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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]

ChatGPT can revolutionize education by enhancing student engagement and making learning more personalized. Drawing on UTAUT2, this study investigated determinants of intention to use ChatGPT for educational purposes. Data were gathered from 406 Malaysian students and analyzed using a hybrid approach including "partial least squares" (PLS) and "fuzzy-set qualitative comparative analysis" (fsQCA). PLS showed that performance expectancy, effort expectancy, hedonic motivation, and learning value significantly influence the intention to use ChatGPT. Furthermore, we found that personal innovativeness and information accuracy negatively moderate the associations between ChatGPT use and its determinants. While PLS demonstrated that social influence, facilitating conditions, and habit do not affect ChatGPT use, fsQCA revealed that all factors might affect the intention to use ChatGPT. fsQCA suggested that eight combinations of factors may lead to high ChatGPT use. The results hold various implications for ChatGPT developers, instructors, and universities and provide insights for accelerating ChatGPT adoption. [ABSTRACT FROM AUTHOR]

This article explores the ethical problems arising from the use of ChatGPT as a kind of generative AI and suggests responses based on the Human-Centered Artificial Intelligence (HCAI) framework. The HCAI framework is appropriate because it understands technology above all as a tool to empower, augment, and enhance human agency while referring to human wellbeing as a "grand challenge," thus perfectly aligning itself with ethics, the science of human flourishing. Further, HCAI provides objectives, principles, procedures, and structures for reliable, safe, and trustworthy AI which we apply to our ChatGPT assessments. The main danger ChatGPT presents is the propensity to be used as a "weapon of mass deception" (WMD) and an enabler of criminal activities involving deceit. We review technical specifications to better comprehend its potentials and limitations. We then suggest both technical (watermarking, styleme, detectors, and fact-checkers) and non-technical measures (terms of use, transparency, educator considerations, HITL) to mitigate ChatGPT misuse or abuse and recommend best uses (creative writing, non-creative writing, teaching and learning). We conclude with considerations regarding the role of hu mans in ensuring the proper use of ChatGPT for individual and social wellbeing. [ABSTRACT FROM AUTHOR]

As AI agents such as ChatGPT and Microsoft Little Ice begin to demonstrate human-like communication capabilities, they can be seen to respond to people reliably, accurately, and effectively and exhibit similar characteristics to humans in fulfilling their communication responsibilities; accordingly, machine communicative responsibility perception (MCRP) has been developed. Based on mind perception theory, this article explores the intrinsic mechanism underlying MCRP and its implications. MCRP is divided into two categories, i.e., perceived functional communicative responsibility and perceived emotional communicative responsibility, and the MCRP model is validated through the roles of AI advisor and AI partner being played by AI agents. The results show that the perceived functional and emotional communicative responsibility of AI agents significantly and positively influences people's intention to interact with them on an ongoing basis; however, this effect varies across AI roles. For AI advisors, functional communicative responsibility perceptions have a greater impact, whereas for AI partners, emotional communicative responsibility perceptions are more important. [ABSTRACT FROM AUTHOR]

In addition to the popular search engines on the Internet, ChatGPT may provide accurate and reliable health information. The aim of this study was to examine whether ChatGPT's responses to frequently asked questions concerning cerebral palsy (CP) by families were reliable and useful. Google trends were used to find the most frequently searched keywords for CP. Five independent physiatrists assessed ChatGPT responses to 10 questions. Seven-point Likert-type scales were used to rate information reliability and usefulness based on whether the answer can be validated and is understandable. The median ratings for reliability of information for each question varied from 2 (very unsafe) to 5 (relatively very reliable). The median rating was 4 (reliable) for four questions. The median ratings for usefulness of information varied from 2 (very little useful) to 5 (moderately useful). The median rating was 4 (partly useful) for seven questions. Although ChatGPT appears promising as an additional tool for informing family members of individuals with CP about medical information, it should be emphasized that both consumers and health care providers should be aware of the limitations of artificial intelligence-generated information. [ABSTRACT FROM AUTHOR]

Background Society guidelines on colorectal dysplasia screening, surveillance, and endoscopic management in inflammatory bowel disease (IBD) are complex, and physician adherence to them is suboptimal. We aimed to evaluate the use of ChatGPT, a large language model, in generating accurate guideline-based recommendations for colorectal dysplasia screening, surveillance, and endoscopic management in IBD in line with European Crohn's and Colitis Organization (ECCO) guidelines. Methods 30 clinical scenarios in the form of free text were prepared and presented to three separate sessions of ChatGPT and to eight gastroenterologists (four IBD specialists and four non-IBD gastroenterologists). Two additional IBD specialists subsequently assessed all responses provided by ChatGPT and the eight gastroenterologists, judging their accuracy according to ECCO guidelines. Results ChatGPT had a mean correct response rate of 87.8%. Among the eight gastroenterologists, the mean correct response rates were 85.8% for IBD experts and 89.2% for non-IBD experts. No statistically significant differences in accuracy were observed between ChatGPT and all gastroenterologists (P =0.95), or between ChatGPT and the IBD experts and non-IBD expert gastroenterologists, respectively (P =0.82). Conclusions This study highlights the potential of language models in enhancing guideline adherence regarding colorectal dysplasia in IBD. Further investigation of additional resources and prospective evaluation in real-world settings are warranted. [ABSTRACT FROM AUTHOR]

