Search

Artificial intelligence tools are rapidly growing in education, highlighting the imperative need for a thorough and critical evaluation of their performance. To this aim, this study tests the effectiveness of ChatGPT and Google Bard in answering a range of questions within the engineering and health sectors. True/false, multiple choice questions (MCQs), matching, short answer, essay, and calculation questions are among the question types investigated. Findings showed that ChatGPT 4 surpasses both ChatGPT 3.5 and Google Bard in terms of creative problem-solving and accuracy across various question types. The highest accuracy achieved by ChatGPT 4 was in true/false questions, reaching 97.5%, while its least accurate performance was noted in calculation questions with an accuracy of 82.5%. Prompting both ChatGPT and Google Bard to provide short responses apparently prevented them from hallucinating with unrealistic or nonsensical responses. The majority of the problems for which ChatGPT and Google Bard provided incorrect answers demonstrated a correct problemsolving approach; however, both AI models struggled to accurately perform simple calculations. In MCQs related to health sciences, ChatGPT seemed to have a challenge in discerning the correct answer among several plausible options. While all three tools managed the essay questions competently, avoiding any blatantly incorrect responses (unlike with other question types), some nuanced differences were noticed. ChatGPT 3.5 consistently adhered more closely to the essay prompts, providing straightforward and essential responses, while ChatGPT 4 demonstrated superiority over both models in terms of adaptability. ChatGPT4 fabricated references, creating nonexistent authors and research titles in response to prompts for sources. While utilizing AI in education holds a promise, even the latest and most advanced versions of ChatGPT and Google Bard were not able to accurately answer all questions. There remains a significant need for human cognitive skills and further advancements in AI capabilities. [ABSTRACT FROM AUTHOR]

This article explores the use of large language models (LLMs) in forecasting and raises concerns about their reliability. It points out that LLMs may have already learned from publicly available data, making it difficult to evaluate their accuracy. The article also highlights that LLMs are trained by non-experts, which can lead to inaccurate forecasts. Furthermore, it discusses instances where LLMs provided misleading information about their forecasting models. The article advises caution when using LLMs for forecasting and emphasizes the importance of further research and evaluation. [Extracted from the article]

The article discusses the role of artificial intelligence (AI) platforms in handling patient-generated questions for Mohs micrographic surgery (MMS). The study evaluates the use of large language models (LLMs) such as ChatGPT and Google Bard/Gemini as educational tools in dermatology. While LLMs are generally considered appropriate and accurate for patient education, they often lack the necessary specificity and conciseness required for effective patient communication in a medical setting. The study emphasizes the need for ongoing validation, standardization, and collaboration with developers to address these limitations effectively. It also highlights the importance of AI tools complementing rather than replacing human guidance in clinical practice. [Extracted from the article]

Sentiment analysis is a vital task in natural language processing (NLP) that aims to identify and extract the emotional states and opinions of text. In this study, we conduct a comprehensive comparison of large language models (LLMs), such as ChatGPT and Google Bard, with conventional methods in sentiment analysis. We employ a rigorous evaluation framework that covers four essential metrics: accuracy, precision, recall, and the F1-score. Our results reveal that TextBlob outperforms other methods, achieving an impressive accuracy of 69% and precision of 83%. On the other hand, Bard shows a relatively poor performance, with only 39% accuracy and 46% precision. This study offers valuable insights into the diverse capabilities of AI models in sentiment analysis. A key finding of this study is the importance of model selection according to the specific requirements of the task. Each model has its own strengths and weaknesses, which are reflected in their performance profiles. Moreover, the context in which these models operate is crucial. For instance, ChatGPT generates varied responses, Bard struggles with multiple sentences, and Robustly Optimized BERT Pretraining Approach (RoBERTa) balances precision and recall. This study also reveals the performance gap between LLMs and state-of-the-art deep learning methods. We believe this work will inspire future research and applications of ChatGPT and similar AI models in sentiment analysis and related tasks. [ABSTRACT FROM AUTHOR]

