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Technological innovations since the turn of the century have created opportunities to increase public participation in notice and comment rulemaking, increase the efficiency of the process, and increase the quality of the rules adopted by agencies. For some rules, online rulemaking and social media have facilitated increased public participation, but have not necessarily facilitated improvements in the quality of public comments. In addition, in some cases, the transformation of the process has created new challenges for government agencies by making it easier for supporters or opponents of rules to flood agencies with duplicative and potentially false comments to which the agencies must respond. Artificial intelligence, including ChatGPT, is the latest tech trend to create opportunities to transform notice and comment rulemaking. If ChatGPT is only used by members of the public and organizations as a tool to draft comments, it may increase the involvement of the public in the process and assist them in drafting clear and intelligible comments. However, it is unlikely to improve the quality of public comments that they provide to agencies, because it will not help them understand the type of information that agencies are seeking in public comments. In addition, if ChatGPT is used to any significant extent in drafting comments, it could create challenges for agencies by flooding agencies with significantly greater numbers of seemingly unique comments, most of which merely express sentiment, values or preferences, and many of which may include false information. There is, however, an alternative path forward for ChatGPT and artificial intelligence in notice and comment rulemaking. If agencies use ChatGPT to create tools that help members of the public understand proposed rules, the rulemaking process and the types of comments that are effective in that process, agencies may enable members of the public to provide them with more useful comments. In addition, agencies may be able to use other artificial intelligence tools to make it easier to organize and analyze the comments they receive and to notify members of the public about proposed rules that might be of interest to them. Embracing these alternative uses of artificial intelligence and ChatGPT could generate some of the gains in public participation that other technological tools have promised, but not delivered. [ABSTRACT FROM AUTHOR]

Since 2023, ChatGPT has been leading a research boom in large language models. Research on the applications of large language models in various fields is also being explored. The aim of this study was to explore the use of ChatGPT/GPT-4 for post-surgery patient follow-up after oral surgery. Thirty questions that are the most commonly asked or may be encountered during follow-up and in daily practice were collected to test ChatGPT/GPT-4′s responses. A standard prompt was used for each question. The responses given by ChatGPT/GPT-4 were evaluated by three experienced oral and maxillofacial surgeons to assess the suitability of this technology for clinical follow-up, based on the accuracy of medical knowledge and rationality of the advice in ChatGPT/GPT-4′s responses. ChatGPT/GPT-4 achieved full marks in terms of both the accuracy of its medical knowledge and the rationality of its recommendations. Additionally, ChatGPT/GPT-4 was able to accurately sense patient emotions and provide them with reassurance. In conclusion, ChatGPT/GPT-4 could be used for patient follow-up after oral surgeries, but this should be done with careful consideration of the technology's current limitations and under the guidance of healthcare professionals. [ABSTRACT FROM AUTHOR]

Purpose of Review: This comprehensive review discusses the complex relationship between Alzheimer's disease (AD) and osteoporosis, two conditions that are prevalent in the aging population and result in adverse complications on quality of life. The purpose of this review is to succinctly elucidate the many commonalities between the two conditions, including shared pathways, inflammatory and oxidative mechanisms, and hormonal deficiencies. Recent Findings: AD and osteoporosis share many aspects of their respective disease-defining pathophysiology. These commonalities include amyloid beta deposition, the Wnt/β-catenin signaling pathway, and estrogen deficiency. The shared mechanisms and risk factors associated with AD and osteoporosis result in a large percentage of patients that develop both diseases. Previous literature has established that the progression of AD increases the risk of sustaining a fracture. Recent findings demonstrate that the reverse may also be true, suggesting that a fracture early in the life course can predispose one to developing AD due to the activation of these shared mechanisms. The discovery of these commonalities further guides the development of novel therapeutics in which both conditions are targeted. Summary: This detailed review delves into the commonalities between AD and osteoporosis to uncover the shared players that bring these two seemingly unrelated conditions together. The discussion throughout this review ultimately posits that the occurrence of fractures and the mechanism behind fracture healing can predispose one to developing AD later on in life, similar to how AD patients are at an increased risk of developing fractures. By focusing on the shared mechanisms between AD and osteoporosis, one can better understand the conditions individually and as a unit, thus informing therapeutic approaches and further research. This review article is part of a series of multiple manuscripts designed to determine the utility of using artificial intelligence for writing scientific reviews. [ABSTRACT FROM AUTHOR]

Objectives Large Language Models (LLMs) have been proposed as a solution to address high volumes of Patient Medical Advice Requests (PMARs). This study addresses whether LLMs can generate high quality draft responses to PMARs that satisfies both patients and clinicians with prompt engineering. Materials and Methods We designed a novel human-involved iterative processes to train and validate prompts to LLM in creating appropriate responses to PMARs. GPT-4 was used to generate response to the messages. We updated the prompts, and evaluated both clinician and patient acceptance of LLM-generated draft responses at each iteration, and tested the optimized prompt on independent validation data sets. The optimized prompt was implemented in the electronic health record production environment and tested by 69 primary care clinicians. Results After 3 iterations of prompt engineering, physician acceptance of draft suitability increased from 62% to 84% (P  <.001) in the validation dataset (N  = 200), and 74% of drafts in the test dataset were rated as "helpful." Patients also noted significantly increased favorability of message tone (78%) and overall quality (80%) for the optimized prompt compared to the original prompt in the training dataset, patients were unable to differentiate human and LLM-generated draft PMAR responses for 76% of the messages, in contrast to the earlier preference for human-generated responses. Majority (72%) of clinicians believed it can reduce cognitive load in dealing with InBasket messages. Discussion and Conclusion Informed by clinician and patient feedback synergistically, tuning in LLM prompt alone can be effective in creating clinically relevant and useful draft responses to PMARs. [ABSTRACT FROM AUTHOR]

This article explores the application of generative artificial intelligence (AI), such as ChatGPT, in the development of customized textual materials for communication teaching. It underscores the potential of generative AI to address the scarcity of suitable learning materials and enhance teaching effectiveness. A case study of text generation for a communication theory class highlights the process and efficiency of employing generative AI to produce contextually rich and pedagogically relevant teaching resources. [ABSTRACT FROM AUTHOR]