There is an urgent need in the educational community to explore the potential impact of advanced language models powered by artificial intelligence, such as ChatGPT, on thinking skills and creativity. These models hold significant promise for enhancing teaching and learning, but it is crucial to investigate their potential effects on these critical areas of cognitive development. This study employs a narrative inquiry approach, utilising weekly written narratives and qualitative interviews for analysis, to explore and understand student teachers' experiences, perceptions, and reflections on using ChatGPT in their teaching practices. Exploring the experiences and perspectives of 12 English student teachers, this research scrutinises the influence of ChatGPT on the development of thinking skills and creativity during the practicum part of a teacher education programme. Through narratives and interviews the study uncovers the benefits, challenges, and strategies for optimising the use of ChatGPT in pre-service language teacher education. Key findings reveal the importance of human-computer co-creation, engaging in critical analysis, and fostering collaboration to maximise the educational benefits of ChatGPT. These insights contribute to the ongoing discourse on the effective integration of AI in teacher education and offer practical recommendations for educators and students navigating the complex interplay between AI tools and independent thinking. [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]

Large language models (LLMs) like ChatGPT and Bard have the potential to greatly benefit healthcare, including the field of uveitis care. LLMs can provide accessible advice for patients, helping them make decisions about when to seek medical attention or escalate their treatment. They can also assist in the diagnosis of uveitis, although more research specific to uveitis is needed. LLMs can improve the consultation experience by engaging with patients and providing empathetic responses. Additionally, they can aid in medication management and streamline administrative processes. LLMs have the potential to accelerate research in uveitis by automating tasks such as data analysis and identifying potential drug targets. However, there are ethical considerations to address, including patient autonomy, bias, transparency, and accountability. Efforts should be made to ensure equity and accessibility in the implementation of LLMs, and regulatory frameworks should be developed to ensure responsible use. The future directions of LLMs in uveitis care include integrating them with visual and auditory processing and training them in real-time. While LLMs show promise, more research is needed to develop them with reliability and replicability. [Extracted from the article]

Research on language and cognition relies extensively on psycholinguistic datasets or "norms". These datasets contain judgments of lexical properties like concreteness and age of acquisition, and can be used to norm experimental stimuli, discover empirical relationships in the lexicon, and stress-test computational models. However, collecting human judgments at scale is both time-consuming and expensive. This issue of scale is compounded for multi-dimensional norms and those incorporating context. The current work asks whether large language models (LLMs) can be leveraged to augment the creation of large, psycholinguistic datasets in English. I use GPT-4 to collect multiple kinds of semantic judgments (e.g., word similarity, contextualized sensorimotor associations, iconicity) for English words and compare these judgments against the human "gold standard". For each dataset, I find that GPT-4's judgments are positively correlated with human judgments, in some cases rivaling or even exceeding the average inter-annotator agreement displayed by humans. I then identify several ways in which LLM-generated norms differ from human-generated norms systematically. I also perform several "substitution analyses", which demonstrate that replacing human-generated norms with LLM-generated norms in a statistical model does not change the sign of parameter estimates (though in select cases, there are significant changes to their magnitude). I conclude by discussing the considerations and limitations associated with LLM-generated norms in general, including concerns of data contamination, the choice of LLM, external validity, construct validity, and data quality. Additionally, all of GPT-4's judgments (over 30,000 in total) are made available online for further analysis. [ABSTRACT FROM AUTHOR]

Virtual assistants, broadly defined as digital services designed to simulate human conversation and provide personalized responses based on user input, have the potential to improve health care by supporting clinicians and patients in terms of diagnosing and managing disease, performing administrative tasks, and supporting medical research and education. These tasks are particularly helpful in vascular surgery, where the clinical and administrative burden is high due to the rising incidence of vascular disease, the medical complexity of the patients, and the potential for innovation and care advancement. The rapid development of artificial intelligence, machine learning, and natural language processing techniques have facilitated the training of large language models, such as GPT-4 (OpenAI), which can support the development of increasingly powerful virtual assistants. These tools may support holistic, multidisciplinary, and high-quality vascular care delivery throughout the pre-, intra-, and postoperative stages. Importantly, it is critical to consider the design, safety, and challenges related to virtual assistants, including data security, ethical, and equity concerns. By combining the perspectives of patients, clinicians, data scientists, and other stakeholders when developing, implementing, and monitoring virtual assistants, there is potential to harness the power of this technology to care for vascular surgery patients more effectively. In this comprehensive review article, we introduce the concept of virtual assistants, describe potential applications of virtual assistants in vascular surgery for clinicians and patients, highlight the benefits and drawbacks of large language models, such as GPT-4, and discuss considerations around the design, safety, and challenges associated with virtual assistants in vascular surgery. [ABSTRACT FROM AUTHOR]

The advent of Chat Generative Pre-Trained Transformer (ChatGPT), a conversational artificial intelligence application, has sparked considerable interest and debate within the educational sphere. This narrative article explores the potential implications of ChatGPT on dental education, including its applications, challenges, and ethical considerations. In the realm of dental school admissions, the use of ChatGPT raises questions about academic integrity and the equitable access to resources for applicants. While it may offer benefits in aiding students with writer's block, concerns arise regarding plagiarism and the authenticity of personal statements. Furthermore, its impact on the learning experiences of dental students presents both opportunities and challenges. While ChatGPT can serve as a helpful learning tool, its use may also hinder the development of critical thinking skills and lead to issues of academic dishonesty. In the realm of research, ChatGPT holds promise for facilitating literature analysis, question-answering, and information retrieval. However, its limitations, such as the lack of domain-specific knowledge and potential for bias, must be considered. Ethical concerns surrounding AI-generated content, including misinformation and transparency, necessitate careful regulation and oversight. Looking ahead, the integration of generative AI models into dental education requires collaborative efforts to establish best practices and ethical guidelines. Future research should focus on developing tailored AI models specific to dental education and investigating their long-term effects on learning outcomes and patient care. Ultimately, as educators, it is imperative to embrace technological advancements while ensuring responsible and equitable integration into dental education curricula. [ABSTRACT FROM AUTHOR]

