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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]

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]

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]

Generative Artificial Intelligence (GenAI) caught Health Professions Education (HPE) institutions off-guard, and they are currently adjusting to a changed educational environment. On the horizon, however, is Artificial General Intelligence (AGI) which promises to be an even greater leap and challenge. This Guide begins by explaining the context and nature of AGI, including its characteristics of multi-modality, generality, adaptability, autonomy, and learning ability. It then explores the implications of AGI on students (including personalised learning and electronic tutors) and HPE institutions, and considers some of the context provided by AGI in healthcare. It then raises the problems to address, including the impact on employment, social risks, student adaptability, costs, quality, and others. After considering a possible timeline, the Guide then ends by indicating some first steps that HPE institutions and educators can take to prepare for AGI. [ABSTRACT FROM AUTHOR]

Purpose: To assess the diagnostic performance of two chatbots, ChatGPT and Glass, in uveitis diagnosis compared to renowned uveitis specialists, and evaluate clinicians' perception about utilizing artificial intelligence (AI) in ophthalmology practice. Methods: Six cases were presented to uveitis experts, ChatGPT (version 3.5 and 4.0) and Glass 1.0, and diagnostic accuracy was analyzed. Additionally, a survey about the emotions, confidence in utilizing AI-based tools, and the likelihood of incorporating such tools in clinical practice was done. Results: Uveitis experts accurately diagnosed all cases (100%), while ChatGPT achieved a diagnostic success rate of 66% and Glass 1.0 achieved 33%. Most attendees felt excited or optimistic about utilizing AI in ophthalmology practice. Older age and high level of education were positively correlated with increased inclination to adopt AI-based tools. Conclusions: ChatGPT demonstrated promising diagnostic capabilities in uveitis cases and ophthalmologist showed enthusiasm for the integration of AI into clinical practice. [ABSTRACT FROM AUTHOR]

Introduction: Accurate diagnosis and timely management are vital for favorable uveitis outcomes. Artificial Intelligence (AI) holds promise in medical decision-making, particularly in ophthalmology. Yet, the diagnostic precision and management advice from AI-based uveitis chatbots lack assessment. Methods: We appraised diagnostic accuracy and management suggestions of an AI-based chatbot, ChatGPT, versus five uveitis-trained ophthalmologists, using 25 standard cases aligned with new Uveitis Nomenclature guidelines. Participants predicted likely diagnoses, two differentials, and next management steps. Comparative success rates were computed. Results: Ophthalmologists excelled (60–92%) in likely diagnosis, exceeding AI (60%). Considering fully and partially accurate diagnoses, ophthalmologists achieved 76–100% success; AI attained 72%. Despite an 8% AI improvement, its overall performance lagged. Ophthalmologists and AI agreed on diagnosis in 48% cases, with 91.6% exhibiting concurrence in management plans. Conclusions: The study underscores AI chatbots' potential in uveitis diagnosis and management, indicating their value in reducing diagnostic errors. Further research is essential to enhance AI chatbot precision in diagnosis and recommendations. [ABSTRACT FROM AUTHOR]

This article describes an experiment to use the ChatGPT Large Language Model as a tool to refine Schema.org metadata. ChatGPT was asked to give suggestions to improve a preexisting package of Schema.org structured metadata in the NMSU Library homepage for search engine optimization. A package of reformatted metadata based on ChatGPT's recommendations was used to replace the preexisting metadata for seven weeks and relevant web stats are compared to an equivalent seven-week period from the preceding semester. This article discusses ChatGPT's recommendations in some depth and examines the outcomes from a theoretical perspective. Implications of the experiment are outlined along with future areas for research. [ABSTRACT FROM AUTHOR]