This essay explores the nuanced impact of generative AI technologies on management and business education, framed through three paradoxes: the Expertise Paradox suggests that AI's adequate performance at lower-level tasks may weaken students' development of higher-level thinking; the Innovation Paradox states that AI's creativity aid could stifle original thinking; and the Equity Paradox highlights AI's potential to provide immense gains to experts but disproportionately harm novices. We take the position that without "sensible" AI use guidelines in management education, AI is likely to have a net-negative effect on learning. This stance is based on our trials with ChatGPT on various cognitive tasks organized around the revised Bloom's Taxonomy of learning. We identify areas where AI tools can enhance learning, such as comprehending established subject domains, as well as areas where they exhibit significant limitations, such as logical reasoning and critical thinking. We caution against the potential deskilling in critical thinking due to students' overreliance on AI for basic tasks. To alleviate these challenges, we recommend sensible AI uses by students that support skill development without fostering overreliance. We also suggest how faculty, administrators, and employers may support students in getting the most out of this new tool. [ABSTRACT FROM AUTHOR]

Background: Few studies have examined the performance of artificial intelligence (AI) content detection in scientific writing. This study evaluates the performance of publicly available AI content detectors when applied to both human-written and AI-generated scientific articles. Methods: Articles published in Annals of Surgical Oncology (ASO) during the year 2022, as well as AI-generated articles using OpenAI's ChatGPT, were analyzed by three AI content detectors to assess the probability of AI-generated content. Full manuscripts and their individual sections were evaluated. Group comparisons and trend analyses were conducted by using ANOVA and linear regression. Classification performance was determined using area under the curve (AUC). Results: A total of 449 original articles met inclusion criteria and were evaluated to determine the likelihood of being generated by AI. Each detector also evaluated 47 AI-generated articles by using titles from ASO articles. Human-written articles had an average probability of being AI-generated of 9.4% with significant differences between the detectors. Only two (0.4%) human-written manuscripts were detected as having a 0% probability of being AI-generated by all three detectors. Completely AI-generated articles were evaluated to have a higher average probability of being AI-generated (43.5%) with a range from 12.0 to 99.9%. Conclusions: This study demonstrates differences in the performance of various AI content detectors with the potential to label human-written articles as AI-generated. Any effort toward implementing AI detectors must include a strategy for continuous evaluation and validation as AI models and detectors rapidly evolve. [ABSTRACT FROM AUTHOR]

ChatGPT becomes a prominent tool for students' learning of science when students read its scientific texts. Students read to learn about climate change misinformation using ChatGPT, while they develop critical awareness of the content, linguistic features as well as nature of AI and science to comprehend these texts. In this exploratory study, we investigated students' reading performance in comprehending two ChatGPT-generated socio-scientific texts, with one focusing on cognitive-epistemic aspects of climate science and another one focusing on social-institutional aspects of climate science. We theorized such reading of ChatGPT-generated outputs as encompassing the content-interpretation, genre-reasoning and epistemic-evaluation domains. Combining Rasch partial-credit model and qualitative analysis, we explored and investigated how a total of 117 junior secondary students (grades 8 to 9) read such texts. Moreover, we also examined how 55 students' holistic reading of socio-scientific texts on climate change in a ChatGPT scenario changes after a reading-science intervention. Our findings indicate that the content-interpretation was the easiest while the epistemic-evaluation domains were the most difficult. Interestingly, after the reading-science intervention, many students developed their tentative view on nature of science when they evaluated ChatGPT's claims; while a small increase in number of students discussed reliability and non-epistemic nature of AI when they evaluated ChatGPT's claims in relation to climate change. The findings also drive a pedagogical model that improves students' holistic reading of socio-scientific texts generated by ChatGPT. [ABSTRACT FROM AUTHOR]

Purpose: This research was developed to identify artificial intelligence (AI) opportunities and challenges in higher education. Design/methodology/approach: This qualitative research was developed using the six-step thematic analysis method (Braun and Clark, 2006). Participants in this study were AI PhD students from Tehran University in 2022–2023. Purposive sampling was used to select the participants; a total of 15 AI PhD students, who were experts in this field, were selected and interviews were conducted. Findings: The authors considered the opportunities that AI creates for higher education in eight secondary subthemes (for faculty members, for students, in the teaching and learning process, for assessment, the development of educational structures, the development of research structures, the development of management structures and the development of academic culture). Correspondingly, The authors identified and categorized the challenges that AI creates for higher education. Research limitations/implications: Concerning the intended research, several limitations are significant. First, the statistical population was limited, and only people with characteristics such as being PhD students, studying at Tehran University and being experts in AI could be considered the statistical population. Second, caution should be exercised when generalizing the results due to the limited statistical population (PhD students from Tehran University). Third, the problem of accessing some students due to their participation in research grants, academic immigration, etc. Originality/value: The innovation of the current research is that the authors identified the opportunities and challenges that AI creates for higher education at different levels. The findings of this study also contribute to the enrichment of existing knowledge in the field regarding the effects of AI on the future of higher education, as researchers need more understanding of AI developments in the future of higher education. [ABSTRACT FROM AUTHOR]

As Natural Language Processing (NLP) continues to advance, driven by the emergence of sophisticated large language models such as ChatGPT, there has been a notable growth in research activity. This rapid uptake reflects increasing interest in the field and induces critical inquiries into ChatGPT's applicability in the NLP domain. This review paper systematically investigates the role of ChatGPT in diverse NLP tasks, including information extraction, Name Entity Recognition (NER), event extraction, relation extraction, Part of Speech (PoS) tagging, text classification, sentiment analysis, emotion recognition and text annotation. The novelty of this work lies in its comprehensive analysis of the existing literature, addressing a critical gap in understanding ChatGPT's adaptability, limitations, and optimal application. In this paper, we employed a systematic stepwise approach following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework to direct our search process and seek relevant studies. Our review reveals ChatGPT's significant potential in enhancing various NLP tasks. Its adaptability in information extraction tasks, sentiment analysis, and text classification showcases its ability to comprehend diverse contexts and extract meaningful details. Additionally, ChatGPT's flexibility in annotation tasks reduces manual efforts and accelerates the annotation process, making it a valuable asset in NLP development and research. Furthermore, GPT-4 and prompt engineering emerge as a complementary mechanism, empowering users to guide the model and enhance overall accuracy. Despite its promising potential, challenges persist. The performance of ChatGPT needs to be tested using more extensive datasets and diverse data structures. Subsequently, its limitations in handling domain-specific language and the need for fine-tuning in specific applications highlight the importance of further investigations to address these issues. [ABSTRACT FROM AUTHOR]