A consumer-focused health care model not only allows unprecedented access to information, but equally warrants consideration of the appropriateness of providing accurate patient health information. Nurses play a large role in influencing patient satisfaction following total knee arthroplasty (TKA), but they come at a cost. A specific natural language artificial intelligence (AI) model, ChatGPT (Chat Generative Pre-trained Transformer), has accumulated over 100 million users within months of launching. As such, we aimed to compare: (1) orthopaedic surgeons' evaluation of the appropriateness of the answers to the most frequently asked patient questions after TKA; and (2) patients' comfort level in answering their postoperative questions by using answers provided by arthroplasty-trained nurses and ChatGPT. We prospectively created 60 questions based on the most commonly asked patient questions following TKA. There were 3 fellowship-trained surgeons who assessed the answers provided by arthroplasty-trained nurses and ChatGPT-4 to each of the questions. The surgeons graded each set of responses based on clinical judgment as: (1) "appropriate," (2) "inappropriate" if the response contained inappropriate information, or (3) "unreliable," if the responses provided inconsistent content. Patients' comfort level and trust in AI were assessed using Research Electronic Data Capture (REDCap) hosted at our local hospital. The surgeons graded 44 out of 60 (73.3%) responses for the arthroplasty-trained nurses and 44 out of 60 (73.3%) for ChatGPT to be "appropriate." There were 4 responses graded "inappropriate" and one response graded "unreliable" provided by the nurses. For the ChatGPT response, there were 5 responses graded "inappropriate" and no responses graded "unreliable." There were 136 patients (53.8%) who were more comfortable with the answers provided by ChatGPT compared to 86 patients (34.0%) who preferred the answers from arthroplasty-trained nurses. Of the 253 patients, 233 (92.1%) were uncertain if they would trust AI to answer their postoperative questions. There were 127 patients (50.2%) who answered that if they knew the previous answer was provided by ChatGPT, their comfort level in trusting the answer would change. One potential use of ChatGPT can be found in providing appropriate answers to patient questions after TKA. At our institution, cost expenditures can potentially be minimized while maintaining patient satisfaction. Inevitably, successful implementation is dependent on the ability to provide information that is credible and in accordance with the objectives of both physicians and patients. III. [ABSTRACT FROM AUTHOR]

While multiple studies have tested the ability of large language models (LLMs), such as ChatGPT, to pass standardized medical exams at different levels of training, LLMs have never been tested on surgical sub-specialty examinations, such as the American Shoulder and Elbow Surgeons (ASES) Maintenance of Certification (MOC). The purpose of this study was to compare results of ChatGPT 3.5, GPT-4, and fellowship-trained surgeons on the 2023 ASES MOC self-assessment exam. ChatGPT 3.5 and GPT-4 were subjected to the same set of text-only questions from the ASES MOC exam, and GPT-4 was additionally subjected to image-based MOC exam questions. Question responses from both models were compared against the correct answers. Performance of both models was compared to corresponding average human performance on the same question subsets. One sided proportional z-test were utilized to analyze data. Humans performed significantly better than Chat GPT 3.5 on exclusively text-based questions (76.4% vs. 60.8%, P =.044). Humans also performed significantly better than GPT 4 on image-based questions (73.9% vs. 53.2%, P =.019). There was no significant difference between humans and GPT 4 in text-based questions (76.4% vs. 66.7%, P =.136). Accounting for all questions, humans significantly outperformed GPT-4 (75.3% vs. 60.2%, P =.012). GPT-4 did not perform statistically significantly betterer than ChatGPT 3.5 on text-only questions (66.7% vs. 60.8%, P =.268). Although human performance was overall superior, ChatGPT demonstrated the capacity to analyze orthopedic information and answer specialty-specific questions on the ASES MOC exam for both text and image-based questions. With continued advancements in deep learning, LLMs may someday rival exam performance of fellowship-trained surgeons. [ABSTRACT FROM AUTHOR]

This paper presents the architecture of a multimodal human–robot interaction control platform that leverages the advanced language capabilities of ChatGPT to facilitate more natural and engaging conversations between humans and robots. Implemented on the Pepper humanoid robot, the platform aims to enhance communication by providing a richer and more intuitive interface. The motivation behind this study is to enhance robot performance in human interaction through cutting-edge natural language processing technology, thereby improving public attitudes toward robots, fostering the development and application of robotic technology, and reducing the negative attitudes often associated with human–robot interactions. To validate the system, we conducted experiments measuring negative attitude robot scale and their robot anxiety scale scores before and after interacting with the robot. Statistical analysis of the data revealed a significant improvement in the participants' attitudes and a notable reduction in anxiety following the interaction, indicating that the system holds promise for fostering more positive human–robot relationships. [ABSTRACT FROM AUTHOR]

One of the interesting discourses that has occurred in Thailand as a result of ChatGPT is how the algorithm is exploited in a growing number of workshops and seminars aimed at teaching how to use, or to 'prompt', the algorithm so as to maximise its potential. On the one hand, the organisers of these workshops aim at profiting from the growing demand for ChatGPT. On the other, there is a sense among the Thai public that they must reap the benefit of the technology for their own purposes. This trend seems to be more prominent in Thailand than in the West, where ChatGPT is viewed more with critical outlook. In this paper, we critically examine the perception of ChatGPT in the Thai context, especially when ChatGPT is used as a topic in these workshops. We will also discuss how this situation should lead to a discussion of how ChatGPT should be regulated. [ABSTRACT FROM AUTHOR]

