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Drawing parallels from the TV series Suits, this paper investigates the role of emotional intelligence (EQ) in fostering positive workplace culture, specifically within academic libraries. While the high-octane world of corporate law might seem disparate from the calm corridors of academic libraries, the interpersonal dynamics and challenges faced are strikingly similar. Central to this discussion is the recurring motif in Suits: the power of dialogue. Through characters like Harvey Specter and Donna Paulsen, the show emphasizes that open communication can be the key to resolution, even in high-pressure environments. Moreover, with the introduction of "The Donna," a unique AI tool, Suits provides a lens to examine the interplay between technology and EQ, raising questions about the true essence of human communication and its irreplaceability. This paper explores these themes, offering practical strategies for academic libraries to bolster EQ, enhance user experience, and ensure technology complements, rather than replaces, genuine human interaction. [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]

The recent proliferation of Large Language Models (LLMs) raises questions as to the role of such tools both within an educational learning environment and their epistemic capacity. If, as Alfred North Whitehead remarked, western philosophy indeed 'consists of a series of footnotes to Plato', it would be of doubtless importance to evaluate the position of LLMs in his epistemological framework. We analyse Plato and existing scholarship regarding his epistemology, combining this with a brief outline of the architectural features of GPT-3 and similar LLMs, before finally addressing whether they meet Plato's criteria, and where they stand in relation to education in general. We conclude that, in conjunction with the well-known factual unreliability of LLMs, they are likewise unsuited for the satisfaction of a Platonic epistemological standard. On account of this, we find LLMs to be sub-optimal within an educational learning environment, and this is why we suggest that their use be accordingly circumscribed. [ABSTRACT FROM AUTHOR]

ABSTRACT Recent years have witnessed a growing application of generative artificial intelligence (GenAI) technology in writing instruction. Students should mobilise their metacognitive strategies during this endeavour to maximise the benefits of GenAI while avoiding the potential negative impacts. Within the context of tertiary education in Hong Kong, this qualitative research investigates 13 postgraduate students' metacognitive strategies when utilising ChatGPT for English academic writing. Using data collected from audio recordings, semi‐structured interviews and human–GenAI chat logs, we explored students' metacognitive strategies within five dimensions, namely, planning, monitoring, evaluating, information management and debugging. The identified themes and subthemes within each dimension exhibited similarities to those observed in other educational settings. Meanwhile, certain metacognitive strategies (e.g., debugging) were found to be unique to the context of GenAI‐supported academic writing. This study contributes to the literature in two ways. Firstly, it provides a detailed and thorough understanding of metacognitive strategies, complementing the extensive quantitative research in this area. Secondly, this research expands the framework of metacognitive strategies to the context of GenAI‐supported academic writing learning. [ABSTRACT FROM AUTHOR]

\nKey PointsThe multifaceted implications of global population aging require regular assessments of the current state of aging-related social science research and the identification of potential future research priorities in this important area. Given the multi-, inter-, and transdisciplinary nature of this field, such assessments typically require the involvement of experts from diverse backgrounds to ensure a comprehensive picture and to synthesize understudied and newly emerging topics into a future research agenda. We explored to what extent ChatGPT (version GPT-4, OpenAI) might be a useful tool for synthesizing the current state of research and identifying promising future research areas, which could feed into expert panel discussions for priority setting. ChatGPT proposed a long list of topics and specific research questions that are useful in summarizing current views on research priorities across diverse sources. To illustrate, the top five priorities for future aging research identified by ChatGPT were digital integration, climate change and older populations, mental health and aging, aging in diverse contexts, and post-pandemic aging. In conclusion, ChatGPT may be a useful tool for identifying research agenda priorities across organizations present in the web, but the lack of transparency requires that experts critically evaluate the values and views underlying selected priorities. ChatGPT suggested research topics of past and for future aging research.The chatbot provided research topic lists for both academic and policy research.ChatGPT can inform priority setting by synthesizing the views of sources in the web.Due to lack of transparency, ChatGPT’s insights need to be critically assessed by human experts.ChatGPT suggested research topics of past and for future aging research.The chatbot provided research topic lists for both academic and policy research.ChatGPT can inform priority setting by synthesizing the views of sources in the web.Due to lack of transparency, ChatGPT’s insights need to be critically assessed by human experts. [ABSTRACT FROM AUTHOR]

The study highlights ChatGPT-4’s potential in educational settings for the evaluation of university students’ open-ended written examination responses. ChatGPT-4 evaluated 54 written responses, ranging from 24 to 256 words in English. It assessed each response using five criteria and assigned a grade on a six-point scale from fail to excellent, resulting in 3,240 evaluations. Verification-based chain-of-thought prompting with the RAG framework ensured ChatGPT-4’s accurate recall of responses and secure alignment in the university’s evaluation criteria. ChatGPT-4’s grading showed good consistency with the teacher’s grading. Mistakes in recalls and discrepancies between ChatGPT-4 and teacher assessments could be reduced. The results suggest a promising potential for using LLMs like ChatGPT-4 in academic written response evaluations. [ABSTRACT FROM AUTHOR]

AbstractAI has the potential to revolutionize learning and assessments. This research explored ChatGPT’s potential in creating MCQ-based clinical pharmacy exam papers at the Pharm-D level and compared their quality to human-made exams. In this study, three different MCQ-based papers (2 by ChatGPT; AIeasy & AIhard, and 1 by instructor; HUMAN) were set to have 10 MCQs each. We asked ChatGPT to set one exam with a low level of difficulty (AIeasy) and a second exam with specific instructions to achieve a more difficult exam (AIhard). Students attempted these exams as part of their regular assessment and rated them from 1 to 5 for various parameters such as critical thinking involved, difficulty level, and overall experience. Our study shows that students obtained higher marks for AIeasy (7.67 ± 3.92) than for AIhard (7.06 ± 1.32) and HUMAN (5.02 ± 1.70), making the HUMAN exam the most difficult one. Students rated the AIhard and HUMAN exams higher for critical thinking than for AIeasy. The students rated highest for AIeasy for overall experience. Interestingly, most of the students (n = 52, 83%) could not correctly identify the exam set by the instructor. Therefore, with clear instructions, ChatGPT can create content-relevant, good-quality exam papers with varying difficulty levels. This is especially useful for students who need to self-study and be ready for several exams to gauge their knowledge. [ABSTRACT FROM AUTHOR]