The fast evolution of artificial intelligence frameworks has resulted in the creation of increasingly sophisticated large language models (LLM), ChatGPT being the most famous one. This study paper dives into this LLM with a case study of ChatGPT's architecture and provides a thorough comparative analysis of its numerous versions, tracking its history from its conception to its most recent incarnations. This research intends to give a full knowledge of the model's history by investigating the underlying mechanisms and enhancements provided in each edition. The comparative analysis covers key aspects such as model size, training data, fine-tuning techniques, and performance metrics. Furthermore, this study evaluates the limits of ChatGPT in its many incarnations. These limitations include common sense reasoning difficulties, biased replies, verbosity, sensitivity to input wording, and others. Each constraint is investigated for potential remedies and workarounds. This research article also provides a complete analysis of the ChatGPT architecture and its progress through multiple iterations. It gives vital insights for academics, developers, and users wanting to harness the promise of ChatGPT while managing its restrictions by exploring both the model's strengths and limitations. The distinctiveness of this paper rests in its comprehensive assessment of ChatGPT's architectural development and its practical strategy for resolving the myriad difficulties in producing cohesive and contextually relevant replies. [ABSTRACT FROM AUTHOR]

AbstractPublic perceptions are the decisive factor affecting ChatGPT development. Previous research mostly employed single quantitative or qualitative methods to analyze the perceptions of ChatGPT as a whole over a period of time, ignoring the dynamics of public perception. The true perceptions of ChatGPT remain an open question. We extracted 56,769 comments from 1 December 2022 to 15 February 2023 on Sina Weibo to investigate the changes in public perceptions based on sentiment and concerns. The trend analysis results revealed that positive attitudes dominated in stage 1, but attitudes reversed and negative sentiment gradually increased. Impact, Fault, and Regret were constant topics of public concern, while there were also worries about ethics and social division. Besides, excessive media publicity on ChatGPT results in over-trust, thereby affecting human-machine interaction and triggering reflections on individualism and the weakening of subjectivity. Our findings provided valuable insights for the future development of ChatGPT. [ABSTRACT FROM AUTHOR]

In part two of this mini-series, we evaluate the range of machine-learning tools now available for application to veterinary clinical text-mining. These tools will be vital to automate extraction of information from large datasets of veterinary clinical narratives curated by projects such as the Small Animal Veterinary Surveillance Network (SAVSNET) and VetCompass, where volumes of millions of records preclude reading records and the complexities of clinical notes limit usefulness of more "traditional" text-mining approaches. We discuss the application of various machine learning techniques ranging from simple models for identifying words and phrases with similar meanings to expand lexicons for keyword searching, to the use of more complex language models. Specifically, we describe the use of language models for record annotation, unsupervised approaches for identifying topics within large datasets, and discuss more recent developments in the area of generative models (such as ChatGPT). As these models become increasingly complex it is pertinent that researchers and clinicians work together to ensure that the outputs of these models are explainable in order to instill confidence in any conclusions drawn from them. [ABSTRACT FROM AUTHOR]

Claims that GPT-4 can outperform more than 90% of human test-takers in the US Uniform Bar Examination have sparked heated debates about the impact of Generative AI (GenAI) on legal education, academic integrity, and the future of legal practice. Yet GenAI’s capabilities in broader legal examination contexts – including in jurisdictions outside the US – are unclear. This study addresses this gap by evaluating GenAI’s performance against students who took the ‘Criminal Law’ final exam at an Australian law school in Spring 2023. Various AI models and prompt engineering techniques were used to generate 10 distinct answers to the exam question. Five criminal law tutors, unaware of AI involvement, graded a mix of AI-generated and student responses. Then, the tutors were briefed on the AI-generated papers they marked and engaged in reflective semi-structured interviews. The study found that GenAI performed below the student average in questions that required detailed legal and critical analysis. However, all GenAI papers performed better than students in open-ended questions and essay writing tasks. These results provide a benchmark for the capabilities and limitations of GenAI in higher education and provide insights into the potential implications of its application to legal assessments and education, curriculum development, and the future workforce. [ABSTRACT FROM AUTHOR]

Adverse drug events (ADEs) are one of the major causes of hospital admissions and are associated with increased morbidity and mortality. Post-marketing ADE identification is one of the most important phases of drug safety surveillance. Traditionally, data sources for post-marketing surveillance mainly come from spontaneous reporting system such as the Food and Drug Administration Adverse Event Reporting System (FAERS). Social media data such as posts on X (formerly Twitter) contain rich patient and medication information and could potentially accelerate drug surveillance research. However, ADE information in social media data is usually locked in the text, making it difficult to be employed by traditional statistical approaches. In recent years, large language models (LLMs) have shown promise in many natural language processing tasks. In this study, we developed several LLMs to perform ADE classification on X data. We fine-tuned various LLMs including BERT-base, Bio_ClinicalBERT, RoBERTa, and RoBERTa-large. We also experimented ChatGPT few-shot prompting and ChatGPT fine-tuned on the whole training data. We then evaluated the model performance based on sensitivity, specificity, negative predictive value, positive predictive value, accuracy, F1-measure, and area under the ROC curve. Our results showed that RoBERTa-large achieved the best F1-measure (0.8) among all models followed by ChatGPT fine-tuned model with F1-measure of 0.75. Our feature importance analysis based on 1200 random samples and RoBERTa-Large showed the most important features are as follows: “withdrawals”/”withdrawal”, “dry”, “dealing”, “mouth”, and “paralysis”. The good model performance and clinically relevant features show the potential of LLMs in augmenting ADE detection for post-marketing drug safety surveillance. [ABSTRACT FROM AUTHOR]

Following the emergence of AI technology such as ChatGPT, young adults in the USA with limited access to school-based sexuality education are likely to utilise AI to acquire sexual knowledge. Through the application of Technology Mediation Theory, which highlights the interactions between technology and humans, we identified various potential outcomes of AI-Facilitated sexuality education. These outcomes were examined in terms of the degree to which they aligned with and/or diverged from the US National Standards of Sex Education (NSES). Beneficial outcomes included accessibility, emotionally neutral responses and impartial information, and accuracy. Conversely, detrimental outcomes include limited ability to monitor developmental appropriateness, lack of human empathy, lack of interactive learning techniques, and concerns about the accuracy of current information. To optimise the benefits, and to mitigate the potential drawbacks of ChatGPT as a form of AI-Facilitated Sexuality Education, we offer recommendations of use to AI designers, sexuality educators, parents, and users. [ABSTRACT FROM AUTHOR]