Large language models, e.g., Chat Generative Pre-Trained Transformer (also known as ChatGPT), are currently contributing enormously to making artificial intelligence even more popular, especially among the general population. However, such chatbot models were developed as tools to support natural language communication between humans. Problematically, it is very much a "statistical correlation machine" (correlation instead of causality), and there are indeed ethical concerns associated with the use of AI language models including ChatGPT, such as bias, privacy, and abuse. This paper highlights specific ethical concerns about ChatGPT and articulates key challenges when ChatGPT is used in various applications. Practical recommendations for different stakeholders of ChatGPT are also proposed that can serve as checklist guidelines for those applying ChatGPT in their applications. These best practice examples are expected to motivate the ethical use of ChatGPT. [ABSTRACT FROM AUTHOR]

Generative AI (GenAI) is a technology that, since the launch of ChatGPT in November 2022, has taken the world by storm. While a lot of the conversation around GenAI is hype, there are some real applications of this technology that can bring real value to businesses. There are, however, risks in applying this technology blindly that sometimes can outweigh the value it brings. This paper discusses the potential applicability of GenAI to the processes in post-trade and what impact it could have on financial institutions and their ability to meet challenges in the market, such as T+1. We also discuss the risks of implementing this technology and how these can be mitigated, as well as ensuring that all the objectives are met not only from a business perspective, but also technology and compliance. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Education Research (1680-6360) is the property of Angle Publishing Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Education Research (1680-6360) is the property of Angle Publishing Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Education Research (1680-6360) is the property of Angle Publishing Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Education Research (1680-6360) is the property of Angle Publishing Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study assesses the effectiveness of ChatGPT versus the Longman Dictionary of Contemporary English (LDOCE) in supporting English language learners in lexically challenging receptive and productive lexical tasks. With a sample of 223 university students at B2 to C1 proficiency levels, this research investigates whether a leading AI-driven chatbot or a high-quality learners' dictionary better assists learners in accurately understanding and producing English. The results reveal ChatGPT's superior performance in both task types. Efficiency, in terms of consultation speed, also favoured ChatGPT, though only in the production task. This study advocates for an integrated approach that leverages both AI, with its interactive and immediate feedback, and more traditional lexicographic tools that may foster learner autonomy and linguistic proficiency. [ABSTRACT FROM AUTHOR]

Large language models (LLMs) offer transformative opportunities in bioinformatics education for medical students by creating interactive experiences. The integration of LLMs enhances educational outcomes through providing accessible code templates, clarifying the function of coding elements, and assisting in error troubleshooting. Here, we demonstrate the practical applications of LLMs with a case study on transcriptome sequencing data processing, a vital component of medical research. However, the reliability of the content that LLMs generate requires rigorous validation. Ensuring the accuracy and appropriateness of the LLM-generated information requires integrating innovative LLMs with traditional educational methods to prepare medical students effectively for future challenges in bioinformatics. [ABSTRACT FROM AUTHOR]

Since ChatGPT exploded onto the scene in October 2022, the media has been breathless with their discussion about how it is going to impact many aspects of life and work. In order to bring this new reality of generative AI into the Introduction to Management Information Systems classroom at both the undergraduate and graduate levels, we have developed two short cases that can be completed in or out of the classroom. These cases allow students to use generative AI for an interesting and useful purpose and develop prompts to deliver the output. Students can begin to learn what generative AI can do for them, and how it can shape their future careers. We tested the perceived effectiveness of the project on students using a pre/post survey. The results are analyzed using a paired t-test and demonstrate that students increased their understanding of generative AI and prompt engineering. [ABSTRACT FROM AUTHOR]

This study explores the polarization of news content shared on Facebook compared with email using data from the New York Times' Most Emailed and Most Shared lists over 2.5 years. Employing latent Dirichlet allocation and large language models (LLMs), we find that highly polarized articles are more likely to be shared on Facebook (versus email), even after accounting for factors like topics, emotion, and article age. Additionally, distinct topic preferences emerge, with social issues dominating Facebook shares and lifestyle topics prevalent in emails. Contrary to expectations, political polarization of articles shared on Facebook did not escalate post-2020 election. We introduce a novel approach to measuring polarization of text content that leverages generative artificial intelligence models, like ChatGPT, and it is both scalable and cost effective. This research contributes to the evolving intersection of LLMs, social media, and polarization studies, shedding light on descriptive patterns of content dissemination across different digital channels. This paper was accepted by Duncan Simester, marketing. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.04134. [ABSTRACT FROM AUTHOR]

Objectives This study aims to evaluate the utility of large language models (LLMs) in healthcare, focusing on their applications in enhancing patient care through improved diagnostic, decision-making processes, and as ancillary tools for healthcare professionals. Materials and Methods We evaluated ChatGPT, GPT-4, and LLaMA in identifying patients with specific diseases using gold-labeled Electronic Health Records (EHRs) from the MIMIC-III database, covering three prevalent diseases—Chronic Obstructive Pulmonary Disease (COPD), Chronic Kidney Disease (CKD)—along with the rare condition, Primary Biliary Cirrhosis (PBC), and the hard-to-diagnose condition Cancer Cachexia. Results In patient identification, GPT-4 had near similar or better performance compared to the corresponding disease-specific Machine Learning models (F1-score ≥ 85%) on COPD, CKD, and PBC. GPT-4 excelled in the PBC use case, achieving a 4.23% higher F1-score compared to disease-specific "Traditional Machine Learning" models. ChatGPT and LLaMA3 demonstrated lower performance than GPT-4 across all diseases and almost all metrics. Few-shot prompts also help ChatGPT, GPT-4, and LLaMA3 achieve higher precision and specificity but lower sensitivity and Negative Predictive Value. Discussion The study highlights the potential and limitations of LLMs in healthcare. Issues with errors, explanatory limitations and ethical concerns like data privacy and model transparency suggest that these models would be supplementary tools in clinical settings. Future studies should improve training datasets and model designs for LLMs to gain better utility in healthcare. Conclusion The study shows that LLMs have the potential to assist clinicians for tasks such as patient identification but false positives and false negatives must be mitigated before LLMs are adequate for real-world clinical assistance. [ABSTRACT FROM AUTHOR]