Objective: The integration of artificial intelligence (AI) in healthcare education is inevitable. Understanding the proficiency of generative AI in different languages to answer complex questions is crucial for educational purposes. The study objective was to compare the performance ChatGPT-4 and Gemini in answering Virology multiple-choice questions (MCQs) in English and Arabic, while assessing the quality of the generated content. Both AI models' responses to 40 Virology MCQs were assessed for correctness and quality based on the CLEAR tool designed for evaluation of AI-generated content. The MCQs were classified into lower and higher cognitive categories based on the revised Bloom's taxonomy. The study design considered the METRICS checklist for the design and reporting of generative AI-based studies in healthcare. Results: ChatGPT-4 and Gemini performed better in English compared to Arabic, with ChatGPT-4 consistently surpassing Gemini in correctness and CLEAR scores. ChatGPT-4 led Gemini with 80% vs. 62.5% correctness in English compared to 65% vs. 55% in Arabic. For both AI models, superior performance in lower cognitive domains was reported. Both ChatGPT-4 and Gemini exhibited potential in educational applications; nevertheless, their performance varied across languages highlighting the importance of continued development to ensure the effective AI integration in healthcare education globally. [ABSTRACT FROM AUTHOR]

Summary Large language models (LLMs) have significantly impacted various fields with their ability to understand and generate human‐like text. This study explores the potential benefits and limitations of integrating LLMs, such as ChatGPT, into haematology practices. Utilizing systematic review methodologies, we analysed studies published after 1 December 2022, from databases like PubMed, Web of Science and Scopus, and assessing each for bias with the QUADAS‐2 tool. We reviewed 10 studies that applied LLMs in various haematology contexts. These models demonstrated proficiency in specific tasks, such as achieving 76% diagnostic accuracy for haemoglobinopathies. However, the research highlighted inconsistencies in performance and reference accuracy, indicating variability in reliability across different uses. Additionally, the limited scope of these studies and constraints on datasets could potentially limit the generalizability of our findings. The findings suggest that, while LLMs provide notable advantages in enhancing diagnostic processes and educational resources within haematology, their integration into clinical practice requires careful consideration. Before implementing them in haematology, rigorous testing and specific adaptation are essential. This involves validating their accuracy and reliability across different scenarios. Given the field's complexity, it is also critical to continuously monitor these models and adapt them responsively. [ABSTRACT FROM AUTHOR]

Background: Large language models (LLMs) such as ChatGPT-4 and Google Gemini show potential for patient health education, but concerns about their accuracy require careful evaluation. This study evaluates the readability and accuracy of ChatGPT-4 and Google Gemini in answering questions about retinal detachment. Methods: Comparative study analyzing responses from ChatGPT-4 and Google Gemini to 13 retinal detachment questions, categorized by difficulty levels (D1, D2, D3). Masked responses were reviewed by ten vitreoretinal specialists and rated on correctness, errors, thematic accuracy, coherence, and overall quality grading. Analysis included Flesch Readability Ease Score, word and sentence counts. Results: Both Artificial Intelligence tools required college-level understanding for all difficulty levels. Google Gemini was easier to understand (p = 0.03), while ChatGPT-4 provided more correct answers for the more difficult questions (p = 0.0005) with fewer serious errors. ChatGPT-4 scored highest on most challenging questions, showing superior thematic accuracy (p = 0.003). ChatGPT-4 outperformed Google Gemini in 8 of 13 questions, with higher overall quality grades in the easiest (p = 0.03) and hardest levels (p = 0.0002), showing a lower grade as question difficulty increased. Conclusions: ChatGPT-4 and Google Gemini effectively address queries about retinal detachment, offering mostly accurate answers with few critical errors, though patients require higher education for comprehension. The implementation of AI tools may contribute to improving medical care by providing accurate and relevant healthcare information quickly. [ABSTRACT FROM AUTHOR]

When women receive a diagnosis of a gynecologic malignancy, they can have questions about their diagnosis or treatment that can result in voice queries to virtual assistants for more information. Recent advancement in artificial intelligence (AI) has transformed the landscape of medical information accessibility. The Google virtual assistant (VA) outperformed Siri, Alexa and Cortana in voice queries presented prior to the explosive implementation of AI in early 2023. The efforts presented here focus on determining if advances in AI in the last 12 months have improved the accuracy of Google VA responses related to gynecologic oncology. Previous questions were utilized to form a common basis for queries prior to 2023 and responses in 2024. Correct answers were obtained from the UpToDate medical resource. Responses related to gynecologic oncology were obtained using Google VA, as well as the generative AI chatbots Google Bard/Gemini and Microsoft Bing-Copilot. The AI narrative responses varied in length and positioning of answers within the response. Google Bard/Gemini achieved an 87.5% accuracy rate, while Microsoft Bing-Copilot reached 83.3%. In contrast, the Google VA's accuracy in audible responses improved from 18% prior to 2023 to 63% in 2024. While the accuracy of the Google VA has improved in the last year, it underperformed Google Bard/Gemini and Microsoft Bing-Copilot so there is considerable room for further improved accuracy. [ABSTRACT FROM AUTHOR]