Generative artificial intelligence (GenAI) tools such as ChatGPT with their human‐like intelligence and language processing capabilities are significantly impacting the way we live, work, and communicate with each other. While scholars have increasingly focused on the use of GenAI in higher education since its inception, little is known about how key higher education stakeholders, particularly students, perceive its impact on teaching and learning within the context of academic communication, an area central to students' development of transferable skills and literacy competencies yet heavily influenced by the technology. This empirical study addresses the gap by investigating students' experiences and attitudes toward GenAI tools for English academic communication, focusing on their overall perceptions, perceived benefits, limitations, and challenges. Drawing on data from a questionnaire survey with 475 students and interviews with 12 at two universities in China, our findings indicate that students generally view GenAI positively, considering them useful for learning academic communication skills, particularly in writing, grammar, vocabulary, and reading. However, limitations are recognized in terms of giving feedback on critical thinking, creativity, and speaking skills. In addition, information reliability, ethical issues, and impact on assessment and academic integrity also emerged as important concerns. Our study argues that universities should embrace and capitalize on the affordances of GenAI and address its challenges to better support students' learning of critical academic literacy. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is influencing different aspects of human life. An AI‐powered technology, which has been recently released, is ChatGPT. It is a cutting‐edge technology that influences second/foreign language (L2) education. Although there is increasing research on the benefits and misfits of this chatbot in different disciplines, L2 education lacks a thorough investigation. To fill this lacuna, this phenomenographic study examined the perceptions of research‐active English as a Foreign Language (EFL) teachers regarding the potentials and pitfalls of ChatGPT for L2 learning, teaching, assessment, and research. To this end, a semistructured interview was held with 30 Iranian EFL teachers with varying educational backgrounds and AI integration experiences. The results of content and thematic analysis indicated that ChatGPT is a double‐edged sword that can both benefit and hurt these areas of L2 education. The most notable potentials were augmenting learner autonomy, providing personalized learning, reducing teachers' teaching workload, designing assessment rubrics, and summarizing lengthy papers and theses to save L2 researchers' time and energy. Concerning pitfalls, it was reported that ChatGPT might kill creativity and academic integrity, encourage cheating in online exams, spread fake and misinformation into the world of research, and cherish high‐tech plagiarism. Some practical suggestions are made to empower L2 educators and researchers to survive in the world of AI. [ABSTRACT FROM AUTHOR]

Purpose: Large language models (LLM) have recently attracted attention because of their enormous performance. Based on artificial intelligence, LLM enable dialogic communication using quasi-natural language that approximates the quality of human communication. Thus, LLM could play an important role for patients to become informed. To evaluate the validity of an LLM in providing medical information, we used one of the first high-performance LLM (ChatGPT) on the clinical example of acute lumbar disc herniation (LDH). Methods: Twenty-four spinal surgeons experienced in LDH surgery directed questions to ChatGPT about the clinical picture of LDH from a patient's perspective. They evaluated the quality of ChatGPT responses and its potential use in medical communication. The responses were compared with the information content of a standard informed consent form. Results: ChatGPT provided good results in terms of comprehensibility, specificity, and satisfaction of responses and in terms of medical accuracy and completeness. ChatGPT was not able to provide all the information that was provided in the informed consent form, but did communicate information that was not listed there. In some cases, albeit minor, ChatGPT made medically inaccurate claims, such as listing kyphoplasty and vertebroplasty as surgical options for LDH. Conclusion: With the incipient use of artificial intelligence in communication, LLM will certainly become increasingly important to patients. Even if LLM are unlikely to play a role in clinical communication between physicians and patients at the moment, the opportunities—but also the risks—of this novel technology should be alertly monitored. [ABSTRACT FROM AUTHOR]

Background: Artificial intelligence models such as ChatGPT (Open AI) have performed well on the exams of various medical and surgical fields. It is not yet known how ChatGPT performs on similar metabolic and bariatric surgery (MBS) questions. Objective: Assess the performance of ChatGPT on Focused Practice Designation in Metabolic and Bariatric Surgery board-style questions. Setting: United States. Methods: Questions obtained from the largest commercially available bank of FPD-MBS practice questions were entered into ChatGPT-4, as is, without prior training. We assessed the overall percentage correct as well as the percentage correct within each of the five American Board of Surgery (ABS) question categories. One-way ANOVA was used to determine if the frequency of correct answers differed between categories. Results: Out of 255 questions, ChatGPT-4 correctly answered 189 (74.1%). Between the five question categories there was no difference between the frequency of correct answers (p = 0.22). It did not matter if questions were entered individually or in groups of up to 10. Conclusion: Without prior training, ChatGPT-4 scored highly when evaluated on the largest practice question bank for the FPD-MBS exam. [ABSTRACT FROM AUTHOR]

Purpose: Generative AI will become an integral part of education in future. The potential of this technology in different disciplines should be identified to promote effective adoption. This study evaluated the performance of ChatGPT in tutorial and case-based learning questions in physiology and biochemistry for medical undergraduates. Our study mainly focused on the performance of GPT-3.5 version while a subgroup was comparatively assessed on GPT-3.5 and GPT-4 performances. Materials and methods: Answers were generated in GPT-3.5 for 44 modified essay questions (MEQs) in physiology and 43 MEQs in biochemistry. Each answer was graded by two independent examiners. Subsequently, a subset of 15 questions from each subject were selected to represent different score categories of the GPT-3.5 answers; responses were generated in GPT-4, and graded. Results: The mean score for physiology answers was 74.7 (SD 25.96). GPT-3.5 demonstrated a statistically significant (p =.009) superior performance in lower-order questions of Bloom's taxonomy in comparison to higher-order questions. Deficiencies in the application of physiological principles in clinical context were noted as a drawback. Scores in biochemistry were relatively lower with a mean score of 59.3 (SD 26.9) for GPT-3.5. There was no statistically significant difference in the scores for higher and lower-order questions of Bloom's taxonomy. The deficiencies highlighted were lack of in-depth explanations and precision. The subset of questions where the GPT-4 and GPT-3.5 were compared demonstrated a better overall performance in GPT-4 responses in both subjects. This difference between the GPT-3.5 and GPT-4 performance was statistically significant in biochemistry but not in physiology. Conclusions: The differences in performance across the two versions, GPT-3.5 and GPT-4 across the disciplines are noteworthy. Educators and students should understand the strengths and limitations of this technology in different fields to effectively integrate this technology into teaching and learning. [ABSTRACT FROM AUTHOR]