Objectives To evaluate the proficiency of a HIPAA-compliant version of GPT-4 in identifying actionable, incidental findings from unstructured radiology reports of Emergency Department patients. To assess appropriateness of artificial intelligence (AI)-generated, patient-facing summaries of these findings. Materials and Methods Radiology reports extracted from the electronic health record of a large academic medical center were manually reviewed to identify non-emergent, incidental findings with high likelihood of requiring follow-up, further sub-stratified as "definitely actionable" (DA) or "possibly actionable—clinical correlation" (PA-CC). Instruction prompts to GPT-4 were developed and iteratively optimized using a validation set of 50 reports. The optimized prompt was then applied to a test set of 430 unseen reports. GPT-4 performance was primarily graded on accuracy identifying either DA or PA-CC findings, then secondarily for DA findings alone. Outputs were reviewed for hallucinations. AI-generated patient-facing summaries were assessed for appropriateness via Likert scale. Results For the primary outcome (DA or PA-CC), GPT-4 achieved 99.3% recall, 73.6% precision, and 84.5% F-1. For the secondary outcome (DA only), GPT-4 demonstrated 95.2% recall, 77.3% precision, and 85.3% F-1. No findings were "hallucinated" outright. However, 2.8% of cases included generated text about recommendations that were inferred without specific reference. The majority of True Positive AI-generated summaries required no or minor revision. Conclusion GPT-4 demonstrates proficiency in detecting actionable, incidental findings after refined instruction prompting. AI-generated patient instructions were most often appropriate, but rarely included inferred recommendations. While this technology shows promise to augment diagnostics, active clinician oversight via "human-in-the-loop" workflows remains critical for clinical implementation. [ABSTRACT FROM AUTHOR]

Objectives To evaluate demographic biases in diagnostic accuracy and health advice between generative artificial intelligence (AI) (ChatGPT GPT-4) and traditional symptom checkers like WebMD. Materials and Methods Combination symptom and demographic vignettes were developed for 27 most common symptom complaints. Standardized prompts, written from a patient perspective, with varying demographic permutations of age, sex, and race/ethnicity were entered into ChatGPT (GPT-4) between July and August 2023. In total, 3 runs of 540 ChatGPT prompts were compared to the corresponding WebMD Symptom Checker output using a mixed-methods approach. In addition to diagnostic correctness, the associated text generated by ChatGPT was analyzed for readability (using Flesch-Kincaid Grade Level) and qualitative aspects like disclaimers and demographic tailoring. Results ChatGPT matched WebMD in 91% of diagnoses, with a 24% top diagnosis match rate. Diagnostic accuracy was not significantly different across demographic groups, including age, race/ethnicity, and sex. ChatGPT's urgent care recommendations and demographic tailoring were presented significantly more to 75-year-olds versus 25-year-olds (P  < .01) but were not statistically different among race/ethnicity and sex groups. The GPT text was suitable for college students, with no significant demographic variability. Discussion The use of non-health-tailored generative AI, like ChatGPT, for simple symptom-checking functions provides comparable diagnostic accuracy to commercially available symptom checkers and does not demonstrate significant demographic bias in this setting. The text accompanying differential diagnoses, however, suggests demographic tailoring that could potentially introduce bias. Conclusion These results highlight the need for continued rigorous evaluation of AI-driven medical platforms, focusing on demographic biases to ensure equitable care. [ABSTRACT FROM AUTHOR]

Objectives Phenotyping is a core task in observational health research utilizing electronic health records (EHRs). Developing an accurate algorithm demands substantial input from domain experts, involving extensive literature review and evidence synthesis. This burdensome process limits scalability and delays knowledge discovery. We investigate the potential for leveraging large language models (LLMs) to enhance the efficiency of EHR phenotyping by generating high-quality algorithm drafts. Materials and Methods We prompted four LLMs—GPT-4 and GPT-3.5 of ChatGPT, Claude 2, and Bard—in October 2023, asking them to generate executable phenotyping algorithms in the form of SQL queries adhering to a common data model (CDM) for three phenotypes (ie, type 2 diabetes mellitus, dementia, and hypothyroidism). Three phenotyping experts evaluated the returned algorithms across several critical metrics. We further implemented the top-rated algorithms and compared them against clinician-validated phenotyping algorithms from the Electronic Medical Records and Genomics (eMERGE) network. Results GPT-4 and GPT-3.5 exhibited significantly higher overall expert evaluation scores in instruction following, algorithmic logic, and SQL executability, when compared to Claude 2 and Bard. Although GPT-4 and GPT-3.5 effectively identified relevant clinical concepts, they exhibited immature capability in organizing phenotyping criteria with the proper logic, leading to phenotyping algorithms that were either excessively restrictive (with low recall) or overly broad (with low positive predictive values). Conclusion GPT versions 3.5 and 4 are capable of drafting phenotyping algorithms by identifying relevant clinical criteria aligned with a CDM. However, expertise in informatics and clinical experience is still required to assess and further refine generated algorithms. [ABSTRACT FROM AUTHOR]