Zero-shot image classification enables the recognition of new categories without requiring additional training data, thereby enhancing the model's generalization capability when specific training are unavailable. This paper introduces a zero-shot image classification framework to recognize new categories that are unseen during training by distilling knowledge from foundation models. Specifically, we first employ ChatGPT and DALL-E to synthesize reference images of unseen categories from text prompts. Then, the test image is aligned with text and reference images using CLIP and DINO to calculate the logits. Finally, the predicted logits are aggregated according to their confidence to produce the final prediction. Experiments are conducted on multiple datasets, including MNIST, SVHN, CIFAR-10, CIFAR-100, and TinyImageNet. The results demonstrate that our method can significantly improve classification accuracy compared to previous approaches, achieving AUROC scores of over 96% across all test datasets. Our code is available at https://github.com/1134112149/MICW-ZIC. [ABSTRACT FROM AUTHOR]

Large language models such as ChatGPT have been shown to excel in solving complex math problems. However, they cannot solve basic arithmetic problems such as 758*639 = 484,362. This makes us ponder if LLMs have been trained to solve math and science problems in the right way. When a student learns math at school, she or he starts with arithmetic, then moves to word problems, polynomials, and calculus. Each skill she or he acquires will be used in the next stage to solve more advanced problems. In this paper we propose Scaffolding Learning for LLMs, which imitates how a student learns a subject in a step-by-step manner. For example, we first train an LLM to perform highly specific operations such as multiplication and division, and then apply such "skills" in a more generic task such as solving word problems. This is related to Curriculum Training, which trains a model on tasks following a specific order, such as training on easy tasks first and then gradually increases the difficulty. Our proposed approach goes from specific tasks to generic ones, which can be considered as a special case of Curriculum Training. Our empirical studies show that when an LLM has "mastered" a specific skill, only a small amount of training is required to teach it to apply the skill to a more generic application. [ABSTRACT FROM AUTHOR]

AbstractBackgroundObjectiveMethodsResultsConclusionNuclear Medicine(NM), as an inherently interdisciplinary field, integrates diverse scientific principles and advanced imaging techniques. The advent of ChatGPT, a large language model, opens new avenues for medical educational innovation. With its advanced natural language processing abilities and complex algorithms, ChatGPT holds the potential to substantially enrich medical education, particularly in NM.To investigate the current application of ChatGPT in undergraduate Nuclear Medicine Education(NME).Employing a mixed-methods sequential explanatory design, the research investigates the current status of NME, the use of ChatGPT and the attitude towards ChatGPT among teachers and students in the Second Clinical College of Chongqing Medical University.The investigation yields several salient findings: (1) Students and educators in NM face numerous challenges in the learning process; (2) ChatGPT is found to possess significant applicability and potential benefits in NME; (3) There is a pronounced inclination among respondents to adopt ChatGPT, with a keen interest in its diverse applications within the educational sphere.ChatGPT has been utilized to address the difficulties faced by undergraduates at Chongqing Medical University in NME, and has been applied in various aspects to assist learning. The findings of this survey may offer some insights into how ChatGPT can be integrated into practical medical education. [ABSTRACT FROM AUTHOR]

Sentiment analysis is a vital task in natural language processing (NLP) that aims to identify and extract the emotional states and opinions of text. In this study, we conduct a comprehensive comparison of large language models (LLMs), such as ChatGPT and Google Bard, with conventional methods in sentiment analysis. We employ a rigorous evaluation framework that covers four essential metrics: accuracy, precision, recall, and the F1-score. Our results reveal that TextBlob outperforms other methods, achieving an impressive accuracy of 69% and precision of 83%. On the other hand, Bard shows a relatively poor performance, with only 39% accuracy and 46% precision. This study offers valuable insights into the diverse capabilities of AI models in sentiment analysis. A key finding of this study is the importance of model selection according to the specific requirements of the task. Each model has its own strengths and weaknesses, which are reflected in their performance profiles. Moreover, the context in which these models operate is crucial. For instance, ChatGPT generates varied responses, Bard struggles with multiple sentences, and Robustly Optimized BERT Pretraining Approach (RoBERTa) balances precision and recall. This study also reveals the performance gap between LLMs and state-of-the-art deep learning methods. We believe this work will inspire future research and applications of ChatGPT and similar AI models in sentiment analysis and related tasks. [ABSTRACT FROM AUTHOR]

Backgrounds: The use of large language models (LLMs) in medicine can help physicians improve the quality and effectiveness of health care by increasing the efficiency of medical information management, patient care, medical research, and clinical decision-making. Methods: We collected 34 frequently asked questions about glucocorticoid-induced osteoporosis (GIOP), covering topics related to the disease's clinical manifestations, pathogenesis, diagnosis, treatment, prevention, and risk factors. We also generated 25 questions based on the 2022 American College of Rheumatology Guideline for the Prevention and Treatment of Glucocorticoid-Induced Osteoporosis (2022 ACR-GIOP Guideline). Each question was posed to the LLM (ChatGPT-3.5, ChatGPT-4, and Google Gemini), and three senior orthopedic surgeons independently rated the responses generated by the LLMs. Three senior orthopedic surgeons independently rated the answers based on responses ranging between 1 and 4 points. A total score (TS) > 9 indicated 'good' responses, 6 ≤ TS ≤ 9 indicated 'moderate' responses, and TS < 6 indicated 'poor' responses. Results: In response to the general questions related to GIOP and the 2022 ACR-GIOP Guidelines, Google Gemini provided more concise answers than the other LLMs. In terms of pathogenesis, ChatGPT-4 had significantly higher total scores (TSs) than ChatGPT-3.5. The TSs for answering questions related to the 2022 ACR-GIOP Guideline by ChatGPT-4 were significantly higher than those for Google Gemini. ChatGPT-3.5 and ChatGPT-4 had significantly higher self-corrected TSs than pre-corrected TSs, while Google Gemini self-corrected for responses that were not significantly different than before. Conclusions: Our study showed that Google Gemini provides more concise and intuitive responses than ChatGPT-3.5 and ChatGPT-4. ChatGPT-4 performed significantly better than ChatGPT3.5 and Google Gemini in terms of answering general questions about GIOP and the 2022 ACR-GIOP Guidelines. ChatGPT3.5 and ChatGPT-4 self-corrected better than Google Gemini. [ABSTRACT FROM AUTHOR]

