UNASSIGNED: This cross-sectional study assessed a generative-AI platform to automate the creation of accurate, appropriate, and compelling social-media (SoMe) posts from urological journal articles.
UNASSIGNED: One hundred SoMe-posts from the top 3 journals in urology X (Twitter) profiles were collected from Aug-2022 to Oct-2023 A freeware GPT-tool was developed to auto-generate SoMe posts, which included title-summarization, key findings, pertinent emojis, hashtags, and DOI links to the article. Three physicians independently evaluated GPT-generated posts for achieving tetrafecta of accuracy and appropriateness criteria. Fifteen scenarios were created from 5 randomly selected posts from each journal. Each scenario contained both the original and the GPT-generated post for the same article. Five questions were formulated to investigate the posts\' likability, shareability, engagement, understandability, and comprehensiveness. The paired posts were then randomized and presented to blinded academic authors and general public through Amazon Mechanical Turk (AMT) responders for preference evaluation.
UNASSIGNED: Median (IQR) time for post auto-generation was 10.2 seconds (8.5-12.5). Of the 150 rated GPT-generated posts, 115 (76.6%) met the correctness tetrafecta: 144 (96%) accurately summarized the title, 147 (98%) accurately presented the articles\' main findings, 131 (87.3%) appropriately used emojis and hashtags 138 (92%). A total of 258 academic urologists and 493 AMT responders answered the surveys, wherein the GPT-generated posts consistently outperformed the original journals\' posts for both academicians and AMT responders (P < .05).
UNASSIGNED: Generative-AI can automate the creation of SoMe posts from urology journal abstracts that are both accurate and preferable by the academic community and general public.

BACKGROUND: Mental disorders have ranked among the top 10 prevalent causes of burden on a global scale. Generative artificial intelligence (GAI) has emerged as a promising and innovative technological advancement that has significant potential in the field of mental health care. Nevertheless, there is a scarcity of research dedicated to examining and understanding the application landscape of GAI within this domain.
OBJECTIVE: This review aims to inform the current state of GAI knowledge and identify its key uses in the mental health domain by consolidating relevant literature.
METHODS: Records were searched within 8 reputable sources including Web of Science, PubMed, IEEE Xplore, medRxiv, bioRxiv, Google Scholar, CNKI and Wanfang databases between 2013 and 2023. Our focus was on original, empirical research with either English or Chinese publications that use GAI technologies to benefit mental health. For an exhaustive search, we also checked the studies cited by relevant literature. Two reviewers were responsible for the data selection process, and all the extracted data were synthesized and summarized for brief and in-depth analyses depending on the GAI approaches used (traditional retrieval and rule-based techniques vs advanced GAI techniques).
RESULTS: In this review of 144 articles, 44 (30.6%) met the inclusion criteria for detailed analysis. Six key uses of advanced GAI emerged: mental disorder detection, counseling support, therapeutic application, clinical training, clinical decision-making support, and goal-driven optimization. Advanced GAI systems have been mainly focused on therapeutic applications (n=19, 43%) and counseling support (n=13, 30%), with clinical training being the least common. Most studies (n=28, 64%) focused broadly on mental health, while specific conditions such as anxiety (n=1, 2%), bipolar disorder (n=2, 5%), eating disorders (n=1, 2%), posttraumatic stress disorder (n=2, 5%), and schizophrenia (n=1, 2%) received limited attention. Despite prevalent use, the efficacy of ChatGPT in the detection of mental disorders remains insufficient. In addition, 100 articles on traditional GAI approaches were found, indicating diverse areas where advanced GAI could enhance mental health care.
CONCLUSIONS: This study provides a comprehensive overview of the use of GAI in mental health care, which serves as a valuable guide for future research, practical applications, and policy development in this domain. While GAI demonstrates promise in augmenting mental health care services, its inherent limitations emphasize its role as a supplementary tool rather than a replacement for trained mental health providers. A conscientious and ethical integration of GAI techniques is necessary, ensuring a balanced approach that maximizes benefits while mitigating potential challenges in mental health care practices.

The emergence of generative artificial intelligence (AI) has revolutionized various fields. In ophthalmology, generative AI has the potential to enhance efficiency, accuracy, personalization and innovation in clinical practice and medical research, through processing data, streamlining medical documentation, facilitating patient-doctor communication, aiding in clinical decision-making, and simulating clinical trials. This review focuses on the development and integration of generative AI models into clinical workflows and scientific research of ophthalmology. It outlines the need for development of a standard framework for comprehensive assessments, robust evidence, and exploration of the potential of multimodal capabilities and intelligent agents. Additionally, the review addresses the risks in AI model development and application in clinical service and research of ophthalmology, including data privacy, data bias, adaptation friction, over interdependence, and job replacement, based on which we summarized a risk management framework to mitigate these concerns. This review highlights the transformative potential of generative AI in enhancing patient care, improving operational efficiency in the clinical service and research in ophthalmology. It also advocates for a balanced approach to its adoption.