Importance The study highlights the potential and limitations of the Large Language Models (LLMs) in recognizing different states of motivation to provide appropriate information for behavior change. Following the Transtheoretical Model (TTM), we identified the major gap of LLMs in responding to certain states of motivation through validated scenario studies, suggesting future directions of LLMs research for health promotion. Objectives The LLMs-based generative conversational agents (GAs) have shown success in identifying user intents semantically. Little is known about its capabilities to identify motivation states and provide appropriate information to facilitate behavior change progression. Materials and Methods We evaluated 3 GAs, ChatGPT, Google Bard, and Llama 2 in identifying motivation states following the TTM stages of change. GAs were evaluated using 25 validated scenarios with 5 health topics across 5 TTM stages. The relevance and completeness of the responses to cover the TTM processes to proceed to the next stage of change were assessed. Results 3 GAs identified the motivation states in the preparation stage providing sufficient information to proceed to the action stage. The responses to the motivation states in the action and maintenance stages were good enough covering partial processes for individuals to initiate and maintain their changes in behavior. However, the GAs were not able to identify users' motivation states in the precontemplation and contemplation stages providing irrelevant information, covering about 20%-30% of the processes. Discussion GAs are able to identify users' motivation states and provide relevant information when individuals have established goals and commitments to take and maintain an action. However, individuals who are hesitant or ambivalent about behavior change are unlikely to receive sufficient and relevant guidance to proceed to the next stage of change. Conclusion The current GAs effectively identify motivation states of individuals with established goals but may lack support for those ambivalent towards behavior change. [ABSTRACT FROM AUTHOR]

Objective Recently, large language models (LLMs) have showcased remarkable capabilities in natural language understanding. While demonstrating proficiency in everyday conversations and question-answering (QA) situations, these models frequently struggle in domains that require precision, such as medical applications, due to their lack of domain-specific knowledge. In this article, we describe the procedure for building a powerful, open-source language model specifically designed for medicine applications, termed as PMC-LLaMA. Materials and methods We adapt a general-purpose LLM toward the medical domain, involving data-centric knowledge injection through the integration of 4.8M biomedical academic papers and 30K medical textbooks, as well as comprehensive domain-specific instruction fine-tuning, encompassing medical QA, rationale for reasoning, and conversational dialogues with 202M tokens. Results While evaluating various public medical QA benchmarks and manual rating, our lightweight PMC-LLaMA, which consists of only 13B parameters, exhibits superior performance, even surpassing ChatGPT. All models, codes, and datasets for instruction tuning will be released to the research community. Discussion Our contributions are 3-fold: (1) we build up an open-source LLM toward the medical domain. We believe the proposed PMC-LLaMA model can promote further development of foundation models in medicine, serving as a medical trainable basic generative language backbone; (2) we conduct thorough ablation studies to demonstrate the effectiveness of each proposed component, demonstrating how different training data and model scales affect medical LLMs; (3) we contribute a large-scale, comprehensive dataset for instruction tuning. Conclusion In this article, we systematically investigate the process of building up an open-source medical-specific LLM, PMC-LLaMA. [ABSTRACT FROM AUTHOR]

Objective This scoping review aims to assess the current research landscape of the application and use of large language models (LLMs) and generative Artificial Intelligence (AI), through tools such as ChatGPT in telehealth. Additionally, the review seeks to identify key areas for future research, with a particular focus on AI ethics considerations for responsible use and ensuring trustworthy AI. Materials and Methods Following the scoping review methodological framework, a search strategy was conducted across 6 databases. To structure our review, we employed AI ethics guidelines and principles, constructing a concept matrix for investigating the responsible use of AI in telehealth. Using the concept matrix in our review enabled the identification of gaps in the literature and informed future research directions. Results Twenty studies were included in the review. Among the included studies, 5 were empirical, and 15 were reviews and perspectives focusing on different telehealth applications and healthcare contexts. Benefit and reliability concepts were frequently discussed in these studies. Privacy, security, and accountability were peripheral themes, with transparency, explainability, human agency, and contestability lacking conceptual or empirical exploration. Conclusion The findings emphasized the potential of LLMs, especially ChatGPT, in telehealth. They provide insights into understanding the use of LLMs, enhancing telehealth services, and taking ethical considerations into account. By proposing three future research directions with a focus on responsible use, this review further contributes to the advancement of this emerging phenomenon of healthcare AI. [ABSTRACT FROM AUTHOR]

Objective This study evaluates ChatGPT's symptom-checking accuracy across a broad range of diseases using the Mayo Clinic Symptom Checker patient service as a benchmark. Methods We prompted ChatGPT with symptoms of 194 distinct diseases. By comparing its predictions with expectations, we calculated a relative comparative score (RCS) to gauge accuracy. Results ChatGPT's GPT-4 model achieved an average RCS of 78.8%, outperforming the GPT-3.5-turbo by 10.5%. Some specialties scored above 90%. Discussion The test set, although extensive, was not exhaustive. Future studies should include a more comprehensive disease spectrum. Conclusion ChatGPT exhibits high accuracy in symptom checking for a broad range of diseases, showcasing its potential as a medical training tool in learning health systems to enhance care quality and address health disparities. [ABSTRACT FROM AUTHOR]

ChatGPT is another large language model (LLM) vastly available for the consumers on their devices but due to its performance and ability to converse effectively, it has gained a huge popularity amongst research as well as industrial community. Recently, many studies have been published to show the effectiveness, efficiency, integration, and sentiments of chatGPT and other LLMs. In contrast, this study focuses on the important aspects that are mostly overlooked, i.e. sustainability, privacy, digital divide, and ethics and suggests that not only chatGPT but every subsequent entry in the category of conversational bots should undergo Sustainability, PrivAcy, Digital divide, and Ethics (SPADE) evaluation. This paper discusses in detail the issues and concerns raised over chatGPT in line with aforementioned characteristics. We also discuss the recent EU AI Act briefly in accordance with the SPADE evaluation. We support our hypothesis by some preliminary data collection and visualizations along with hypothesized facts. We also suggest mitigations and recommendations for each of the concerns. Furthermore, we also suggest some policies and recommendations for EU AI policy act concerning ethics, digital divide, and sustainability. [ABSTRACT FROM AUTHOR]

