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

Providing appropriate patient education during a medical encounter remains an important area for improvement across healthcare settings. Personalized resources can offer an impactful way to improve patient understanding and satisfaction during or after a healthcare visit. ChatGPT is a novel chatbot—computer program designed to simulate conversation with humans— that has the potential to assist with care-related questions, clarify discharge instructions, help triage medical problem urgency, and could potentially be used to improve patient-clinician communication. However, due to its training methodology, ChatGPT has inherent limitations, including technical restrictions, risk of misinformation, lack of input standardization, and privacy concerns. Medicolegal liability also remains an open question for physicians interacting with this technology. Nonetheless, careful utilization of ChatGPT in clinical medicine has the potential to supplement patient education in important ways.

BACKGROUND: Qualitative methods are incredibly beneficial to the dissemination and implementation of new digital health interventions; however, these methods can be time intensive and slow down dissemination when timely knowledge from the data sources is needed in ever-changing health systems. Recent advancements in generative artificial intelligence (GenAI) and their underlying large language models (LLMs) may provide a promising opportunity to expedite the qualitative analysis of textual data, but their efficacy and reliability remain unknown. OBJECTIVE: The primary objectives of our study were to evaluate the consistency in themes, reliability of coding, and time needed for inductive and deductive thematic analyses between GenAI (ie, ChatGPT and Bard) and human coders. METHODS: The qualitative data for this study consisted of 40 brief SMS text message reminder prompts used in a digital health intervention for promoting antiretroviral medication adherence among people with HIV who use methamphetamine. Inductive and deductive thematic analyses of these SMS text messages were conducted by 2 independent teams of human coders. An independent human analyst conducted analyses following both approaches using ChatGPT and Bard. The consistency in themes (or the extent to which the themes were the same) and reliability (or agreement in coding of themes) between methods were compared. RESULTS: The themes generated by GenAI (both ChatGPT and Bard) were consistent with 71% (5/7) of the themes identified by human analysts following inductive thematic analysis. The consistency in themes was lower between humans and GenAI following a deductive thematic analysis procedure (ChatGPT: 6/12, 50%; Bard: 7/12, 58%). The percentage agreement (or intercoder reliability) for these congruent themes between human coders and GenAI ranged from fair to moderate (ChatGPT, inductive: 31/66, 47%; ChatGPT, deductive: 22/59, 37%; Bard, inductive: 20/54, 37%; Bard, deductive: 21/58, 36%). In general, ChatGPT and Bard performed similarly to each other across both types of qualitative analyses in terms of consistency of themes (inductive: 6/6, 100%; deductive: 5/6, 83%) and reliability of coding (inductive: 23/62, 37%; deductive: 22/47, 47%). On average, GenAI required significantly less overall time than human coders when conducting qualitative analysis (20, SD 3.5 min vs 567, SD 106.5 min). CONCLUSIONS: The promising consistency in the themes generated by human coders and GenAI suggests that these technologies hold promise in reducing the resource intensiveness of qualitative thematic analysis; however, the relatively lower reliability in coding between them suggests that hybrid approaches are necessary. Human coders appeared to be better than GenAI at identifying nuanced and interpretative themes. Future studies should consider how these powerful technologies can be best used in collaboration with human coders to improve the efficiency of qualitative research in hybrid approaches while also mitigating potential ethical risks that they may pose.

BackgroundPatients find technology tools to be more approachable for seeking sensitive health-related information, such as reproductive health information. The inventive conversational ability of artificial intelligence (AI) chatbots, such as ChatGPT (OpenAI Inc), offers a potential means for patients to effectively locate answers to their health-related questions digitally.ObjectiveA pilot study was conducted to compare the novel ChatGPT with the existing Google Search technology for their ability to offer accurate, effective, and current information regarding proceeding action after missing a dose of oral contraceptive pill.MethodsA sequence of 11 questions, mimicking a patient inquiring about the action to take after missing a dose of an oral contraceptive pill, were input into ChatGPT as a cascade, given the conversational ability of ChatGPT. The questions were input into 4 different ChatGPT accounts, with the account holders being of various demographics, to evaluate potential differences and biases in the responses given to different account holders. The leading question, "what should I do if I missed a day of my oral contraception birth control?" alone was then input into Google Search, given its nonconversational nature. The results from the ChatGPT questions and the Google Search results for the leading question were evaluated on their readability, accuracy, and effective delivery of information.ResultsThe ChatGPT results were determined to be at an overall higher-grade reading level, with a longer reading duration, less accurate, less current, and with a less effective delivery of information. In contrast, the Google Search resulting answer box and snippets were at a lower-grade reading level, shorter reading duration, more current, able to reference the origin of the information (transparent), and provided the information in various formats in addition to text.ConclusionsChatGPT has room for improvement in accuracy, transparency, recency, and reliability before it can equitably be implemented into health care information delivery and provide the potential benefits it poses. However, AI may be used as a tool for providers to educate their patients in preferred, creative, and efficient ways, such as using AI to generate accessible short educational videos from health care provider-vetted information. Larger studies representing a diverse group of users are needed.

