring extensive preparation and ongoing monitoring. Artificial intelligence (AI)‐powered chatbots hold potential for providing accessible health information, but our understanding of their role in offering health advice for kidney transplantation and how individuals assess such advice remains limited. This study investigates how individuals evaluate ChatGPT's responses to kidney transplantation questions in terms of information quality and empathy, focusing on potential differences across race/ethnicity and educational backgrounds. Methods: We collected Reddit posts (N = 4624) regarding kidney transplantation and selected 86 questions to represent typical clinician inquiries. These questions were used as input prompts for ChatGPT. A total of 565 participants assessed ChatGPT's responses through online surveys, rating information quality and empathy using Likert scales. Results: Multilevel analyses (N = 2825) show that there is a significant interaction between race/ethnicity and education levels in various measures related to perceived information quality, but not perceived empathy of ChatGPT's responses: accuracy (p < 0.05); authenticity (p < 0.01); believability (p < 0.05); informativeness (p = 0.053); usefulness (p < 0.05); recognizing users' feelings (p = 0.70) and understanding feelings and situations (p = 0.65). Among non‐White individuals, higher education levels predicted higher perceived quality of ChatGPT's responses across all information quality measures. Notably, this trend was reversed for White individuals, where higher education levels led to lower perceived information quality. Conclusions: Our results highlight the importance of developing AI tools sensitive to diverse communication styles and information needs. [ABSTRACT FROM AUTHOR]

This article evaluates the use of an artificial intelligence tool called ChatGPT in the field of respiratory medicine. ChatGPT is designed to provide human-like responses to a wide range of topics, including medical inquiries. However, there are concerns about its accuracy and reliability in the medical sector, as it has not been extensively trained with biomedical datasets or vetted by medical professionals. The study found that ChatGPT provided correct answers for 63.5% of respiratory medicine questions, with higher accuracy in basic medical knowledge and lower accuracy in treatment and management. It is important to note that ChatGPT should not replace professional medical advice and should be used with caution. The article highlights the limitations of ChatGPT and suggests that it can be used as a supplementary educational tool under expert guidance. Future research should focus on improving the model's accuracy and reliability in specialized domains like respiratory medicine. [Extracted from the article]

Background: ChatGPT is a novel and online large‐scale language model used as a source providing up‐to‐date and useful health‐related knowledges to patients and clinicians. However, its performance on Helicobacter pylori infection‐related questions remain unknown. This study aimed to evaluate the accuracy of ChatGPT's responses on H. pylori‐related questions compared with that of gastroenterologists during the same period. Methods: Twenty‐five H. pylori‐related questions from five domains: Indication, Diagnostics, Treatment, Gastric cancer and prevention, and Gut Microbiota were selected based on the Maastricht VI Consensus report. Each question was tested three times with ChatGPT3.5 and ChatGPT4. Two independent H. pylori experts assessed the responses from ChatGPT, with discrepancies resolved by a third reviewer. Simultaneously, a nationwide survey with the same questions was conducted among 1279 gastroenterologists and 154 medical students. The accuracy of responses from ChatGPT3.5 and ChatGPT4 was compared with that of gastroenterologists. Results: Overall, both ChatGPT3.5 and ChatGPT4 demonstrated high accuracy, with median accuracy rates of 92% for each of the three responses, surpassing the accuracy of nationwide gastroenterologists (median: 80%) and equivalent to that of senior gastroenterologists. Compared with ChatGPT3.5, ChatGPT4 provided more concise responses with the same accuracy. ChatGPT3.5 performed well in the Indication, Treatment, and Gut Microbiota domains, whereas ChatGPT4 excelled in Diagnostics, Gastric cancer and prevention, and Gut Microbiota domains. Conclusion: ChatGPT exhibited high accuracy and reproducibility in addressing H. pylori‐related questions except the decision for H. pylori treatment, performing at the level of senior gastroenterologists and could serve as an auxiliary information tool for assisting patients and clinicians. [ABSTRACT FROM AUTHOR]