Artificial intelligence has evolved since its inception in the 1950s, resulting in the creation of large language models that are trained on extensive data sets to understand and generate content, such as OpenAI's ChatGPT, which launched in November 2022. Modern technology that is easy to access and free to use, like ChatGPT, is changing the educational landscape as it is an example of a large language model – an artificial intelligence network – that can process and generate text that reads as though it were written by a person. Using a survey and semi-structured interviews, this exploratory mixed-method study examined social work educators' experiences of addressing students' use of ChatGPT and found that although they are uncomfortable with addressing ChatGPT use with their students, they believe it is necessary to communicate its ethical use to them. Results also suggested that while using ChatGPT as a learning tool is valid, it can be overused. ChatGPT use gives students a false sense of having mastered academic material, and institutional guidelines are needed to address concerns regarding academic integrity and plagiarism. Moreover, instructors should address students' use of ChatGPT with their institutions and take intentional steps to ethically and creatively integrate artificial intelligence models into the social work curriculum. [ABSTRACT FROM AUTHOR]

Introduction: Large language models (LLM), such as Chat Generative Pre-trained Transformer (ChatGPT) and Bard utilise deep learning algorithms that have been trained on a massive data set of text and code to generate human-like responses. Several studies have demonstrated satisfactory performance on postgraduate examinations, including the United States Medical Licensing Examination. We aimed to evaluate artificial intelligence performance in Part A of the intercollegiate Membership of the Royal College of Surgeons (MRCS) examination. Methods: The MRCS mock examination from Pastest, a commonly used question bank for examinees, was used to assess the performance of three LLMs: GPT-3.5, GPT 4.0 and Bard. Three hundred mock questions were input into the three LLMs, and the responses provided by the LLMs were recorded and analysed. The pass mark was set at 70%. Results: The overall accuracies for GPT-3.5, GPT 4.0 and Bard were 67.33%, 71.67% and 65.67%, respectively (p = 0.27). The performances of GPT-3.5, GPT 4.0 and Bard in Applied Basic Sciences were 68.89%, 72.78% and 63.33% (p = 0.15), respectively. Furthermore, the three LLMs obtained correct answers in 65.00%, 70.00% and 69.17% of the Principles of Surgery in General questions (p = 0.67). There were no differences in performance in the overall and subcategories among the three LLMs. Conclusions: Our findings demonstrated satisfactory performance for all three LLMs in the MRCS Part A examination, with GPT 4.0 the only LLM that achieved the pass mark set. [ABSTRACT FROM AUTHOR]

ABSTRACT Background Materials and Methods Results Conclusions In this study, the accuracy and comprehensiveness of the answers given to questions about dental avulsion by two artificial intelligence‐based language models, ChatGPT and Gemini, were comparatively evaluated.Based on the guidelines of the International Society of Dental Traumatology, a total of 33 questions were prepared, including multiple‐choice questions, binary questions, and open‐ended questions as technical questions and patient questions about dental avulsion. They were directed to ChatGPT and Gemini. Responses were recorded and scored by four pediatric dentists. Statistical analyses, including ICC analysis, were performed to determine the agreement and accuracy of the responses. The significance level was set as p < 0.050.The mean score of the Gemini model was statistically significantly higher than the ChatGPT (p = 0.001). ChatGPT gave more correct answers to open‐ended questions and T/F questions on dental avulsion; it showed the lowest accuracy in the MCQ section. There was no significant difference between the responses of the Gemini model to different types of questions on dental avulsion and the median scores (p = 0.088). ChatGPT and Gemini were analyzed with the Mann–Whitney U test without making a distinction between question types, and Gemini answers were found to be statistically significantly more accurate (p = 0.004).The Gemini and ChatGPT language models based on the IADT guideline for dental avulsion undoubtedly show promise. To guarantee the successful incorporation of LLMs into practice, it is imperative to conduct additional research, clinical validation, and improvements to the models. [ABSTRACT FROM AUTHOR]

The article discusses the importance of engaging in conversations with Holocaust survivors, rather than simply collecting their stories as sources of knowledge. It highlights the significance of building relationships and friendships with survivors, emphasizing the value of learning together through sustained conversations. The text also explores various perspectives on survivor testimonies, challenging traditional approaches to analyzing and interpreting their accounts. The authors stress the human connection and emotional depth involved in understanding survivors' experiences, advocating for a more personal and empathetic approach to Holocaust studies. [Extracted from the article]

Purpose: This study aims to investigate the role of one of these models in the field of voice pathology and compare its performance in distinguishing the perceived dysphonia level. Methods: Demographic information, voice self-assessments, and acoustic measurements related to a sample of 50 adult dysphonic outpatients were presented to ChatGPT and Perplexity AI chatbots, which were interrogated for the perceived dysphonia level. Results: The agreement between the auditory-perceptual assessment by experts and ChatGPT and Perplexity AI chatbots, as determined by Cohen's Kappa, was not statistically significant (p = 0.429). There was also a low positive correlation (rs = 0.30, p = 0.03) between the diagnosis made by ChatGPT and Perplexity AI chatbots (rs = 0.30, p = 0.03). Conclusion: It seems that AI could not play a vital role in helping the voice care teams determine the perceptual level of dysphonia. [ABSTRACT FROM AUTHOR]

Objective: To investigate the accuracy of information provided by ChatGPT-4o to patients about tracheotomy. Methods: Twenty common questions of patients about tracheotomy were presented to ChatGPT-4o twice (7-day intervals). The accuracy, clarity, relevance, completeness, referencing, and usefulness of responses were assessed by a board-certified otolaryngologist and a board-certified intensive care unit practitioner with the Quality Analysis of Medical Artificial Intelligence (QAMAI) tool. The interrater reliability and the stability of the ChatGPT-4o responses were evaluated with intraclass correlation coefficient (ICC) and Pearson correlation analysis. Results: The total scores of QAMAI were 22.85 ± 4.75 for the intensive care practitioner and 21.45 ± 3.95 for the otolaryngologist, which consists of moderate-to-high accuracy. The otolaryngologist and the ICU practitioner reported high ICC (0.807; 95%CI: 0.655–0.911). The highest QAMAI scores have been found for clarity and completeness of explanations. The QAMAI scores for the accuracy of the information and the referencing were the lowest. The information related to the post-laryngectomy tracheostomy remains incomplete or erroneous. ChatGPT-4o did not provide references for their responses. The stability analysis reported high stability in regenerated questions. Conclusion: The accuracy of ChatGPT-4o is moderate-to-high in providing information related to the tracheotomy. However, patients using ChatGPT-4o need to be cautious about the information related to tracheotomy care, steps, and the differences between temporary and permanent tracheotomies. [ABSTRACT FROM AUTHOR]