UNASSIGNED: Teaching medical students the skills required to acquire, interpret, apply, and communicate clinical information is an integral part of medical education. A crucial aspect of this process involves providing students with feedback regarding the quality of their free-text clinical notes.
UNASSIGNED: The goal of this study was to assess the ability of ChatGPT 3.5, a large language model, to score medical students\' free-text history and physical notes.
UNASSIGNED: This is a single-institution, retrospective study. Standardized patients learned a prespecified clinical case and, acting as the patient, interacted with medical students. Each student wrote a free-text history and physical note of their interaction. The students\' notes were scored independently by the standardized patients and ChatGPT using a prespecified scoring rubric that consisted of 85 case elements. The measure of accuracy was percent correct.
UNASSIGNED: The study population consisted of 168 first-year medical students. There was a total of 14,280 scores. The ChatGPT incorrect scoring rate was 1.0%, and the standardized patient incorrect scoring rate was 7.2%. The ChatGPT error rate was 86%, lower than the standardized patient error rate. The ChatGPT mean incorrect scoring rate of 12 (SD 11) was significantly lower than the standardized patient mean incorrect scoring rate of 85 (SD 74; P=.002).
UNASSIGNED: ChatGPT demonstrated a significantly lower error rate compared to standardized patients. This is the first study to assess the ability of a generative pretrained transformer (GPT) program to score medical students\' standardized patient-based free-text clinical notes. It is expected that, in the near future, large language models will provide real-time feedback to practicing physicians regarding their free-text notes. GPT artificial intelligence programs represent an important advance in medical education and medical practice.

BACKGROUND: As generative artificial intelligence (GenAI) tools continue advancing, rigorous evaluations are needed to understand their capabilities relative to experienced clinicians and nurses. The aim of this study was to objectively compare the diagnostic accuracy and response formats of ICU nurses versus various GenAI models, with a qualitative interpretation of the quantitative results.
METHODS: This formative study utilized four written clinical scenarios representative of real ICU patient cases to simulate diagnostic challenges. The scenarios were developed by expert nurses and underwent validation against current literature. Seventy-four ICU nurses participated in a simulation-based assessment involving four written clinical scenarios. Simultaneously, we asked ChatGPT-4 and Claude-2.0 to provide initial assessments and treatment recommendations for the same scenarios. The responses from ChatGPT-4 and Claude-2.0 were then scored by certified ICU nurses for accuracy, completeness and response.
RESULTS: Nurses consistently achieved higher diagnostic accuracy than AI across open-ended scenarios, though certain models matched or exceeded human performance on standardized cases. Reaction times also diverged substantially. Qualitative response format differences emerged such as concision versus verbosity. Variations in GenAI models system performance across cases highlighted generalizability challenges.
CONCLUSIONS: While GenAI demonstrated valuable skills, experienced nurses outperformed in open-ended domains requiring holistic judgement. Continued development to strengthen generalized decision-making abilities is warranted before autonomous clinical integration. Response format interfaces should consider leveraging distinct strengths. Rigorous mixed methods research involving diverse stakeholders can help iteratively inform safe, beneficial human-GenAI partnerships centred on experience-guided care augmentation.
CONCLUSIONS: This mixed-methods simulation study provides formative insights into optimizing collaborative models of GenAI and nursing knowledge to support patient assessment and decision-making in intensive care. The findings can help guide development of explainable GenAI decision support tailored for critical care environments.
UNASSIGNED: Patients or public were not involved in the design and implementation of the study or the analysis and interpretation of the data.

With its increasing popularity, healthcare professionals and patients may use ChatGPT to obtain medication-related information. This study was conducted to assess ChatGPT\'s ability to provide satisfactory responses (i.e., directly answers the question, accurate, complete and relevant) to medication-related questions posed to an academic drug information service. ChatGPT responses were compared to responses generated by the investigators through the use of traditional resources, and references were evaluated. Thirty-nine questions were entered into ChatGPT; the three most common categories were therapeutics (8; 21%), compounding/formulation (6; 15%) and dosage (5; 13%). Ten (26%) questions were answered satisfactorily by ChatGPT. Of the 29 (74%) questions that were not answered satisfactorily, deficiencies included lack of a direct response (11; 38%), lack of accuracy (11; 38%) and/or lack of completeness (12; 41%). References were included with eight (29%) responses; each included fabricated references. Presently, healthcare professionals and consumers should be cautioned against using ChatGPT for medication-related information.