Background Artificial intelligence chatbots have demonstrated potential to enhance clinical decision-making and streamline health care workflows, potentially alleviating administrative burdens. However, the contribution of AI chatbots to radiologic decision-making for clinical scenarios remains insufficiently explored. This study evaluates the accuracy and reliability of four prominent Large Language Models (LLMs)—Microsoft Bing, Claude, ChatGPT 3.5, and Perplexity—in offering clinical decision support for initial imaging for suspected pulmonary embolism (PE). Methods Open-ended (OE) and select-all-that-apply (SATA) questions were crafted, covering four variants of case scenarios of PE in-line with the American College of Radiology Appropriateness Criteria. These questions were presented to the LLMs by three radiologists from diverse geographical regions and setups. The responses were evaluated based on established scoring criteria, with a maximum achievable score of 2 points for OE responses and 1 point for each correct answer in SATA questions. To enable comparative analysis, scores were normalized (score divided by the maximum achievable score). Result In OE questions, Perplexity achieved the highest accuracy (0.83), while Claude had the lowest (0.58), with Bing and ChatGPT each scoring 0.75. For SATA questions, Bing led with an accuracy of 0.96, Perplexity was the lowest at 0.56, and both Claude and ChatGPT scored 0.6. Overall, OE questions saw higher scores (0.73) compared to SATA (0.68). There is poor agreement among radiologists' scores for OE (Intraclass Correlation Coefficient [ICC] = −0.067, p = 0.54), while there is strong agreement for SATA (ICC = 0.875, p < 0.001). Conclusion The study revealed variations in accuracy across LLMs for both OE and SATA questions. Perplexity showed superior performance in OE questions, while Bing excelled in SATA questions. OE queries yielded better overall results. The current inconsistencies in LLM accuracy highlight the importance of further refinement before these tools can be reliably integrated into clinical practice, with a need for additional LLM fine-tuning and judicious selection by radiologists to achieve consistent and reliable support for decision-making. [ABSTRACT FROM AUTHOR]

Conversation AI such as ChatGPT is a disruptive technology that has the potential to influence the field of counseling. The present article examines how AI based chatbot ChatGPT can be used by counselor educators in helping counselors-in-training practice counseling skills through role play. The author developed a model, ChatGPT Client Simulation (CCS), to support counselors-in-training learn and practice counseling skills. The CCS model can be used both in synchronous online as well as in-person counseling courses. [ABSTRACT FROM AUTHOR]

Dialogue systems, from Virtual Personal Assistants such as Siri, Cortana, and Alexa to state-of-the-art systems such as BlenderBot3 and ChatGPT, are already widely available, used in a variety of applications, and are increasingly part of many people's lives. However, the task of enabling them to use empathetic language more convincingly is still an emerging research topic. Such systems generally make use of complex neural networks to learn the patterns of typical human language use, and the interactions in which the systems participate are usually mediated either via interactive text-based or speech-based interfaces. In human–human interaction, empathy has been shown to promote prosocial behaviour and improve interaction. In the context of dialogue systems, to advance the understanding of how perceptions of empathy affect interactions, it is necessary to bring greater clarity to how empathy is measured and assessed. Assessing the way dialogue systems create perceptions of empathy brings together a range of technological, psychological, and ethical considerations that merit greater scrutiny than they have received so far. However, there is currently no widely accepted evaluation method for determining the degree of empathy that any given system possesses (or, at least, appears to possess). Currently, different research teams use a variety of automated metrics, alongside different forms of subjective human assessment such as questionnaires, self-assessment measures and narrative engagement scales. This diversity of evaluation practice means that, given two DSs, it is usually impossible to determine which of them conveys the greater degree of empathy in its dialogic exchanges with human users. Acknowledging this problem, the present article provides an overview of how empathy is measured in human–human interactions and considers some of the ways it is currently measured in human–DS interactions. Finally, it introduces a novel third-person analytical framework, called the Empathy Scale for Human–Computer Communication (ESHCC), to support greater uniformity in how perceived empathy is measured during interactions with state-of-the-art DSs. [ABSTRACT FROM AUTHOR]

Large language models such as ChatGPT enable users to automatically produce text but also raise ethical concerns, for example about authorship and deception. This paper analyses and discusses some key philosophical assumptions in these debates, in particular assumptions about authorship and language and—our focus—the use of the appearance/reality distinction. We show that there are alternative views of what goes on with ChatGPT that do not rely on this distinction. For this purpose, we deploy the two phased approach of deconstruction and relate our finds to questions regarding authorship and language in the humanities. We also identify and respond to two common counter-objections in order to show the ethical appeal and practical use of our proposal. [ABSTRACT FROM AUTHOR]