Purpose: This study delves into the broader implications of artificial intelligence (AI) text generation technologies, including large language models (LLMs) and chatbots, on the scientific literature of otolaryngology. By observing trends in AI-generated text within published otolaryngology studies, this investigation aims to contextualize the impact of AI-driven tools that are reshaping scientific writing and communication. Methods: Text from 143 original articles published in JAMA Otolaryngology – Head and Neck Surgery was collected, representing periods before and after ChatGPT's release in November 2022. The text from each article's abstract, introduction, methods, results, and discussion were entered into ZeroGPT.com to estimate the percentage of AI-generated content. Statistical analyses, including T-Tests and Fligner-Killeen's tests, were conducted using R. Results: A significant increase was observed in the mean percentage of AI-generated text post-ChatGPT release, especially in the abstract (from 34.36 to 46.53%, p = 0.004), introduction (from 32.43 to 45.08%, p = 0.010), and discussion sections (from 15.73 to 25.03%, p = 0.015). Publications of authors from non-English speaking countries demonstrated a higher percentage of AI-generated text. Conclusion: This study found that the advent of ChatGPT has significantly impacted writing practices among researchers publishing in JAMA Otolaryngology – Head and Neck Surgery, raising concerns over the accuracy of AI-created content and potential misinformation risks. This manuscript highlights the evolving dynamics between AI technologies, scientific communication, and publication integrity, emphasizing the urgent need for continued research in this dynamic field. The findings also suggest an increasing reliance on AI tools like ChatGPT, raising questions about their broader implications for scientific publishing. [ABSTRACT FROM AUTHOR]

Objectives: Head and neck squamous cell carcinoma (HNSCC) is a complex malignancy that requires a multidisciplinary tumor board approach for individual treatment planning. In recent years, artificial intelligence tools have emerged to assist healthcare professionals in making informed treatment decisions. This study investigates the application of the newly published LLM Claude 3 Opus compared to the currently most advanced LLM ChatGPT 4.0 for the diagnosis and therapy planning of primary HNSCC. The results were compared to that of a conventional multidisciplinary tumor board; (2) Materials and Methods: We conducted a study in March 2024 on 50 consecutive primary head and neck cancer cases. The diagnostics and MDT recommendations were compared to the Claude 3 Opus and ChatGPT 4.0 recommendations for each patient and rated by two independent reviewers for the following parameters: clinical recommendation, explanation, and summarization in addition to the Artificial Intelligence Performance Instrument (AIPI); (3) Results: In this study, Claude 3 achieved better scores for the diagnostic workup of patients than ChatGPT 4.0 and provided treatment recommendations involving surgery, chemotherapy, and radiation therapy. In terms of clinical recommendations, explanation and summarization Claude 3 scored similar to ChatGPT 4.0, listing treatment recommendations which were congruent with the MDT, but failed to cite the source of the information; (4) Conclusion: This study is the first analysis of Claude 3 for primary head and neck cancer cases and demonstrates a superior performance in the diagnosis of HNSCC than ChatGPT 4.0 and similar results for therapy recommendations. This marks the advent of a newly launched advanced AI model that may be superior to ChatGPT 4.0 for the assessment of primary head and neck cancer cases and may assist in the clinical diagnostic and MDT setting. Highlights: Claude 3 Opus HNSCC Multidisciplinary Tumorboard Artificial Intelligence LLM [ABSTRACT FROM AUTHOR]

Objective: The purpose of this study was to assess how well ChatGPT, an AI-powered chatbot, performed in helping to manage pediatric sialadenitis and identify when sialendoscopy was necessary. Methods: 49 clinical cases of pediatric sialadenitis were retrospectively reviewed. ChatGPT was given patient data, and it offered differential diagnoses, proposed further tests, and suggested treatments. The decisions made by the treating otolaryngologists were contrasted with the answers provided by ChatGPT. Analysis was done on ChatGPT response consistency and interrater reliability. Results: ChatGPT showed 78.57% accuracy in primary diagnosis, and 17.35% of cases were considered likely. On the other hand, otolaryngologists recommended fewer further examinations than ChatGPT (111 vs. 60, p < 0.001). For additional exams, poor agreement was found between ChatGPT and otolaryngologists. Only 28.57% of cases received a pertinent and essential treatment plan via ChatGPT, indicating that the platform's treatment recommendations were frequently lacking. For treatment ratings, judges' interrater reliability was greatest (Kendall's tau = 0.824, p < 0.001). For the most part, ChatGPT's response constancy was high. Conclusions: Although ChatGPT has the potential to correctly diagnose pediatric sialadenitis, there are a number of noteworthy limitations with regard to its ability to suggest further testing and treatment regimens. Before widespread clinical use, more research and confirmation are required. To guarantee that chatbots are utilized properly and effectively to supplement human expertise rather than to replace it, a critical viewpoint is required. [ABSTRACT FROM AUTHOR]

Background: The widespread diffusion of Artificial Intelligence (AI) platforms is revolutionizing how health-related information is disseminated, thereby highlighting the need for tools to evaluate the quality of such information. This study aimed to propose and validate the Quality Assessment of Medical Artificial Intelligence (QAMAI), a tool specifically designed to assess the quality of health information provided by AI platforms. Methods: The QAMAI tool has been developed by a panel of experts following guidelines for the development of new questionnaires. A total of 30 responses from ChatGPT4, addressing patient queries, theoretical questions, and clinical head and neck surgery scenarios were assessed by 27 reviewers from 25 academic centers worldwide. Construct validity, internal consistency, inter-rater and test–retest reliability were assessed to validate the tool. Results: The validation was conducted on the basis of 792 assessments for the 30 responses given by ChatGPT4. The results of the exploratory factor analysis revealed a unidimensional structure of the QAMAI with a single factor comprising all the items that explained 51.1% of the variance with factor loadings ranging from 0.449 to 0.856. Overall internal consistency was high (Cronbach's alpha = 0.837). The Interclass Correlation Coefficient was 0.983 (95% CI 0.973–0.991; F (29,542) = 68.3; p < 0.001), indicating excellent reliability. Test–retest reliability analysis revealed a moderate-to-strong correlation with a Pearson's coefficient of 0.876 (95% CI 0.859–0.891; p < 0.001). Conclusions: The QAMAI tool demonstrated significant reliability and validity in assessing the quality of health information provided by AI platforms. Such a tool might become particularly important/useful for physicians as patients increasingly seek medical information on AI platforms. [ABSTRACT FROM AUTHOR]

