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Interdisciplinary teaching approaches have gained significant importance in today's educational landscape. Among these approaches, decision-making competence plays a pivotal role by nurturing critical thinking and problem-solving skills. Focusing on the decision-making process regarding the sensibility of using sugar-substitutes, this study addresses three key questions. (1) Do chemistry and sports and health students differ in evaluation-competence? (2) What criteria do they use in evaluating the decision-making problem? (3) How do they use ChatGPT (OpenAI, San Francisco, CA, USA), an AI tool, in decision-making? Through surveys, the study revealed two evaluation-competence factors, identified decision-making criteria, and analyzed querying behavior using ChatGPT. The results revealed nuanced differences between the two student groups in the factors of evaluation-competence. While both groups showed improvements in recognizing moral dimensions and considering alternatives, chemistry students displayed enhanced abilities in establishing evaluation criteria and empathetic thinking. Furthermore, differences emerged in the criteria selected for decision-making and querying behavior, indicating varying engagement with ChatGPT. This study offers insights into enhancing interdisciplinary education and underscores the need for tailored interventions to address diverse student needs. [ABSTRACT FROM AUTHOR]

Introduction Large language models (LLMs) are designed for recognizing, summarizing, translating, predicting, and generating text-based content from knowledge gained from extensive data sets. ChatGPT4 (Generative Pre-trained Transformer 4) (OpenAI, San Francisco, California, United States) is a transformer-based LLM model pretrained on public data as well as data obtained from third-party sources using deep learning techniques of fine tuning and reinforcement learning from human feedback to predict the next text. We wanted to explore the role of LLM as a teaching assistant (TA) in plastic surgery. Material and Methods TA roles were first identified in available literature, and based on the roles, a list of suitable tasks was created where LLM could be used to perform the task. Prompts designed to be fed in to the LLM (specifically ChatGPT) to generate appropriate output, were then created and fed to the ChatGPT model. The outputs generated were scored by evaluators and compared for interobserver agreement. Results A final set of eight TA roles were identified where a LLM could be utilized to generate content. These contents were scored for usefulness and accuracy. These were scored independently by the eight study authors in a scoring sheet created for the study. Interobserver agreements for content accuracy, usefulness, and clarity were 100% for content generated for the following: interactive case studies (generation), simulation of preoperative consultations, and generation of ethical considerations. Discussion LLMs in general and ChatGPT (on which this study is based) in specific, can generate answers to questions and prompts based on huge amount of text fed into the model for training the underlying language model. The answers generated have been found to be accurate, readable, and even indistinguishable from human-generated text. This capability of automated content synthesis can be exploited to generate summaries to text, answer short and long answers, and generate case scenarios. We could identify a few such scenarios where the LLM could in general be utilized to play the role of a TA and aid plastic surgery residents in particular. In addition, these models could also be used by students to obtain feedback and gain reflection which itself stimulates critical thinking. Conclusion Incorporating LLMs into the educational arsenal of plastic surgery residency programs can provide a dynamic, interactive, and individualized learning experience for residents and prove to be worthy TAs of future. [ABSTRACT FROM AUTHOR]

Objectives: This study aims to assess the accuracy of answers generated by ChatGPT-3.5 using zero-shot and roleplay prompts in response to questions about herbal medicine from clinical pharmacists. In addition, we aim to evaluate the quality of text responses when ChatGPT-3.5 is prompted to provide references for the given answers. Materials and Methods: ChatGPT-3.5, developed by OpenAI in San Francisco, is an advanced artificial intelligence chatbot that utilizes a large language model to generate text resembling human-like responses. In this study, a total of 90 questions were posed to ChatGPT-3.5, and the accuracy of its responses was compared using both zero-shot and roleplay prompts. These questions were evenly distributed among four categories related to herbal medicine clinical practice. To evaluate the correctness of the responses, they were cross-referenced with peer-reviewed and trusted references. The authenticity of the references provided by ChatGPT-3.5 was also assessed. Results: Using the roleplay prompt with ChatGPT-3.5 led to an 86.67% correct answer rate for herbal medicine questions, compared to 77.78% with zero-shot prompts. When references were requested, 77.48% (zero-shot) and 75.69% (roleplay) were identified as "true" references, but ChatGPT-3.5 itself generated many references. Only 38.37% (zero-shot) and 50.46% (Roleplay) of these "true' references, provided a sufficient background for accurate answers. Conclusion: In this pilot study, ChatGPT-3.5 with the roleplay prompt shows promise as an alternative tool for addressing herbal medicine questions. However, improvements are needed to enhance reference accuracy and the availability of comprehensive background information. [ABSTRACT FROM AUTHOR]

ChatGPT (OpenAI, San Francisco, CA) is a large language model-based artificial intelligence chatbot. ChatGPT can be used to answer unique, user-generated questions without direct training on specific content. In this study, we assessed the accuracy and utility of ChatGPT to generate responses to questions regarding renal masses and renal cell carcinoma. We reviewed three professional society guidelines for renal cell carcinoma to generate fifteen content questions. These were posed to ChatGPT and its text output recorded. We also generated pre- and post-content assessment questions regarding familiarity with and opinions on ChatGPT use in medical and patient education. Questions were then presented to four urologic oncologists and four non-clinical staff members and responses analyzed using basic statistics. We found that all assessors rated ChatGPT highly on the accuracy and usefulness of information provided with overall mean scores of 4.64 (± 0.62 SD) and 3.58 (± 0.75) out of 5, respectively. Clinicians and non-clinicians did not differ in their scoring of responses (p=0.378). Completing content assessment improved confidence in the accuracy of ChatGPT's information (p=0.011) and increased agreement that it should be used for medical education (p=0.007, Figure 1). Attitudes towards use for patient education did not change (p=0.305) ChatGPT has significant potential utility in medical education if it can provide accurate and useful information. We have found it to be a useful adjunct to expert human guidance both for medical trainee and, less so, for patient education. Further work is needed to validate these results before adoption. [ABSTRACT FROM AUTHOR]

Academic publishing is crucial for scientific communication, is governed by accepted ethical norms, and underpins the collective literature on basic science, and technological and medical principles and advances. In November 2022, the public and professional global communities, including the scientific community, witnessed the release of ChatGPT by OpenAI in San Francisco, California, USA. Excluding its public appeal and entertaining aspects but considering its diverse potential applications, some ethical concerns must be considered before establishing guidelines on using and including ChatGPT or similar platforms in scientific publishing. Some academic publishers and preprints have accepted manuscripts with ChatGPT listed as a "co-author". Though excluding such platforms from scientific publishing may not be practicable with time, establishing ethical principles is essential before ChatGPT could become a "co-author" in any scientific, published manuscript. [ABSTRACT FROM AUTHOR]