Classical natural language processing endeavored to understand the language of native speakers. When this proved to lie beyond the horizon, a scaled-down version settled for text analysis and processing but retained the old name and acronym. But text ≠ language. Any combination of signs and symbols qualifies as text. Language presupposes meaning, which is what connects it to real life. Failing to distinguish between the two results in confusing humanoids (machines thinking like humans) with machinoids (humans thinking like machines). As scientific English (SciEng) became the lingua franca of science, it has acquired all the traits of a machine language: reduced vocabulary, where fewer and fewer words have taken on more and more meanings; prescribed use of pronouns; depersonalized rigid syntactic forms and rules of composition. Compliance with SciEng standards can be automatically verified, which means that Sci Eng can be automatically imitated, what is referred to as AI writing (ChatGPT). The article discusses an attempt to automatically correct deviations from the rules by what is touted as AI editing. [ABSTRACT FROM AUTHOR]

A large language model (LLM) is an artificial intelligence (AI) model that uses natural language processing (NLP) to understand, interpret, and generate human-like language responses from unstructured text input. Its real-time response capabilities and eloquent dialogue enhance the interactive user experience in human–AI communication like never before. By gathering several sources on the internet, LLM chatbots can interact and respond to a wide range of queries, including problem solving, text summarization, and creating informative notes. Since ophthalmology is one of the medical fields integrating image analysis, telemedicine, AI, and other technologies, LLMs are likely to play an important role in eye care in the near future. This review summarizes the performance and potential applicability of LLMs in ophthalmology according to currently available publications. [ABSTRACT FROM AUTHOR]

Introduction: OpenAI recently introduced the ability to create custom generative pre-trained transformers (cGPTs) using text-based instruction and/or external documents using retrieval-augmented generation (RAG) architecture without coding knowledge. This study aimed to analyze the features of ophthalmology-related cGPTs and explore their potential utilities. Methods: Data collection took place on January 20 and 21, 2024, and custom GPTs were found by entering ophthalmology keywords into the "Explore GPTS" section of the website. General and specific features of cGPTs were recorded, such as knowledge other than GPT-4 training data. The instruction and description sections were analyzed for compatibility using the Likert scale. We analyzed two custom GPTs with the highest Likert score in detail. We attempted to create a convincingly presented yet potentially harmful cGPT to test safety features. Results: We analyzed 22 ophthalmic cGPTs, of which 55% were for general use and the most common subspecialty was glaucoma (18%). Over half (55%) contained knowledge other than GPT-4 training data. The representation of the instructions through the description was between "Moderately representative" and "Very representative" with a median Likert score of 3.5 (IQR 3.0–4.0). The instruction word count was significantly associated with Likert scores (P = 0.03). Tested cGPTs demonstrated potential for specific conversational tone, information, retrieval and combining knowledge from an uploaded source. With these safety settings, creating a malicious GPT was possible. Conclusions: This is the first study to our knowledge to examine the GPT store for a medical field. Our findings suggest that these cGPTs can be immediately implemented in practice and may offer more targeted and effective solutions compared to the standard GPT-4. However, further research is necessary to evaluate their capabilities and limitations comprehensively. The safety features currently appear to be rather limited. It may be helpful for the user to review the instruction section before using a cGPT. [ABSTRACT FROM AUTHOR]

ChatGPT and other LLMs are at the forefront of pedagogical considerations in classrooms across the academy. Many studies have spoken to the technology's capacity to generate one-off texts in a variety of genres. This study complements those by inquiring into its capacity to generate compelling texts at scale. In this study, we quantitatively and qualitatively analyze a small corpus of generated texts in two genres and gauge it against novice and published academic writers along known dimensions of linguistic variation. Theoretically, we position and historicize ChatGPT as a writing technology and consider the ways in which generated text may not be congruent with established trajectories of writing development in higher education. Our study found that generated texts are more informationally dense than authored texts and often read as dialogically closed, "empty," and "fluffy." We close with a discussion of potentially explanatory linguistic features, as well as relevant pedagogical implications. [ABSTRACT FROM AUTHOR]