Purpose: Large language models continue to dramatically change the medical landscape. We aimed to explore the utility of ChatGPT in providing accurate, actionable, and understandable generative medical translations in English, Spanish, and Mandarin pertaining to Otolaryngology. Methods: Responses of GPT-4 to commonly asked patient questions listed on official otolaryngology clinical practice guidelines (CPG) were evaluated with the Patient Education materials Assessment Tool-printable (PEMAT-P.) Additional critical elements were identified a priori to evaluate ChatGPT's accuracy and thoroughness in its responses. Multiple fluent speakers of English, Mandarin, and Spanish evaluated each response generated by ChatGPT. Results: Total PEMAT-P scores differed between English, Mandarin, and Spanish GPT-4 generated responses depicting a moderate effect size of language, Eta-Square 0.07 with scores ranging from 73 to 77 (P-value = 0.03). Overall understandability scores did not differ between English, Mandarin, and Spanish depicting a small effect size of language, Eta-Square 0.02 scores ranging from 76 to 79 (P-value = 0.17), nor did overall actionability scores Eta-Square 0 score ranging 66–73 (P-value = 0.44). Overall a priori procedure-specific responses similarly did not differ between English, Spanish, and Mandarin Eta-Square 0.02 scores ranging 61–78 (P-value = 0.22). Conclusion: GPT-4 produces accurate, understandable, and actionable outputs in English, Spanish, and Mandarin. Responses generated by GPT-4 in Spanish and Mandarin are comparable to English counterparts indicating a novel use for these models within Otolaryngology, and implications for bridging healthcare access and literacy gaps. Level of evidence: IV. [ABSTRACT FROM AUTHOR]

Purpose: As online health information-seeking surges, concerns mount over the quality and safety of accessible content, potentially leading to patient harm through misinformation. On one hand, the emergence of Artificial Intelligence (AI) in healthcare could prevent it; on the other hand, questions raise regarding the quality and safety of the medical information provided. As laryngeal cancer is a prevalent head and neck malignancy, this study aims to evaluate the utility and safety of three large language models (LLMs) as sources of patient information about laryngeal cancer. Methods: A cross-sectional study was conducted using three LLMs (ChatGPT 3.5, ChatGPT 4.0, and Bard). A questionnaire comprising 36 inquiries about laryngeal cancer was categorised into diagnosis (11 questions), treatment (9 questions), novelties and upcoming treatments (4 questions), controversies (8 questions), and sources of information (4 questions). The population of reviewers consisted of 3 groups, including ENT specialists, junior physicians, and non-medicals, who graded the responses. Each physician evaluated each question twice for each model, while non-medicals only once. Everyone was blinded to the model type, and the question order was shuffled. Outcome evaluations were based on a safety score (1–3) and a Global Quality Score (GQS, 1–5). Results were compared between LLMs. The study included iterative assessments and statistical validations. Results: Analysis revealed that ChatGPT 3.5 scored highest in both safety (mean: 2.70) and GQS (mean: 3.95). ChatGPT 4.0 and Bard had lower safety scores of 2.56 and 2.42, respectively, with corresponding quality scores of 3.65 and 3.38. Inter-rater reliability was consistent, with less than 3% discrepancy. About 4.2% of responses fell into the lowest safety category (1), particularly in the novelty category. Non-medical reviewers' quality assessments correlated moderately (r = 0.67) with response length. Conclusions: LLMs can be valuable resources for patients seeking information on laryngeal cancer. ChatGPT 3.5 provided the most reliable and safe responses among the models evaluated. [ABSTRACT FROM AUTHOR]

Purpose: Access to high-quality and comprehensible patient information is crucial. However, information provided by increasingly prevalent Artificial Intelligence tools has not been thoroughly investigated. This study assesses the quality and readability of information from ChatGPT regarding three index ENT operations: tonsillectomy, adenoidectomy, and grommets. Methods: We asked ChatGPT standard and simplified questions. Readability was calculated using Flesch-Kincaid Reading Ease Score (FRES), Flesch-Kincaid Grade Level (FKGL), Gunning Fog Index (GFI) and Simple Measure of Gobbledygook (SMOG) scores. We assessed quality using the DISCERN instrument and compared these with ENT UK patient leaflets. Results: ChatGPT readability was poor, with mean FRES of 38.9 and 55.1 pre- and post-simplification, respectively. Simplified information from ChatGPT was 43.6% more readable (FRES) but scored 11.6% lower for quality. ENT UK patient information readability and quality was consistently higher. Conclusions: ChatGPT can simplify information at the expense of quality, resulting in shorter answers with important omissions. Limitations in knowledge and insight curb its reliability for healthcare information. Patients should use reputable sources from professional organisations alongside clear communication with their clinicians for well-informed consent and making decisions. [ABSTRACT FROM AUTHOR]

In November 2022, the tech company OpenAI launched a groundbreaking chatbot model, ChatGPT. This unprecedented chatbot, characterized by an ease of use for lay internet users, gained immediate popularity and attracted extensive media attention. This article examines global press coverage of ChatGPT in peak reporting dates over the first full year of its existence. Based on a qualitative holistic narrative analysis, our findings point to two narrated scapes of political fear in the coverage of ChatGPT: The fear of the machine and the fear of the human. These attest to the collective imagining of an intensified future, where post-humanist interaction with political information is associated with exploitation, propaganda, and polarization of existing political rifts. We draw on the case study to articulate journalists' role in signaling instability in the current political media ecosystem, and their construction of a techno-moral framework for society. We discuss an important blind-spot in journalists' fulfilment of their normative role in fostering technology-informed citizens globally. [ABSTRACT FROM AUTHOR]