Background: Recurrent and metastatic head and neck squamous cell carcinoma (HNSCC) is characterized by a complex therapeutic management that needs to be discussed in multidisciplinary tumor boards (MDT). While artificial intelligence (AI) improved significantly to assist healthcare professionals in making informed treatment decisions for primary cases, an application in the even more complex recurrent/metastatic setting has not been evaluated yet. This study also represents the first evaluation of the recently published LLM ChatGPT 4o, compared to ChatGPT 4.0 for providing therapy recommendations. Methods: The therapy recommendations for 100 HNSCC cases generated by each LLM, 50 cases of recurrence and 50 cases of distant metastasis were evaluated by two independent reviewers. The primary outcome measured was the quality of the therapy recommendations measured by the following parameters: clinical recommendation, explanation, and summarization. Results: In this study, ChatGPT 4o and 4.0 provided mostly general answers for surgery, palliative care, or systemic therapy. ChatGPT 4o proved to be 48.5% faster than ChatGPT 4.0. For clinical recommendation, explanation, and summarization both LLMs obtained high scores in terms of performance of therapy recommendations, with no significant differences between both LLMs, but demonstrated to be mostly an assisting tool, requiring validation by an experienced clinician due to a lack of transparency and sometimes recommending treatment modalities that are not part of the current treatment guidelines. Conclusion: This research demonstrates that ChatGPT 4o and 4.0 share a similar performance, while ChatGPT 4o is significantly faster. Since the current versions cannot tailor therapy recommendations, and sometimes recommend incorrect treatment options and lack information on the source material, advanced AI models at the moment can merely assist in the MDT setting for recurrent/metastatic HNSCC. [ABSTRACT FROM AUTHOR]

Background: ChatGPT is a free artificial intelligence (AI) language model developed and released by OpenAI in late 2022. This study aimed to evaluate the performance of ChatGPT to accurately answer clinical questions (CQs) on the Guideline for the Management of Blepharoptosis published by the American Society of Plastic Surgeons (ASPS) in 2022. Methods: CQs in the guideline were used as question sources in both English and Japanese. For each question, ChatGPT provided answers for CQs, evidence quality, recommendation strength, reference match, and answered word counts. We compared the performance of ChatGPT in each component between English and Japanese queries. Results: A total of 11 questions were included in the final analysis, and ChatGPT answered 61.3% of these correctly. ChatGPT demonstrated a higher accuracy rate in English answers for CQs compared to Japanese answers for CQs (76.4% versus 46.4%; p = 0.004) and word counts (123 words versus 35.9 words; p = 0.004). No statistical differences were noted for evidence quality, recommendation strength, and reference match. A total of 697 references were proposed, but only 216 of them (31.0%) existed. Conclusions: ChatGPT demonstrates potential as an adjunctive tool in the management of blepharoptosis. However, it is crucial to recognize that the existing AI model has distinct limitations, and its primary role should be to complement the expertise of medical professionals. Level of Evidence V: Observational study under respected authorities. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266. [ABSTRACT FROM AUTHOR]

Background: Artificial intelligence (AI) has emerged as a powerful tool in various medical fields, including plastic surgery. This study aims to evaluate the performance of ChatGPT, an AI language model, in elucidating historical aspects of plastic surgery and identifying potential avenues for innovation. Methods: A comprehensive analysis of ChatGPT's responses to a diverse range of plastic surgery-related inquiries was performed. The quality of the AI-generated responses was assessed based on their relevance, accuracy, and novelty. Additionally, the study examined the AI's ability to recognize gaps in existing knowledge and propose innovative solutions. ChatGPT's responses were analysed by specialist plastic surgeons with extensive research experience, and quantitatively analysed with a Likert scale. Results: ChatGPT demonstrated a high degree of proficiency in addressing a wide array of plastic surgery-related topics. The AI-generated responses were found to be relevant and accurate in most cases. However, it demonstrated convergent thinking and failed to generate genuinely novel ideas to revolutionize plastic surgery. Instead, it suggested currently popular trends that demonstrate great potential for further advancements. Some of the references presented were also erroneous as they cannot be validated against the existing literature. Conclusion: Although ChatGPT requires major improvements, this study highlights its potential as an effective tool for uncovering novel aspects of plastic surgery and identifying areas for future innovation. By leveraging the capabilities of AI language models, plastic surgeons may drive advancements in the field. Further studies are needed to cautiously explore the integration of AI-driven insights into clinical practice and to evaluate their impact on patient outcomes. Level of Evidence V: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 [ABSTRACT FROM AUTHOR]

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]

Background: There is a dearth of artificial intelligence (AI) development and research dedicated to pediatric radiology. The newest iterations of large language models (LLMs) like ChatGPT can process image and video input in addition to text. They are thus theoretically capable of providing impressions of input radiological images. Objective: To assess the ability of multimodal LLMs to interpret pediatric radiological images. Materials and methods: Thirty medically significant cases were collected and submitted to GPT-4 (OpenAI, San Francisco, CA), Gemini 1.5 Pro (Google, Mountain View, CA), and Claude 3 Opus (Anthropic, San Francisco, CA) with a short history for a total of 90 images. AI responses were recorded and independently assessed for accuracy by a resident and attending physician. 95% confidence intervals were determined using the adjusted Wald method. Results: Overall, the models correctly diagnosed 27.8% (25/90) of images (95% CI=19.5–37.8%), were partially correct for 13.3% (12/90) of images (95% CI=2.7–26.4%), and were incorrect for 58.9% (53/90) of images (95% CI=48.6–68.5%). Conclusion: Multimodal LLMs are not yet capable of interpreting pediatric radiological images. [ABSTRACT FROM AUTHOR]