The potential applications of artificial intelligence (AI) in health care have garnered significant interest in recent years. This study presents the first published exploration of ChatGPT, an AI language model, as a tool for providing postoperative guidance during rhinoplasty recovery. The primary objective was to shed light on the role of ChatGPT in augmenting patient care during the critical postoperative phase. Using the Rhinobase database, standardized questions were formulated to evaluate AI-generated responses addressing pain management, swelling, bruising, and potential asymmetries. Results demonstrated that ChatGPT has the potential to enhance patient education and alleviate emotional distress by providing general information and reassurance during the recovery process. However, the study emphasized that AI should not replace personalized advice from qualified health care professionals. This pioneering investigation offers valuable insights into the integration of AI and human expertise, paving the way for optimized postrhinoplasty recovery care. [ABSTRACT FROM AUTHOR]

Postoperative complications in rhinoplasty require prompt intervention for optimal outcomes. ChatGPT, an artificial intelligence (AI) tool, offers potential for assisting in postoperative care. This study aims to assess ChatGPT's effectiveness in providing guidance for various rhinoplasty complications. Different complication scenarios were input into ChatGPT. Responses were categorized into "Contact Surgeon" or "Follow Postoperative Instructions." ChatGPT consistently advised immediate surgeon contact for infection. For other complications, it recommended monitoring and adhering to instructions while suggesting surgeon contact if concerns persisted. ChatGPT shows promise in aiding patients' postoperative care by accurately identifying cases necessitating communication with surgeons or emergency care. This research underscores AI's potential in enhancing patient-centered care and contributes to the evolving landscape of health care practices. [ABSTRACT FROM AUTHOR]

Since its release in late 2022, ChatGPT and subsequent generative artificial intelligence (GAI) tools have raised a wide variety of questions and concerns for the field of technical communication: How will these tools be incorporated into professional settings? How might we appropriately integrate these tools into our research and teaching? In this review, we examine research published in 2023–2024 addressing these questions (N = 28). Overall, we find preliminary evidence that GAI tools can positively impact student writing and assessment; they also have the potential to assist with some aspects of academic and medical research and writing. However, there are concerns about their reliability and the ethical conundrums raised when they are used inappropriately or when their outputs cannot be distinguished from humans. More research is needed for evidence-based teaching and research strategies as well as policies guiding ethical use. We offer suggestions for new research avenues and methods. [ABSTRACT FROM AUTHOR]

This introductory article examines the evolving landscape of generative artificial intelligence (GAI) tools, contextualizing their impact through historical tropes of automation as both helper and threat. The authors argue that GAI tools are neither sentient helpers nor existential threats but complex systems that require careful integration into educational and research settings. The article underscores the importance of nuanced, evidence-based approaches, advocating for a balanced understanding of GAI's potential and limitations. It emphasizes ethical considerations and promotes reflective adoption over reactionary measures. [ABSTRACT FROM AUTHOR]

This study explores how prompting techniques, especially those integrated with rhetorical analysis results, may improve the effectiveness of artificial intelligence (AI)-generated business communication messages. I conducted an experiment to assess the effectiveness of these prompting techniques in the context of crafting a negative message generated with ChatGPT 3.5 (n = 85). A multiple regression was calculated to explore prompting techniques' impact on the negative message grades and how each technique influences the message grade. The results (F (4, 80) = 31.84, p <.001), with an adjusted R 2 =.595, indicate a positive relationship between prompting techniques and the effectiveness of AI-generated messages. This study also identified challenges related to students' AI literacy. I conclude the study by recommending practical measures on how to incorporate AI into business and professional writing classrooms. [ABSTRACT FROM AUTHOR]