The rapid emergence of generative AI models in the media sector demands a critical examination of the narratives these models produce, particularly in relation to sensitive topics, such as politics, racism, immigration, public health, gender and violence, among others. The ease with which generative AI can produce narratives on sensitive topics raises concerns about potential harms, such as amplifying biases or spreading misinformation. Our study juxtaposes the content generated by a state-of-the-art generative AI, specifically ChatGPT-4, with actual articles from leading UK media outlets on the topic of immigration. Our specific case study focusses on the representation of Eastern European Roma migrants in the context of the 2016 UK Referendum on EU membership. Through a comparative critical discourse analysis, we uncover patterns of representation, inherent biases and potential discrepancies in representation between AI-generated narratives and mainstream media discourse with different political views. Preliminary findings suggest that ChatGPT-4 exhibits a remarkable degree of objectivity in its reporting and demonstrates heightened racial awareness in the content it produces. Moreover, it appears to consistently prioritise factual accuracy over sensationalism. All these features set it apart from right-wing media articles in our sample. This is further evidenced by the fact that, in most instances, ChatGPT-4 refrains from generating text or does so only after considerable adjustments when prompted with headlines that the model deems inflammatory. While these features can be attributed to the model's diverse training data and model architecture, the findings invite further examination to determine the full scope of ChatGPT-4's capabilities and its potential shortcomings in representing the full spectrum of social and political perspectives prevalent in society. [ABSTRACT FROM AUTHOR]

Purpose: To assess the accuracy and completeness of ChatGPT-generated answers regarding uveitis description, prevention, treatment, and prognosis. Methods: Thirty-two uveitis-related questions were generated by a uveitis specialist and inputted into ChatGPT 3.5. Answers were compiled into a survey and were reviewed by five uveitis specialists using standardized Likert scales of accuracy and completeness. Results: In total, the median accuracy score for all the uveitis questions (n = 32) was 4.00 (between "more correct than incorrect" and "nearly all correct"), and the median completeness score was 2.00 ("adequate, addresses all aspects of the question and provides the minimum amount of information required to be considered complete"). The interrater variability assessment had a total kappa value of 0.0278 for accuracy and 0.0847 for completeness. Conclusion: ChatGPT can provide relatively high accuracy responses for various questions related to uveitis; however, the answers it provides are incomplete, with some inaccuracies. Its utility in providing medical information requires further validation and development prior to serving as a source of uveitis information for patients. [ABSTRACT FROM AUTHOR]

This study explores the ethical decision-making of introductory public speaking students (n = 190) when considering using ChatGPT to generate speeches. Using a modified version of Jones (1991) model, this study examined how students perceived moral aspects, recognized ethical issues, judged situations, and formed intentions. Results showed that perceived moral intensity directly affected recognizing ethical problems. Better recognition led to more appropriate ethical judgments, particularly among those with higher perceived moral intensity. Ethical judgments significantly influenced intentions to use ChatGPT for speeches. Students who viewed the use of ChatGPT as ethically problematic were inclined to avoid it. [ABSTRACT FROM AUTHOR]

Objective To evaluate the reliability of three artificial intelligence (AI) chatbots (ChatGPT, Google Bard, and Chatsonic) in generating accurate references from existing obstetric literature. Study Design Between mid-March and late April 2023, ChatGPT, Google Bard, and Chatsonic were prompted to provide references for specific obstetrical randomized controlled trials (RCTs) published in 2020. RCTs were considered for inclusion if they were mentioned in a previous article that primarily evaluated RCTs published by the top medical and obstetrics and gynecology journals with the highest impact factors in 2020 as well as RCTs published in a new journal focused on publishing obstetric RCTs. The selection of the three AI models was based on their popularity, performance in natural language processing, and public availability. Data collection involved prompting the AI chatbots to provide references according to a standardized protocol. The primary evaluation metric was the accuracy of each AI model in correctly citing references, including authors, publication title, journal name, and digital object identifier (DOI). Statistical analysis was performed using a permutation test to compare the performance of the AI models. Results Among the 44 RCTs analyzed, Google Bard demonstrated the highest accuracy, correctly citing 13.6% of the requested RCTs, whereas ChatGPT and Chatsonic exhibited lower accuracy rates of 2.4 and 0%, respectively. Google Bard often substantially outperformed Chatsonic and ChatGPT in correctly citing the studied reference components. The majority of references from all AI models studied were noted to provide DOIs for unrelated studies or DOIs that do not exist. Conclusion To ensure the reliability of scientific information being disseminated, authors must exercise caution when utilizing AI for scientific writing and literature search. However, despite their limitations, collaborative partnerships between AI systems and researchers have the potential to drive synergistic advancements, leading to improved patient care and outcomes. Key Points AI chatbots often cite scientific articles incorrectly. AI chatbots can create false references. Responsible AI use in research is vital. [ABSTRACT FROM AUTHOR]

Background: Artificial intelligence (AI) and large language models (LLMs) transform how patients inform themselves. LLMs offer potential as educational tools, but their quality depends upon the information generated. Current literature examining AI as an informational tool in dermatology has been limited in evaluating AI's multifaceted roles and diversity of opinions. Here, we evaluate LLMs as a patient‐educational tool for Mohs micrographic surgery (MMS) in and out of the clinic utilizing an international expert panel. Methods: The most common patient MMS questions were extracted from Google and transposed into two LLMs and Google's search engine. 15 MMS surgeons evaluated the generated responses, examining their appropriateness as a patient‐facing informational platform, sufficiency of response in a clinical environment, and accuracy of content generated. Validated scales were employed to assess the comprehensibility of each response. Results: The majority of reviewers deemed all LLM responses appropriate. 75% of responses were rated as mostly accurate or higher. ChatGPT had the highest mean accuracy. The majority of the panel deemed 33% of responses sufficient for clinical practice. The mean comprehensibility scores for all platforms indicated a required 10th‐grade reading level. Conclusions: LLM‐generated responses were rated as appropriate patient informational sources and mostly accurate in their content. However, these platforms may not provide sufficient information to function in a clinical environment, and complex comprehensibility may represent a barrier to utilization. As the popularity of these platforms increases, it is important for dermatologists to be aware of these limitations. [ABSTRACT FROM AUTHOR]

The article explores the use of artificial intelligence tools, specifically ChatGPT‐4 and Google Lens, in dermatological diagnosis. A study compared the diagnostic capabilities of these tools with clinical diagnoses made by dermatologists, showing that ChatGPT‐4 had a higher overall accuracy of 70.15% compared to Google Lens's 45.35%. While ChatGPT‐4 performed better in certain conditions like acne vulgaris and psoriasis, both tools faced challenges with conditions like pityriasis rosea. The study suggests that integrating advanced AI technologies like ChatGPT‐4 could enhance dermatological diagnostic processes, especially in resource‐limited settings, while highlighting the need for further refinement of tools like Google Lens. [Extracted from the article]