The significant advancements in the capabilities, reasoning, and efficiency of artificial intelligence (AI)-based tools and systems are evident. Some noteworthy examples of such tools include generative AI-based large language models (LLMs) such as generative pretrained transformer 3.5 (GPT 3.5), generative pretrained transformer 4 (GPT-4), and Bard. LLMs are versatile and effective for various tasks such as composing poetry, writing codes, generating essays, and solving puzzles. Thus far, LLMs can only effectively process text-based input. However, recent advancements have enabled them to handle multimodal inputs, such as text, images, and audio, making them highly general-purpose tools. LLMs have achieved decent performance in pattern recognition tasks (such as classification), therefore, there is a curiosity about whether general-purpose LLMs can perform comparable or even superior to specialized deep learning models (DLMs) trained specifically for a given task. In this study, we compared the performances of fine-tuned DLMs with those of general-purpose LLMs for image-based emotion recognition. We trained DLMs, namely, a convolutional neural network (CNN) (two CNN models were used: C N N 1 and C N N 2 ), ResNet50, and VGG-16 models, using an image dataset for emotion recognition, and then tested their performance on another dataset. Subsequently, we subjected the same testing dataset to two vision-enabled LLMs (LLaVa and GPT-4). The C N N 2 was found to be the superior model with an accuracy of 62% while VGG16 produced the lowest accuracy with 31%. In the category of LLMs, GPT-4 performed the best, with an accuracy of 55.81%. LLava LLM had a higher accuracy than C N N 1 and VGG16 models. The other performance metrics such as precision, recall, and F1-score followed similar trends. However, GPT-4 performed the best with small datasets. The poor results observed in LLMs can be attributed to their general-purpose nature, which, despite extensive pretraining, may not fully capture the features required for specific tasks like emotion recognition in images as effectively as models fine-tuned for those tasks. The LLMs did not surpass specialized models but achieved comparable performance, making them a viable option for specific tasks without additional training. In addition, LLMs can be considered a good alternative when the available dataset is small. [ABSTRACT FROM AUTHOR]

The field of cognitive computing, conversational AI has witnessed remarkable progress, largely driven by the development of the Generative Pre-trained Transformer (GPT) series, notably ChatGPT. These transformer-based models have revolutionized natural language understanding by effectively capturing context and long-range dependencies. In light of this, this paper conducts a comprehensive exploration of ChatGPT, encompassing its architectural design, training methodology, real-world applications, and future potential within the conversational AI landscape. The paper studies the ChatGPT ability for advanced control and responsiveness, exhibiting a superior capacity for comprehending language and generating precise, informative responses. The comprehensive survey depicts ChatGPT excels in sustaining context and engaging in multi-turn dialogues, thereby fostering more interactive and meaningful conversations. Furthermore, its adaptability for integration into various systems and scalability has broadened its applicability across diverse domains, including customer service, education, content generation, healthcare, gaming, research, and exploration. Additionally, the paper presents alternative conversational AI models, such as Amazon Codewhisperer, Google Bard (LaMDA), Microsoft Bing AI, DeepMind Sparrow, and Character AI, providing a comparative analysis that underscores ChatGPT's advantages in terms of inference capabilities and future promise. Recognizing the evolution and profound impact of ChatGPT holds paramount significance for researchers and developers at the forefront of AI innovation. In a rapidly evolving conversational AI landscape, ChatGPT emerges as a pivotal player, capable of reshaping the way we interact with AI systems across a wide array of applications. [ABSTRACT FROM AUTHOR]

This paper analyses the phenomenology and epistemology of chatbots such as ChatGPT and Bard. The computational architecture underpinning these chatbots are large language models (LLMs), which are generative artificial intelligence (AI) systems trained on a massive dataset of text extracted from the Web. We conceptualise these LLMs as multifunctional computational cognitive artifacts, used for various cognitive tasks such as translating, summarizing, answering questions, information-seeking, and much more. Phenomenologically, LLMs can be experienced as a "quasi-other"; when that happens, users anthropomorphise them. For most users, current LLMs are black boxes, i.e., for the most part, they lack data transparency and algorithmic transparency. They can, however, be phenomenologically and informationally transparent, in which case there is an interactional flow. Anthropomorphising and interactional flow can, in some users, create an attitude of (unwarranted) trust towards the output LLMs generate. We conclude this paper by drawing on the epistemology of trust and testimony to examine the epistemic implications of these dimensions. Whilst LLMs generally generate accurate responses, we observe two epistemic pitfalls. Ideally, users should be able to match the level of trust that they place in LLMs to the degree that LLMs are trustworthy. However, both their data and algorithmic opacity and their phenomenological and informational transparency can make it difficult for users to calibrate their trust correctly. The effects of these limitations are twofold: users may adopt unwarranted attitudes of trust towards the outputs of LLMs (which is particularly problematic when LLMs hallucinate), and the trustworthiness of LLMs may be undermined. [ABSTRACT FROM AUTHOR]

ChatGPT frequently appears in the media, with many predicting significant disruptions, especially in the fields of accounting and auditing. Yet research has demonstrated relatively poor performance of ChatGPT on student assessment questions. We extend this research to examine whether more recent ChatGPT models and capabilities can pass major accounting certification exams including the Certified Public Accountant (CPA), Certified Management Accountant (CMA), Certified Internal Auditor (CIA), and Enrolled Agent (EA) certification exams. We find that the ChatGPT 3.5 model cannot pass any exam (average score across all assessments of 53.1%). However, with additional enhancements, ChatGPT can pass all sections of each tested exam: moving to the ChatGPT 4 model improved scores by an average of 16.5%, providing 10-shot training improved scores an additional 6.6%, and allowing the model to use reasoning and acting (e.g., allow ChatGPT to use a calculator and other resources) improved scores an additional 8.9%. After all these improvements, ChatGPT passed all exams with an average score of 85.1%. This high performance indicates that ChatGPT has sufficient capabilities to disrupt the accounting and auditing industries, which we discuss in detail. This research provides practical insights for accounting professionals, investors, and stakeholders on how to adapt and mitigate the potential harms of this technology in accounting and auditing firms. [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]