Background: The complex nature of rheumatic diseases poses considerable challenges for clinicians when developing individualized treatment plans. Large language models (LLMs) such as ChatGPT could enable treatment decision support. Objective: To compare treatment plans generated by ChatGPT-3.5 and GPT-4 to those of a clinical rheumatology board (RB). Design/methods: Fictional patient vignettes were created and GPT-3.5, GPT-4, and the RB were queried to provide respective first- and second-line treatment plans with underlying justifications. Four rheumatologists from different centers, blinded to the origin of treatment plans, selected the overall preferred treatment concept and assessed treatment plans' safety, EULAR guideline adherence, medical adequacy, overall quality, justification of the treatment plans and their completeness as well as patient vignette difficulty using a 5-point Likert scale. Results: 20 fictional vignettes covering various rheumatic diseases and varying difficulty levels were assembled and a total of 160 ratings were assessed. In 68.8% (110/160) of cases, raters preferred the RB's treatment plans over those generated by GPT-4 (16.3%; 26/160) and GPT-3.5 (15.0%; 24/160). GPT-4's plans were chosen more frequently for first-line treatments compared to GPT-3.5. No significant safety differences were observed between RB and GPT-4's first-line treatment plans. Rheumatologists' plans received significantly higher ratings in guideline adherence, medical appropriateness, completeness and overall quality. Ratings did not correlate with the vignette difficulty. LLM-generated plans were notably longer and more detailed. Conclusion: GPT-4 and GPT-3.5 generated safe, high-quality treatment plans for rheumatic diseases, demonstrating promise in clinical decision support. Future research should investigate detailed standardized prompts and the impact of LLM usage on clinical decisions. [ABSTRACT FROM AUTHOR]

Three within-subject experiments were conducted by providing students with answers to content questions across different subject matters (a definition, explanation, and example) offered by a human professor (subject-matter expert) versus generative artificial intelligence (ChatGPT). In a randomized order, students read both the expert and ChatGPT’s responses (both were de-identified and declared to be “professors,” so students were not aware one was artificial intelligence), rated both explanations on teaching clarity and competence, and then reported on their affect toward the content and situational interest. Study 1 (interpersonal communication content) revealed no significant differences in repeated measure ratings comparing the expert versus ChatGPT. However, in Study 2 (business communication content) and Study 3 (instructional communication content), compared with the expert, ChatGPT (impersonating a professor) was rated by the same students as higher in teaching clarity and competence, and it generated more student affect and situational interest. In Study 2 and Study 3, a within-subjects mediation analysis revealed that ChatGPT generated more student affect toward the content through the clarity in responses it provided to students. [ABSTRACT FROM AUTHOR]

We evaluated the performance of a chatbot compared to the National Comprehensive Cancer Network (NCCN) Guidelines for the management of ovarian cancer. Using NCCN Guidelines, we generated 10 questions and answers regarding management of ovarian cancer at a single point in time. Questions were thematically divided into risk factors, surgical management, medical management, and surveillance. We asked ChatGPT (GPT-4) to provide responses without prompting (unprompted GPT) and with prompt engineering (prompted GPT). Responses were blinded and evaluated for accuracy and completeness by 5 gynecologic oncologists. A score of 0 was defined as inaccurate, 1 as accurate and incomplete, and 2 as accurate and complete. Evaluations were compared among NCCN, unprompted GPT, and prompted GPT answers. Overall, 48% of responses from NCCN, 64% from unprompted GPT, and 66% from prompted GPT were accurate and complete. The percentage of accurate but incomplete responses was higher for NCCN vs GPT-4. The percentage of accurate and complete scores for questions regarding risk factors, surgical management, and surveillance was higher for GPT-4 vs NCCN; however, for questions regarding medical management, the percentage was lower for GPT-4 vs NCCN. Overall, 14% of responses from unprompted GPT, 12% from prompted GPT, and 10% from NCCN were inaccurate. GPT-4 provided accurate and complete responses at a single point in time to a limited set of questions regarding ovarian cancer, with best performance in areas of risk factors, surgical management, and surveillance. Occasional inaccuracies, however, should limit unsupervised use of chatbots at this time. • There is interest in expanding the use of chatbot technology in medicine. • A high percentage of responses from ChatGPT to questions about ovarian cancer were graded as accurate and complete. • ChatGPT had a higher percentage of inaccurate responses compared to the National Comprehensive Cancer Network Guidelines. • ChatGPT received the highest scores in response to questions regarding risk factors, surgical management, and surveillance. [ABSTRACT FROM AUTHOR]