Purpose: To compare the performance of licensed dentists and two software versions (3.5 legacy and 4.0) of an artificial intelligence (AI)-based chatbot (ChatGPT) answering the exam for the 2022 Certification in Implant Dentistry of the European Association for Osseointegration (EAO). Materials and Methods: The 50-question, multiple-choice exam of the EAO for the 2022 Certification in Implant Dentistry was obtained. Three groups were created based on the individual or program answering the exam: licensed dentists (D group) and two software versions of an artificial intelligence (AI)-based chatbot (ChatGPT)--3.5 legacy (ChatGPT-3.5 group) and the 4.0 version (ChatGPT-4.0 group). The EAO provided the results of the 2022 examinees (D group). For the ChatGPT groups, the 50 multiple-choice questions were introduced into both ChatGBT versions, and the answers were recorded. Pearson correlation matrix was used to analyze the linear relationship among the subgroups. The inter- and intraoperator reliability was calculated using Cronbach's alpha coefficient. One-way ANOVA and Tukey post-hoc tests were used to examine the data (α = .05). Results: ChatGPT was able to pass the exam for the 2022 Certification in Implant Dentistry of the EAO. Additionally, the software version of ChatGPT impacted the score obtained. The 4.0 version not only pass the exam but also obtained a significantly higher score than the 3.5 version and licensed dentists completing the same exam. Conclusions: The AI-based chatbot tested not only passed the exam but performed better than licensed dentists. [ABSTRACT FROM AUTHOR]

In this article, we provide a brief overview of generative artificial intelligence (GenAI) and large language models (LLMs). We then propose eight critical questions that libraries should ask when exploring this technology and its implications for their communities. We argue that libraries have a unique role in facilitating informed and responsible use of GenAI, as well as safeguarding and promoting the values of access, privacy, and intellectual freedom. [ABSTRACT FROM AUTHOR]

BACKGROUND: Amyloidosis, a rare multisystem condition, often requires complex, multidisciplinary care. Its low prevalence underscores the importance of efforts to ensure the availability of high-quality patient education materials for better outcomes. ChatGPT (OpenAI) is a large language model powered by artificial intelligence that offers a potential avenue for disseminating accurate, reliable, and accessible educational resources for both patients and providers. Its user-friendly interface, engaging conversational responses, and the capability for users to ask follow-up questions make it a promising future tool in delivering accurate and tailored information to patients. OBJECTIVE: We performed a multidisciplinary assessment of the accuracy, reproducibility, and readability of ChatGPT in answering questions related to amyloidosis. METHODS: In total, 98 amyloidosis questions related to cardiology, gastroenterology, and neurology were curated from medical societies, institutions, and amyloidosis Facebook support groups and inputted into ChatGPT-3.5 and ChatGPT-4. Cardiology- and gastroenterology-related responses were independently graded by a board-certified cardiologist and gastroenterologist, respectively, who specialize in amyloidosis. These 2 reviewers (RG and DCK) also graded general questions for which disagreements were resolved with discussion. Neurology-related responses were graded by a board-certified neurologist (AAH) who specializes in amyloidosis. Reviewers used the following grading scale: (1) comprehensive, (2) correct but inadequate, (3) some correct and some incorrect, and (4) completely incorrect. Questions were stratified by categories for further analysis. Reproducibility was assessed by inputting each question twice into each model. The readability of ChatGPT-4 responses was also evaluated using the Textstat library in Python (Python Software Foundation) and the Textstat readability package in R software (R Foundation for Statistical Computing). RESULTS: ChatGPT-4 (n=98) provided 93 (95%) responses with accurate information, and 82 (84%) were comprehensive. ChatGPT-3.5 (n=83) provided 74 (89%) responses with accurate information, and 66 (79%) were comprehensive. When examined by question category, ChatGTP-4 and ChatGPT-3.5 provided 53 (95%) and 48 (86%) comprehensive responses, respectively, to general questions (n=56). When examined by subject, ChatGPT-4 and ChatGPT-3.5 performed best in response to cardiology questions (n=12) with both models producing 10 (83%) comprehensive responses. For gastroenterology (n=15), ChatGPT-4 received comprehensive grades for 9 (60%) responses, and ChatGPT-3.5 provided 8 (53%) responses. Overall, 96 of 98 (98%) responses for ChatGPT-4 and 73 of 83 (88%) for ChatGPT-3.5 were reproducible. The readability of ChatGPT-4s responses ranged from 10th to beyond graduate US grade levels with an average of 15.5 (SD 1.9). CONCLUSIONS: Large language models are a promising tool for accurate and reliable health information for patients living with amyloidosis. However, ChatGPTs responses exceeded the American Medical Associations recommended fifth- to sixth-grade reading level. Future studies focusing on improving response accuracy and readability are warranted. Prior to widespread implementation, the technologys limitations and ethical implications must be further explored to ensure patient safety and equitable implementation.