Background: Chat generative pre-trained transformer (ChatGPT) is a publicly available extensive artificial intelligence (AI) language model that leverages deep learning to generate text that mimics human conversations. In this study, the performance of ChatGPT was assessed by offering insightful and precise answers to a series of fictional questions and emulating a preliminary consultation on blepharoplasty. Methods: ChatGPT was posed with questions derived from a blepharoplasty checklist provided by the American Society of Plastic Surgeons. Board-certified plastic surgeons and non-medical staff members evaluated the responses for accuracy, informativeness, and accessibility. Results: Nine questions were used in this study. Regarding informativeness, the average score given by board-certified plastic surgeons was significantly lower than that given by non-medical staff members (2.89 ± 0.72 vs 4.41 ± 0.71; p = 0.042). No statistically significant differences were observed in accuracy (p = 0.56) or accessibility (p = 0.11). Conclusions: Our results emphasize the effectiveness of ChatGPT in simulating doctor–patient conversations during blepharoplasty. Non-medical individuals found its responses more informative compared with the surgeons. Although limited in terms of specialized guidance, ChatGPT offers foundational surgical information. Further exploration is warranted to elucidate the broader role of AI in esthetic surgical consultations. 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]

One of the most common questions that students ask statistics advisors is 'What test should I do?' This paper explores the use of generative AI chatbots, specifically ChatGPT, as a tool to assist students, in particular those with limited experience in statistics, in selecting appropriate statistical tests for their analyses. Traditional methods, such as flowcharts and online test selectors, require at least a basic understanding of measurement scales and research design, which can be an issue for many students who have limited exposure to statistics on their courses. This research focuses on developing and refining prompts to guide ChatGPT in providing accurate and relevant statistical test recommendations. A hypothetical scenario was used to test the effectiveness of various prompts, ranging from simple, naïve questions to more sophisticated ones utilising specific prompt patterns, such as the 'context manager' and 'flipped interaction.' These patterns were selected to enhance the chatbot's responses and ensure the relevance and accuracy of the test suggestions. The findings suggest that while AI chatbots like ChatGPT can be a valuable resource for students, their effectiveness is highly dependent on the quality of the prompts used. The paper concludes with a discussion on the potential of these AI tools in educational settings, acknowledging the limitations of current technology and suggesting directions for future research and development. [ABSTRACT FROM AUTHOR]

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Copyright of Continue Education Research is the property of Harbin Normal University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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.

This study aims to reveal the role of one of the artificial intelligence (AI) techniques, "ChatGPT," in improving the educational process by following it as a teaching method for the subject of automatic analysis for students of the Chemistry Department and the subject of computer security for students of the Computer Science Department, from the fourth stage at the College of Education for Pure Science (Ibn Al-Haitham), and its impact on their computational thinking to have a good educational environment. The experimental approach was used, and the research samples were chosen intentionally by the research community. Research tools were prepared, which included a scale for CT that included 12 items and the achievement test in both scientific subjects for departments as the second tool. They reached a lot of conclusions. Accordingly, a set of recommendations were proposed. [ABSTRACT FROM AUTHOR]

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]

药理学作为高校药学学科的核心课程, 知识点繁杂且分散。教学技术的升级更新是提高教学 质量的关键。传统教学模式相对单一, 急需探索和融入更多元和有效的教学技术。该文旨在探讨新型教学技 术雨课堂和人工智能 ChatGPT 的结合应用对于药理学课程的有益效果, 同时围绕药理学课程的具体内容和药 学学生的学习特点, 调查了雨课堂与 ChatGPT 结合应用的潜在价值、实践方法和可能面临的挑战。调查结果 表明, 雨课堂和 ChatGPT 的结合应用受到学生的认可, 有利于激发学生的学习积极性, 从而获得良好的学习效 果。雨课堂与 ChatGPT 在药理学课程中的结合应用, 有望成为提高药理学教学质量和效率的有效途径。 [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]

How good a research scientist is ChatGPT? We systematically probed the capabilities of GPT-3.5 and GPT-4 across four central components of the scientific process: as a Research Librarian, Research Ethicist, Data Generator, and Novel Data Predictor, using psychological science as a testing field. In Study 1 (Research Librarian), unlike human researchers, GPT-3.5 and GPT-4 hallucinated, authoritatively generating fictional references 36.0% and 5.4% of the time, respectively, although GPT-4 exhibited an evolving capacity to acknowledge its fictions. In Study 2 (Research Ethicist), GPT-4 (though not GPT-3.5) proved capable of detecting violations like p-hacking in fictional research protocols, correcting 88.6% of blatantly presented issues, and 72.6% of subtly presented issues. In Study 3 (Data Generator), both models consistently replicated patterns of cultural bias previously discovered in large language corpora, indicating that ChatGPT can simulate known results, an antecedent to usefulness for both data generation and skills like hypothesis generation. Contrastingly, in Study 4 (Novel Data Predictor), neither model was successful at predicting new results absent in their training data, and neither appeared to leverage substantially new information when predicting more vs. less novel outcomes. Together, these results suggest that GPT is a flawed but rapidly improving librarian, a decent research ethicist already, capable of data generation in simple domains with known characteristics but poor at predicting novel patterns of empirical data to aid future experimentation. [ABSTRACT FROM AUTHOR]