Introduction: Artificial intelligence continues to play an increasingly important role in modern health care. ChatGPT-3.5 (OpenAI, San Francisco, CA) has gained attention for its potential impact in this domain. Objective: To explore the role of ChatGPT-3.5 in guiding clinical decision-making specifically in the context of pancreatic adenocarcinoma and to assess its growth over a period of time. Participants: We reviewed the National Comprehensive Cancer Network® (NCCN) Clinical Practice Guidelines for the Management of Pancreatic Adenocarcinoma and formulated a complex clinical question for each decision-making page. ChatGPT-3.5 was queried in a reproducible fashion. We scored answers on the following Likert scale: 5) Correct; 4) Correct, with missing information requiring clarification; 3) Correct, but unable to complete answer; 2) Partially incorrect; 1) Absolutely incorrect. We repeated this protocol at 3-months. Score frequencies were compared, and subgroup analysis was conducted on Correctness (defined as scores 1-2 vs 3-5) and Accuracy (scores 1-3 vs 4-5). Results: In total, 50-pages of the NCCN Guidelines® were analyzed, generating 50 complex clinical questions. On subgroup analysis, the percentage of Acceptable answers improved from 60% to 76%. The score improvement was statistically significant (Mann-Whitney U-test; Mean Rank = 44.52 vs 56.48, P = .027). Conclusion: ChatGPT-3.5 represents an interesting but limited tool for assistance in clinical decision-making. We demonstrate that the platform evolved, and its responses to our standardized questions improved over a relatively short period (3-months). Future research is needed to determine the validity of this tool for this clinical application. [ABSTRACT FROM AUTHOR]

Artificial intelligence-driven tools, like ChatGPT, are prevalent sources for online health information. Limited research has explored the congruity between AI-generated content and professional treatment guidelines. This study seeks to compare recommendations for cancer-related symptoms generated from ChatGPT with guidelines from the National Comprehensive Cancer Network (NCCN). We extracted treatment recommendations for nine symptoms from NCCN, separated into four full Supportive Care sections and five subsections of the Palliative Care webpage. We entered "How can I reduce my cancer-related [symptom]" into ChatGPT- 3.5 for these same symptoms and extracted its recommendations. A comparative content analysis focused on recommendations for medications, consultations, and non-pharmacological strategies. We compared word count and Flesch-Kincaid Grade Level (FKGL) readability for each NCCN and ChatGPT section. The mean percent agreement between NCCN and ChatGPT recommendations was 37.3% (range 16.7%–81.8%). NCCN offered more specific medication recommendations. ChatGPT did recommend medications in the constipation and diarrhea sections that were not recommended by NCCN. Significant differences in word count (P =0.03) and FKGL (P <0.01) were found for NCCN Supportive Care webpages, with ChatGPT having lower word count and reading level. In the NCCN Palliative Care webpage subsections, there was no significant difference in word count (P =0.076), but FKGL was significantly lower with ChatGPT (P <0.01). While ChatGPT provides concise, accessible supportive care advice, discrepancies with guidelines raise concerns for patient-facing symptom management recommendations. Future research should consider how AI can be used in conjunction with evidence-based guidelines to support cancer patients' supportive care needs. [ABSTRACT FROM AUTHOR]

ChatGPT has revolutionized the travel industry. This study employs the stimulus-organism-response (SOR) model to develop and validate a conceptual model for ChatGPT acceptance. Social influence and perceived value emerge as key determinants of user cognitive appraisals of ChatGPT's expertise, trustworthiness, and emotional connections through parasocial interaction. These factors subsequently influence traveler acceptance of ChatGPT for travel-related services. Findings reveal that social influence is the most potent predictor of ChatGPT acceptance, while perceived trust directly impacts user acceptance during the cognitive process. These insights advance research on parasocial interaction, while providing valuable guidance for implementing ChatGPT in tourism services. [ABSTRACT FROM AUTHOR]