Background: ChatGPT, a recently developed artificial intelligence (AI) chatbot, has demonstrated improved performance in examinations in the medical field. However, thus far, an overall evaluation of the potential of ChatGPT models (ChatGPT-3.5 and GPT-4) in a variety of national health licensing examinations is lacking. This study aimed to provide a comprehensive assessment of the ChatGPT models' performance in national licensing examinations for medical, pharmacy, dentistry, and nursing research through a meta-analysis. Methods: Following the PRISMA protocol, full-text articles from MEDLINE/PubMed, EMBASE, ERIC, Cochrane Library, Web of Science, and key journals were reviewed from the time of ChatGPT's introduction to February 27, 2024. Studies were eligible if they evaluated the performance of a ChatGPT model (ChatGPT-3.5 or GPT-4); related to national licensing examinations in the fields of medicine, pharmacy, dentistry, or nursing; involved multiple-choice questions; and provided data that enabled the calculation of effect size. Two reviewers independently completed data extraction, coding, and quality assessment. The JBI Critical Appraisal Tools were used to assess the quality of the selected articles. Overall effect size and 95% confidence intervals [CIs] were calculated using a random-effects model. Results: A total of 23 studies were considered for this review, which evaluated the accuracy of four types of national licensing examinations. The selected articles were in the fields of medicine (n = 17), pharmacy (n = 3), nursing (n = 2), and dentistry (n = 1). They reported varying accuracy levels, ranging from 36 to 77% for ChatGPT-3.5 and 64.4–100% for GPT-4. The overall effect size for the percentage of accuracy was 70.1% (95% CI, 65–74.8%), which was statistically significant (p < 0.001). Subgroup analyses revealed that GPT-4 demonstrated significantly higher accuracy in providing correct responses than its earlier version, ChatGPT-3.5. Additionally, in the context of health licensing examinations, the ChatGPT models exhibited greater proficiency in the following order: pharmacy, medicine, dentistry, and nursing. However, the lack of a broader set of questions, including open-ended and scenario-based questions, and significant heterogeneity were limitations of this meta-analysis. Conclusions: This study sheds light on the accuracy of ChatGPT models in four national health licensing examinations across various countries and provides a practical basis and theoretical support for future research. Further studies are needed to explore their utilization in medical and health education by including a broader and more diverse range of questions, along with more advanced versions of AI chatbots. [ABSTRACT FROM AUTHOR]

AbstractChatGPT, a type of generative artificial intelligence, recently became the fastest-growing app of all time, with over 100 million users. The increasing use of this chatbot has led educators to fear that its use will cause various problems, including cheating. However, using ChatGPT can lead to desirable outcomes. Teachers can use this technology to make their tasks easier to complete and to encourage students to think critically. This article focuses on how educators can implement ChatGPT so that students benefit. Three strategies are included to achieve this goal. The first is to prohibit the use of ChatGPT when its use will likely impede learning. The second focuses on using this chatbot to encourage students to learn and think critically, and the third involves the importance of implementing an ethical approach. [ABSTRACT FROM AUTHOR]

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]

AbstractArtificial intelligence (AI) tools are becoming increasingly prevalent in various fields, including foreign language education. Understanding foreign language students’ and teachers’ perceptions, experiences, ideas, and suggestions is crucial for effectively integrating these tools into language learning. To this end, this case study examines the perceptions of 14 English as a Foreign Language instructors and 13 students regarding the integration of ChatGPT in a foreign language school over a year. Data were collected through focus groups and one-to-one interviews and analyzed using reflexive thematic analysis. The findings suggest that generative AI chatbots can enhance language skills, offer diverse learning materials, and foster inclusiveness and equity in foreign language learning. However, concerns were raised about plagiarism and potential negative impacts on students’ cognitive skills. [ABSTRACT FROM AUTHOR]

Chat Generative Pretrained Transformer (ChatGPT; OpenAI) is a state-of-the-art large language model that can simulate human-like conversations based on user input. We evaluated the performance of GPT-4 V in the Japanese National Clinical Engineer Licensing Examination using 2,155 questions from 2012 to 2023. The average correct answer rate for all questions was 86.0%. In particular, clinical medicine, basic medicine, medical materials, biological properties, and mechanical engineering achieved a correct response rate of ≥ 90%. Conversely, medical device safety management, electrical and electronic engineering, and extracorporeal circulation obtained low correct answer rates ranging from 64.8% to 76.5%. The correct answer rates for questions that included figures/tables, required numerical calculation, figure/table ∩ calculation, and knowledge of Japanese Industrial Standards were 55.2%, 85.8%, 64.2% and 31.0%, respectively. The reason for the low correct answer rates is that ChatGPT lacked recognition of the images and knowledge of standards and laws. This study concludes that careful attention is required when using ChatGPT because several of its explanations lack the correct description. [ABSTRACT FROM AUTHOR]

Recent advances in artificial intelligence (AI) give chatbots, e.g. ChatGPT, more human-like interaction and conversational capability. AI chatbots are becoming more popular for supporting language learning. Most current review research disregards the significance of teachers’ roles in chatbot-assisted language learning. Self-determination theory (SDT) explains the roles by suggesting how teachers satisfy student needs and how chatbots thwart those needs. Therefore, this systemic review aims to: (i) suggest the roles that teachers play in student English learning with AI chatbot; (ii) discuss how those roles satisfy SDT needs of the students; and (iii) discuss the challenges in this learning. This review selected 23 articles published throughout the last ten years (2014–2023). The findings offer (i) four empirical and theoretical contributions: technological pedagogical content knowledge (TPACK) for chatbots, needs satisfaction from teachers, needs thwarting from chatbots, and SDT-based research designs for chatbots; (ii) two practical suggestions: understanding technological knowledge of chatbots and SDT needs support for chatbots; and (iii) six research directions: experimental studies, student language proficiency, more teacher roles, revisiting TPACK, future development of chatbots, and large language model teacher-like chatbots. Overall, the findings enhance our knowledge of TPACK and teacher digital and AI competences for chatbot-assisted language learning. [ABSTRACT FROM AUTHOR]

AbstractSince the launch of ChatGPT in November 2022, the number of services providing generative AI has been steadily increasing. As different services enter the market, the generative AI interfaces users experience become more diverse. However, none of the services has yet established itself as the dominant tool, and which interface component affects user preferences the most has yet to be identified. This study investigates user preferences based on the interface of generative AI services currently on the market. We investigated users’ preferred interface components by setting the output data type, generative style, output variation, reference style, and generation history provided by the current generative AI service as properties. We collected data from 500 users through a survey and conducted conjoint analysis. Users preferred the provision of 10 generation histories the most, and the second most preferred the provision of reference style in footnote format. In addition, it was found that there was no preference for the creative generative style, which can be interpreted as users being aware of the problem of hallucination in generative AI. The results of this study will help future generative AI services design interfaces that consider user experience. [ABSTRACT FROM AUTHOR]