Objectives: This study evaluates the performance of AI models, ChatGPT-4o and Google Gemini, in answering rheumatology board-level questions, comparing their effectiveness, reliability, and applicability in clinical practice. Method: A cross-sectional study was conducted using 420 rheumatology questions from the BoardVitals question bank, excluding 27 visual data questions. Both artificial intelligence models categorized the questions according to difficulty (easy, medium, hard) and answered them. In addition, the reliability of the answers was assessed by asking the questions a second time. The accuracy, reliability, and difficulty categorization of the AI models' response to the questions were analyzed. Results: ChatGPT-4o answered 86.9% of the questions correctly, significantly outperforming Google Gemini's 60.2% accuracy (p < 0.001). When the questions were asked a second time, the success rate was 86.7% for ChatGPT-4o and 60.5% for Google Gemini. Both models mainly categorized questions as medium difficulty. ChatGPT-4o showed higher accuracy in various rheumatology subfields, notably in Basic and Clinical Science (p = 0.028), Osteoarthritis (p = 0.023), and Rheumatoid Arthritis (p < 0.001). Conclusions: ChatGPT-4o significantly outperformed Google Gemini in rheumatology board-level questions. This demonstrates the success of ChatGPT-4o in situations requiring complex and specialized knowledge related to rheumatological diseases. The performance of both AI models decreased as the question difficulty increased. This study demonstrates the potential of AI in clinical applications and suggests that its use as a tool to assist clinicians may improve healthcare efficiency in the future. Future studies using real clinical scenarios and real board questions are recommended. Key Points •ChatGPT-4o significantly outperformed Google Gemini in answering rheumatology board-level questions, achieving 86.9% accuracy compared to Google Gemini's 60.2%. •For both AI models, the correct answer rate decreased as the question difficulty increased. •The study demonstrates the potential for AI models to be used in clinical practice as a tool to assist clinicians and improve healthcare efficiency. [ABSTRACT FROM AUTHOR]

• This study evaluated the artificial intelligence (AI) proficiency of obstetrics residents in clinical scenarios. • This study identified a gap between perceived and actual AI skills. • There is a need for AI literacy in medical education. • Structured AI training programmes in obstetrics are proposed. • This study highlighted the role of AI in enhancing healthcare efficiency. In line with the digital transformation trend in medical training, students may resort to artificial intelligence (AI) for learning. This study assessed the interaction between obstetrics residents and ChatGPT during clinically oriented summative evaluations related to acute hepatic steatosis of pregnancy, and their self-reported competencies in information technology (IT) and AI. The participants in this semi-qualitative observational study were 14 obstetrics residents from two university hospitals. Students' queries were categorized into three distinct types: third-party enquiries; search-engine-style queries; and GPT-centric prompts. Responses were compared against a standardized answer produced by ChatGPT with a Delphi-developed expert prompt. Data analysis employed descriptive statistics and correlation analysis to explore the relationship between AI/IT skills and response accuracy. The study participants showed moderate IT proficiency but low AI proficiency. Interaction with ChatGPT regarding clinical signs of acute hepatic steatosis gravidarum revealed a preference for third-party questioning, resulting in only 21% accurate responses due to misinterpretation of medical acronyms. No correlation was found between AI response accuracy and the residents' self-assessed IT or AI skills, with most expressing dissatisfaction with their AI training. This study underlines the discrepancy between perceived and actual AI proficiency, highlighted by clinically inaccurate yet plausible AI responses – a manifestation of the 'stochastic parrot' phenomenon. These findings advocate for the inclusion of structured AI literacy programmes in medical education, focusing on prompt engineering. These academic skills are essential to exploit AI's potential in obstetrics and gynaecology. The ultimate aim is to optimize patient care in AI-augmented health care, and prevent misleading and unsafe knowledge acquisition. [ABSTRACT FROM AUTHOR]

Introduction: Interest is growing in the potential of artificial intelligence (AI) chatbots and large language models like OpenAI's ChatGPT and Google's Gemini, particularly in dental education. To explore dental educators' perceptions of AI chatbots and large language models, specifically their potential benefits and challenges for dental education. Materials and Methods: A global cross‐sectional survey was conducted in May–June 2023 using a 31‐item online‐questionnaire to assess dental educators' perceptions of AI chatbots like ChatGPT and their influence on dental education. Dental educators, representing diverse backgrounds, were asked about their use of AI, its perceived impact, barriers to using chatbots, and the future role of AI in this field. Results: 428 dental educators (survey views = 1516; response rate = 28%) with a median [25/75th percentiles] age of 45 [37, 56] and 16 [8, 25] years of experience participated, with the majority from the Americas (54%), followed by Europe (26%) and Asia (10%). Thirty‐one percent of respondents already use AI tools, with 64% recognising their potential in dental education. Perception of AI's potential impact on dental education varied by region, with Africa (4[4–5]), Asia (4[4–5]), and the Americas (4[3–5]) perceiving more potential than Europe (3[3–4]). Educators stated that AI chatbots could enhance knowledge acquisition (74.3%), research (68.5%), and clinical decision‐making (63.6%) but expressed concern about AI's potential to reduce human interaction (53.9%). Dental educators' chief concerns centred around the absence of clear guidelines and training for using AI chatbots. Conclusion: A positive yet cautious view towards AI chatbot integration in dental curricula is prevalent, underscoring the need for clear implementation guidelines. [ABSTRACT FROM AUTHOR]

Laboratory medical reports are often not intuitively comprehensible to non-medical professionals. Given their recent advancements, easier accessibility and remarkable performance on medical licensing exams, patients are therefore likely to turn to artificial intelligence-based chatbots to understand their laboratory results. However, empirical studies assessing the efficacy of these chatbots in responding to real-life patient queries regarding laboratory medicine are scarce. Thus, this investigation included 100 patient inquiries from an online health forum, specifically addressing Complete Blood Count interpretation. The aim was to evaluate the proficiency of three artificial intelligence-based chatbots (ChatGPT, Gemini and Le Chat) against the online responses from certified physicians. The findings revealed that the chatbots' interpretations of laboratory results were inferior to those from online medical professionals. While the chatbots exhibited a higher degree of empathetic communication, they frequently produced erroneous or overly generalized responses to complex patient questions. The appropriateness of chatbot responses ranged from 51 to 64 %, with 22 to 33 % of responses overestimating patient conditions. A notable positive aspect was the chatbots' consistent inclusion of disclaimers regarding its non-medical nature and recommendations to seek professional medical advice. The chatbots' interpretations of laboratory results from real patient queries highlight a dangerous dichotomy – a perceived trustworthiness potentially obscuring factual inaccuracies. Given the growing inclination towards self-diagnosis using AI platforms, further research and improvement of these chatbots is imperative to increase patients' awareness and avoid future burdens on the healthcare system. [ABSTRACT FROM AUTHOR]