BackgroundElectronic health record (EHR)-based patient messages can contribute to burnout. Messages with a negative tone are particularly challenging to address. In this perspective, we describe our initial evaluation of large language model (LLM)-generated responses to negative EHR patient messages and contend that using LLMs to generate initial drafts may be feasible, although refinement will be needed.MethodsA retrospective sample (n = 50) of negative patient messages was extracted from a health system EHR, de-identified, and inputted into an LLM (ChatGPT). Qualitative analyses were conducted to compare LLM responses to actual care team responses.ResultsSome LLM-generated draft responses varied from human responses in relational connection, informational content, and recommendations for next steps. Occasionally, the LLM draft responses could have potentially escalated emotionally charged conversations.ConclusionFurther work is needed to optimize the use of LLMs for responding to negative patient messages in the EHR.

ChatGPT is a publicly available chatbot released by OpenAI. Its usefulness in responding to medical questions has been assessed in several specialties, but there is limited literature in dermatology. This study seeks to understand how well ChatGPT4 can provide accurate diagnoses and appropriate workup suggestions for clinical vignettes describing common dermatologic conditions. Ten vignettes were input into ChatGPT4 representing presentations of common dermatologic conditions, written from the perspective of a physician not board-certified in dermatology. ChatGPT4 was asked to identify the top five most likely diagnoses and its recommended workup for each vignette. Responses were assessed quantitatively by calculating the percentage of correct diagnoses, with accurate diagnoses defined by three board-certified dermatologists, and qualitatively using Likert scales describing the accuracy of diagnoses and appropriateness of workups scored by eleven board-certified dermatologists. Overall, 52% of ChatGPT4's diagnoses were accurate and 62% of its recommended workup suggestions were deemed completely correct by board-certified dermatologists. ChatGPT4 was better at recommending an appropriate workup than identifying accurate diagnoses across vignettes. ChatGPT4 was able to accurately diagnose and workup common dermatologic conditions in slightly more than half of cases. ChatGPT4 was better at determining an appropriate workup than an accurate diagnosis.Keywords: artificial intelligence, ChatGPT, dermatology, diagnosis, OpenAI, workup

Since ChatGPT's release in November 2022, artificial intelligence has come into the spotlight. Inspiring both fascination and fear, chatbots have stirred debates among researchers, developers, and policy makers. The concerns range from concrete and tangible ones—which include replication of existing biases and discrimination at scale, harvesting personal data, and spreading misinformation—to more existential fears that their development will lead to machines with human-like cognitive abilities. Understanding how chatbots work and the human labor and data involved can better help evaluate the validity of concerns surrounding these systems, which although innovative, are hardly the stuff of science fiction. [ABSTRACT FROM AUTHOR]

Multiple research problems specific to advertising emerge from there, including whether and how the advertising industry should address the ethical and legal issues surrounding the possibility of AI-generated advertising content containing false or misleading information as well as strategies for designing and implementing AI-powered advertising in a socially responsible and ethical manner. In her editorial, the previous editor, Shelly Rodgers ([16]) proposed an AI classification schema to systematically understand and develop subdomains in the emerging AI advertising research field along the following three dimensions: AI type, AI function, and learning type. The lack of transparency and consumers' limited ability to understand AI technology and the mechanism and processes of AI-powered advertising would affect advertising outcomes. Since ChatGPT was released on November 30, 2022, followed by Microsoft's announcement of its artificial intelligence (AI)-powered new Bing search engine on February 7, 2023, and Google's Bard release on March 21, 2023, it seems AI generally has taken over conversations across all sectors of society. [Extracted from the article]

This research note suggests the emergence of Artificial Intelligence-powered chatbots like ChatGPT pose challenges to the future of higher education. We as a field should pay attention to issues and opportunities associated with this technology across learning, teaching, and research spaces. We propose ignoring, or being indifferent to, predictions about what technologies like Artificial Intelligence-powered chatbots can do can cause us to do "dumb things." All health and physical education teacher education faculty members should make efforts to learn about these tools to facilitate informed, solution-focused decisions about whether and where to leverage them. We highlight the importance of maintaining sociocritical perspectives when considering use of digital technologies to understand and address digital (in)equity and promote equitable practices. We conclude by emphasizing the need for field-specific consensus statements to guide ethical and appropriate use of Artificial Intelligence-powered chatbots, to ensure the value of these tools is harnessed for the good of the society. [Output by ChatGPT-3] [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: 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]