The value of generative AI tools in higher education has received considerable attention. Although there are many proponents of its value as a learning tool, many are concerned with the issues regarding academic integrity and its use by students to compose written assessments. This study evaluates and compares the output of three commonly used generative AI tools, ChatGPT, Bing and Bard. Each AI tool was prompted with an essay question from undergraduate (UG) level 4 (year 1), level 5 (year 2), level 6 (year 3) and postgraduate (PG) level 7 biomedical sciences courses. Anonymised AI generated output was then evaluated by four independent markers, according to specifed marking criteria and matched to the Frameworks for Higher Education Qualifcations (FHEQ) of UK level descriptors. Percentage scores and ordinal grades were given for each marking criteria across AI generated papers, inter-rater reliability was calculated using Kendall's coefcient of concordance and generative AI performance ranked. Across all UG and PG levels, ChatGPT performed better than Bing or Bard in areas of scientifc accuracy, scientifc detail and context. All AI tools performed consistently well at PG level compared to UG level, although only ChatGPT consistently met levels of high attainment at all UG levels. ChatGPT and Bing did not provide adequate references, while Bing falsifed references. In conclusion, generative AI tools are useful for providing scientifc information consistent with the academic standards required of students in written assignments. These fndings have broad implications for the design, implementation and grading of written assessments in higher education. [ABSTRACT FROM AUTHOR]

There is a strong demand for science, technology, engineering, and mathematics (STEM) education in Africa, which is crucial in driving the industrial revolution. Efforts to promote STEM education have led to innovations in the teaching–learning process where emerging technologies are injected into instructional processes. Artificial intelligence (AI) has been at the forefront of changing approaches to instructional activities in higher education. In African higher education, academics and undergraduates utilize AI tools for various purposes. After consulting various studies, a systematic literature review approach was used to examine the strategic goals of AI integration among STEM academics and undergraduates in African higher education. The systematic review was carried out using the PRISMA procedure. Our objective was to identify the existing gaps and challenges to provide research guidance to aspiring researchers seeking to contribute significantly to integrating AI into STEM education in African higher education. We searched for reports covering ten years (2015–2024) on the topic, but we could identify and analyze only 12 available studies that were published within three years (2022–2024). Based on our findings, AI tools are strategically utilized by STEM academics in African higher education to engage students in learning activities, administrative processes, information searches, content generation, paraphrasing academic content, grammar checks, teaching, and research. Similarly, STEM undergraduates utilize AI tools for information searching, self-learning, content generation, paraphrasing, grammar checking, and research. The most used AI tool by both STEM academics and undergraduates for various purposes is the ChatGPT. We hold an optimistic position that AI literacy advocacy and advancements in research on adopting AI tools for STEM education in African higher education will enhance the output of STEM education, contributing to the pursuit of sustainable development driven by African higher education. [ABSTRACT FROM AUTHOR]

Background: Uveitis is the ophthalmic subfield dealing with a broad range of intraocular inflammatory diseases. With the raising importance of LLM such as ChatGPT and their potential use in the medical field, this research explores the strengths and weaknesses of its applicability in the subfield of uveitis. Methods: A series of highly clinically relevant questions were asked three consecutive times (attempts 1, 2 and 3) of the LLM regarding current uveitis cases. The answers were classified on whether they were accurate and sufficient, partially accurate and sufficient or inaccurate and insufficient. Statistical analysis included descriptive analysis, normality distribution, non-parametric test and reliability tests. References were checked for their correctness in different medical databases. Results: The data showed non-normal distribution. Data between subgroups (attempts 1, 2 and 3) was comparable (Kruskal-Wallis H test, p-value = 0.7338). There was a moderate agreement between attempt 1 and attempt 2 (Cohen's kappa, ĸ = 0.5172) as well as between attempt 2 and attempt 3 (Cohen's kappa, ĸ = 0.4913). There was a fair agreement between attempt 1 and attempt 3 (Cohen's kappa, ĸ = 0.3647). The average agreement was moderate (Cohen's kappa, ĸ = 0.4577). Between the three attempts together, there was a moderate agreement (Fleiss' kappa, ĸ = 0.4534). A total of 52 references were generated by the LLM. 22 references (42.3%) were found to be accurate and correctly cited. Another 22 references (42.3%) could not be located in any of the searched databases. The remaining 8 references (15.4%) were found to exist, but were either misinterpreted or incorrectly cited by the LLM. Conclusion: Our results demonstrate the significant potential of LLMs in uveitis. However, their implementation requires rigorous training and comprehensive testing for specific medical tasks. We also found out that the references made by ChatGPT 4.o were in most cases incorrect. LLMs are likely to become invaluable tools in shaping the future of ophthalmology, enhancing clinical decision-making and patient care. [ABSTRACT FROM AUTHOR]

Introduction: The increasing reliance on technology within higher education necessitates a thorough examination of artificial intelligence's (AI) application in academic research. This analysis aims to elucidate both the advantages and challenges associated with AI utilization, thereby paving the way for future inquiries. Such studies will be instrumental in delineating strategies for the effective integration of AI tools in scholarly research, ensuring their optimal use in advancing the field. Objective: The purpose of this research is to identify the benefits and challenges of the use of AI in the field of scientific research by analyzing experiences that have implemented AI in scientific research carried out at the university level through a systematic literature review. Method: The research questions that guided the systematic literature review were as follows: (1) What are the benefits of using AI in research? (2) What are the challenges of using AI in research? (3) What are the use and benefits of AI in scientific writing including limitations? (4) What are the main lines of research identified in studies that address scientific practice with artificial intelligence in the university context? The articles analyzed were published in 2023. After applying the inclusion and exclusion criteria, 85 articles were analyzed. Results: The analysis allowed findings such as the usefulness of ChatGPT in different disciplinary areas, challenges such as being able to identify artificial intelligence resources limitations and benefits such as being able to make processes of different kinds more efficient. Discussion: It was possible to establish that although the studies analyzed identified advantages in the application of AI in scientific research, it was also detected that it is necessary to have a critical and creative look to make use of AI resources, such as ChatGPT, in order to use them only as support tools and thus be able to take care of the rigor and quality in the elaboration of scientific texts. [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]