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

Large Language Models (LLMs) offer advanced text generation capabilities, sometimes surpassing human abilities. However, their use without proper expertise poses significant challenges, particularly in educational contexts. This article explores different facets of natural language generation (NLG) within the educational realm, assessing its advantages and disadvantages, particularly concerning LLMs. It addresses concerns regarding the opacity of LLMs and the potential bias in their generated content, advocating for transparent solutions. Therefore, it examines the feasibility of integrating OpenLogos expert-crafted resources into language generation tools used for paraphrasing and translation. In the context of the Multi3Generation COST Action (CA18231), we have been emphasizing the significance of incorporating OpenLogos into language generation processes, and the need for clear guidelines and ethical standards in generative models involving multilingual, multimodal, and multitasking capabilities. The Multi3Generation initiative strives to progress NLG research for societal welfare, including its educational applications. It promotes inclusive models inspired by the Logos Model, prioritizing transparency, human control, preservation of language principles and meaning, and acknowledgment of the expertise of resource creators. We envision a scenario where OpenLogos can contribute significantly to inclusive AI-supported education. Ethical considerations and limitations related to AI implementation in education are explored, highlighting the importance of maintaining a balanced approach consistent with traditional educational principles. Ultimately, the article advocates for educators to adopt innovative tools and methodologies to foster dynamic learning environments that facilitate linguistic development and growth.
Large Language Models boast advanced text generation quality and capabilities, often surpassing those of humans. However, they also pose significant challenges when used without proper expertise or care. In an educational context, the examination of language generation tools and their use by students is vital for establishing guidelines and a shared understanding of their ethical usage. This article explores several aspects of language generation within an educational context, and showcases the potential use of OpenLogos resources, provided within the framework of the Multi3Generation COST Action (CA18231) in language study and their integration into language learning tools, such as paraphrasing (monolingual) and translation (bilingual or multilingual). This article emphasizes the importance of leveraging OpenLogos in education, especially in language learning or language enhancement contexts. By embracing innovative tools and methodologies, educators can nurture a dynamic and enriching learning environment conducive to linguistic growth and development.

Cardiovascular disease (CVD) is a major cause of mortality worldwide, especially in resource-limited countries with limited access to healthcare resources. Early detection and accurate imaging are vital for managing CVD, emphasizing the significance of patient education. Generative artificial intelligence (AI), including algorithms to synthesize text, speech, images, and combinations thereof given a specific scenario or prompt, offers promising solutions for enhancing patient education. By combining vision and language models, generative AI enables personalized multimedia content generation through natural language interactions, benefiting patient education in cardiovascular imaging. Simulations, chat-based interactions, and voice-based interfaces can enhance accessibility, especially in resource-limited settings. Despite its potential benefits, implementing generative AI in resource-limited countries faces challenges like data quality, infrastructure limitations, and ethical considerations. Addressing these issues is crucial for successful adoption. Ethical challenges related to data privacy and accuracy must also be overcome to ensure better patient understanding, treatment adherence, and improved healthcare outcomes. Continued research, innovation, and collaboration in generative AI have the potential to revolutionize patient education. This can empower patients to make informed decisions about their cardiovascular health, ultimately improving healthcare outcomes in resource-limited settings.

OBJECTIVE: The aim of this paper is to investigate the incorporation of visual narratives, such as comics and graphics, into nursing education using Generative Artificial Intelligence (GAI) models like DALL-E.
BACKGROUND: Visual narratives serve as a powerful method for communicating intricate concepts in nursing education. Despite their advantages, challenges in creating effective educational comics persist due to the need for expertise in graphic design and the associated time and resource constraints.
METHODS: This study examines existing literature that highlights the efficacy of visual narratives in education and demonstrates the potential of GAI models, specifically DALL-E, in creating visual narratives for nursing education.
METHODS: We analyze the potential of GAI models, specifically DALL-E, to create visual narratives for educational purposes. This was demonstrated through illustrative examples addressing sensitive topics, illustrating research methodology and designing recruitment posters for clinical trials. Additionally, we discussed the necessity of reviewing and editing the text generated by DALL-E to ensure its accuracy and relevance in educational contexts. The method also considered legal concerns related to copyright and ownership of the generated content, highlighting the evolving legal landscape in this domain.
RESULTS: The study found that GAI, specifically DALL-E, has significant potential to bridge the gap in creating visual narratives for nursing education. While offering cost-effectiveness and accessibility, GAI tools require careful consideration of challenges such as text-related errors, misinterpretation of user prompts and legal concerns.
CONCLUSIONS: GAI models like DALL-E offer promising solutions for enhancing visual storytelling in nursing education. However, their effective integration requires a collaborative approach, where educators engage with these tools as co-pilots, leveraging their capabilities while mitigating potential drawbacks. By doing so, educators can harness the full potential of GAI to enrich the educational experience for learners through compelling visual narratives.