Oral reading fluency probes are essential for reading assessment, intervention, and progress monitoring. Due to the limited options for choosing oral reading fluency probes, it is important to utilize all available resources such as generative artificial intelligence (AI) like ChatGPT to create oral reading fluency probes. The purpose of this article is to describe how to use AI through ChatGPT to create customizable reading passages comparable with that of oral reading fluency probes. Using readability estimates, the ChatGPT-generated passages can be tailored to suit for specific grade levels, similar to how current publishers design oral reading fluency probes for the market. The implication of ChatGPT-generated passages is that researchers and practitioners alike could use ChatGPT to be able to create a seemingly unlimited amount of reading passages tailored to the skill level and interests of the learner for intervention material and potentially assessment material, while reducing cost and time investment. [ABSTRACT FROM AUTHOR]

This study evaluated the readability of existing patient education materials and explored the potential of generative AI tools, such as ChatGPT-4 and Google Gemini, to simplify these materials to a sixth-grade reading level, in accordance with guidelines. Seven patient education documents were selected from a major radiology group. ChatGPT-4 and Gemini were provided the documents and asked to reformulate to target a sixth-grade reading level. Average reading level (ARL) and proportional word count (PWC) change were calculated, and a 1-sample t-test was conducted (p=0.05). Three radiologists assessed the materials on a Likert scale for appropriateness, relevance, clarity, and information retention. The original materials had an ARL of 11.72. ChatGPT ARL was 7.32 ± 0.76 (6/7 significant) and Gemini ARL was 6.55 ± 0.51 (7/7 significant). ChatGPT reduced word count by 15% ± 7%, with 95% retaining at least 75% of information. Gemini reduced word count by 33% ± 7%, with 68% retaining at least 75% of information. ChatGPT outputs were more appropriate (95% vs. 57%), clear (92% vs. 67%), and relevant (95% vs. 76%) than Gemini. Interrater agreement was significantly different for ChatGPT (0.91) than for Gemini (0.46). Generative AI significantly enhances the readability of patient education materials, which did not achieve the recommended sixth-grade ARL. Radiologist evaluations confirmed the appropriateness and relevance of the AI-simplified texts. This study emphasizes the capabilities of generative AI tools and the necessity for ongoing expert review to maintain content accuracy and suitability. • AI tools reduced radiology material reading levels from 11 to ∼7 grade level. • ChatGPT outperformed Google Gemini in appropriateness, clarity, and relevance. • AI simplification maintained high content accuracy and relevance in most cases. • Radiologist oversight remains crucial to ensure patient safety and preparation. • Study suggests AI can make radiology education more accessible to patients. [ABSTRACT FROM AUTHOR]

Background With the growing relevance of artificial intelligence (AI)-based patient-facing information, microsurgical-specific online information provided by professional organizations was compared with that of ChatGPT (Chat Generative Pre-Trained Transformer) and assessed for accuracy, comprehensiveness, clarity, and readability. Methods Six plastic and reconstructive surgeons blindly assessed responses to 10 microsurgery-related medical questions written either by the American Society of Reconstructive Microsurgery (ASRM) or ChatGPT based on accuracy, comprehensiveness, and clarity. Surgeons were asked to choose which source provided the overall highest-quality microsurgical patient-facing information. Additionally, 30 individuals with no medical background (ages: 18–81, μ = 49.8) were asked to determine a preference when blindly comparing materials. Readability scores were calculated, and all numerical scores were analyzed using the following six reliability formulas: Flesch–Kincaid Grade Level, Flesch–Kincaid Readability Ease, Gunning Fog Index, Simple Measure of Gobbledygook Index, Coleman–Liau Index, Linsear Write Formula, and Automated Readability Index. Statistical analysis of microsurgical-specific online sources was conducted utilizing paired t -tests. Results Statistically significant differences in comprehensiveness and clarity were seen in favor of ChatGPT. Surgeons, 70.7% of the time, blindly choose ChatGPT as the source that overall provided the highest-quality microsurgical patient-facing information. Nonmedical individuals 55.9% of the time selected AI-generated microsurgical materials as well. Neither ChatGPT nor ASRM-generated materials were found to contain inaccuracies. Readability scores for both ChatGPT and ASRM materials were found to exceed recommended levels for patient proficiency across six readability formulas, with AI-based material scored as more complex. Conclusion AI-generated patient-facing materials were preferred by surgeons in terms of comprehensiveness and clarity when blindly compared with online material provided by ASRM. Studied AI-generated material was not found to contain inaccuracies. Additionally, surgeons and nonmedical individuals consistently indicated an overall preference for AI-generated material. A readability analysis suggested that both materials sourced from ChatGPT and ASRM surpassed recommended reading levels across six readability scores. [ABSTRACT FROM AUTHOR]

Purpose: There is increasing interest in the use of digital platforms such as ChatGPT for anatomy education. This study aims to evaluate the efficacy of ChatGPT in providing accurate and consistent responses to questions focusing on musculoskeletal anatomy across various time points (hours and days). Methods: A selection of 6 Anatomy-related questions were asked to ChatGPT 3.5 in 4 different timepoints. All answers were rated blindly by 3 expert raters for quality according to a 5 -point Likert Scale. Difference of 0 or 1 points in Likert scale scores between raters was considered as agreement and between different timepoints was considered as consistent indicating good reproducibility. Results: There was significant variation in the quality of the answers ranging from extremely good to very poor quality. There was also variation of consistency levels between different timepoints. Answers were rated as good quality (≥ 3 in Likert scale) in 50% of cases (3/6) and as consistent in 66.6% (4/6) of cases. In the low-quality answers, significant mistakes, conflicting data or lack of information were encountered. Conclusion: As of the time of this article, the quality and consistency of the ChatGPT v3.5 answers is variable, thus limiting its utility as independent and reliable resource of learning musculoskeletal anatomy. Validating information by reviewing the anatomical literature is highly recommended. [ABSTRACT FROM AUTHOR]