Large language models (LLMs) are generating interest in medical settings. For example, LLMs can respond coherently to medical queries by providing plausible differential diagnoses based on clinical notes. However, there are many questions to explore, such as evaluating differences between open- and closed-source LLMs as well as LLM performance on queries from both medical and non-medical users. In this study, we assessed multiple LLMs, including Llama-2-chat, Vicuna, Medllama2, Bard/Gemini, Claude, ChatGPT3.5, and ChatGPT-4, as well as non-LLM approaches (Google search and Phenomizer) regarding their ability to identify genetic conditions from textbook-like clinician questions and their corresponding layperson translations related to 63 genetic conditions. For open-source LLMs, larger models were more accurate than smaller LLMs: 7b, 13b, and larger than 33b parameter models obtained accuracy ranges from 21%–49%, 41%–51%, and 54%–68%, respectively. Closed-source LLMs outperformed open-source LLMs, with ChatGPT-4 performing best (89%–90%). Three of 11 LLMs and Google search had significant performance gaps between clinician and layperson prompts. We also evaluated how in-context prompting and keyword removal affected open-source LLM performance. Models were provided with 2 types of in-context prompts: list-type prompts, which improved LLM performance, and definition-type prompts, which did not. We further analyzed removal of rare terms from descriptions, which decreased accuracy for 5 of 7 evaluated LLMs. Finally, we observed much lower performance with real individuals' descriptions; LLMs answered these questions with a maximum 21% accuracy. Large language models (LLMs) are rapidly changing the biomedical landscape. We compared the ability of multiple LLMs and other search techniques in identifying genetic conditions from text descriptions, including medical and non-medical (colloquial) descriptions. We also investigated the effects of different prompting approaches and the removal of rare terms. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has proven to be a valuable tool for education. It might perform different functions to facilitate teaching and learning experiences, with a learning companion being one of them. However, teachers often have little understanding of the pool of opportunities provided by AI-powered tools. Having a learning companion in a language classroom is crucial. However, getting involved in human–computer interaction requires understanding of the services that the machine can offer, whether it is a text written as a response to students' input, feedback provided to help students gauge their language level or translation prompt helping students understand the reading text. This research aims at displaying the range of AI services that are applicable in the foreign language classroom and grouping them according to their functionality. The authors selected 150 available in open access online instruments that apply AI and evaluated them against the criteria, which allowed them to organize these tools into categories according to their possible applications in the classroom. This might help teachers make informed decisions about the appropriate tool for certain teaching situations or recommend to their students a tool that could enhance the learning experience. [ABSTRACT FROM AUTHOR]

AbstractThis article reports on three digital information literacy initiatives created by instruction librarians to support students’ use of generative AI technologies, namely ChatGPT, in academic library research. The cumulative and formative data gathered from the initiatives reveals a continuing need for academic libraries to provide information literacy instruction that guides students toward the ethical use of information and awareness of using generative AI tools in library research. Moreover, analysis of the cumulative data gathered from Mentimeter anonymous surveys has led to the further reshaping of our existing information literacy program as students’ AI literacy develops in their use of AI tools at California State University, Los Angeles. [ABSTRACT FROM AUTHOR]

Objective: The integration of artificial intelligence (AI) in healthcare education is inevitable. Understanding the proficiency of generative AI in different languages to answer complex questions is crucial for educational purposes. The study objective was to compare the performance ChatGPT-4 and Gemini in answering Virology multiple-choice questions (MCQs) in English and Arabic, while assessing the quality of the generated content. Both AI models' responses to 40 Virology MCQs were assessed for correctness and quality based on the CLEAR tool designed for evaluation of AI-generated content. The MCQs were classified into lower and higher cognitive categories based on the revised Bloom's taxonomy. The study design considered the METRICS checklist for the design and reporting of generative AI-based studies in healthcare. Results: ChatGPT-4 and Gemini performed better in English compared to Arabic, with ChatGPT-4 consistently surpassing Gemini in correctness and CLEAR scores. ChatGPT-4 led Gemini with 80% vs. 62.5% correctness in English compared to 65% vs. 55% in Arabic. For both AI models, superior performance in lower cognitive domains was reported. Both ChatGPT-4 and Gemini exhibited potential in educational applications; nevertheless, their performance varied across languages highlighting the importance of continued development to ensure the effective AI integration in healthcare education globally. [ABSTRACT FROM AUTHOR]

Background: Cervical cancer (CC) and breast cancer (BC) threaten women's well-being, influenced by health-related stigma and a lack of reliable information, which can cause late diagnosis and early death. ChatGPT is likely to become a key source of health information, although quality concerns could also influence health-seeking behaviours. Methods: This cross-sectional online survey compared ChatGPT's responses to five physicians specializing in mammography and five specializing in gynaecology. Twenty frequently asked questions about CC and BC were asked on 26th and 29th of April, 2023. A panel of seven experts assessed the accuracy, consistency, and relevance of ChatGPT's responses using a 7-point Likert scale. Responses were analyzed for readability, reliability, and efficiency. ChatGPT's responses were synthesized, and findings are presented as a radar chart. Results: ChatGPT had an accuracy score of 7.0 (range: 6.6-7.0) for CC and BC questions, surpassing the highest-scoring physicians (P < 0.05). ChatGPT took an average of 13.6 s (range: 7.6-24.0) to answer each of the 20 questions presented. Readability was comparable to that of experts and physicians involved, but ChatGPT generated more extended responses compared to physicians. The consistency of repeated answers was 5.2 (range: 3.4-6.7). With different contexts combined, the overall ChatGPT relevance score was 6.5 (range: 4.8-7.0). Radar plot analysis indicated comparably good accuracy, efficiency, and to a certain extent, relevance. However, there were apparent inconsistencies, and the reliability and readability be considered inadequate. Conclusions: ChatGPT shows promise as an initial source of information for CC and BC. ChatGPT is also highly functional and appears to be superior to physicians, and aligns with expert consensus, although there is room for improvement in readability, reliability, and consistency. Future efforts should focus on developing advanced ChatGPT models explicitly designed to improve medical practice and for those with concerns about symptoms. [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]