This study aims to empirically analyze the relationship between the motivational factors of generative AI users and the intention to continue using the service. Accordingly, the motives of users who use generative AI services are defined as individual, social, and technical motivation factors. This research verified the effect of these factors on intention to continue using the services and tested the meditating effect of trust and acceptance attitude. We tested this through verifying trust and acceptance attitudes. An online survey was conducted on language-based generative AI service users such as OpenAI's ChatGPT, Google Bard, Microsoft Bing, and Meta-Lama, and a structural equation analysis was conducted through a total of 356 surveys. As a result of the analysis, individual, social, and technical motivational factors all had a positive (+) effect on trust and acceptance attitude on the attitude toward accepting generative AI services. Among them, individual motivation such as self-efficacy, innovation orientation, and playful desire were found to have the greatest influence on the formation of the acceptance attitude. In addition, social factors were identified as the factors that have the greatest influence on trust in the use of generative AI services. When it comes to using generative AI, it was confirmed that social reputation or awareness directly affects the trust in usability. [ABSTRACT FROM AUTHOR]

This article explores the application of generative artificial intelligence (GAI) and large language models (LLMs) in the field of gastroenterology and hepatology. It introduces the concept of prompt engineering, which involves optimizing user inputs to guide LLMs in generating desired outputs. The article discusses the goals of prompt engineering, such as task completion and in-context learning, and explains the core elements of a prompt. It also examines different approaches to prompting and the roles of system, user, and assistant in the interaction with LLMs. The article concludes by emphasizing the importance of prompt engineering in improving the performance of LLMs in extracting data from research articles, while acknowledging the limitations of this approach. [Extracted from the article]

Purpose: This study evaluates the efficacy of two advanced Large Language Models (LLMs), OpenAI's ChatGPT 4 and Google's Gemini Advanced, in providing treatment recommendations for head and neck oncology cases. The aim is to assess their utility in supporting multidisciplinary oncological evaluations and decision-making processes. Methods: This comparative analysis examined the responses of ChatGPT 4 and Gemini Advanced to five hypothetical cases of head and neck cancer, each representing a different anatomical subsite. The responses were evaluated against the latest National Comprehensive Cancer Network (NCCN) guidelines by two blinded panels using the total disagreement score (TDS) and the artificial intelligence performance instrument (AIPI). Statistical assessments were performed using the Wilcoxon signed-rank test and the Friedman test. Results: Both LLMs produced relevant treatment recommendations with ChatGPT 4 generally outperforming Gemini Advanced regarding adherence to guidelines and comprehensive treatment planning. ChatGPT 4 showed higher AIPI scores (median 3 [2–4]) compared to Gemini Advanced (median 2 [2–3]), indicating better overall performance. Notably, inconsistencies were observed in the management of induction chemotherapy and surgical decisions, such as neck dissection. Conclusions: While both LLMs demonstrated the potential to aid in the multidisciplinary management of head and neck oncology, discrepancies in certain critical areas highlight the need for further refinement. The study supports the growing role of AI in enhancing clinical decision-making but also emphasizes the necessity for continuous updates and validation against current clinical standards to integrate AI into healthcare practices fully. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this study was to define the capability of ChatGPT-4 and Google Gemini in analyzing detailed glaucoma case descriptions and suggesting an accurate surgical plan. Methods: Retrospective analysis of 60 medical records of surgical glaucoma was divided into "ordinary" (n = 40) and "challenging" (n = 20) scenarios. Case descriptions were entered into ChatGPT and Bard's interfaces with the question "What kind of surgery would you perform?" and repeated three times to analyze the answers' consistency. After collecting the answers, we assessed the level of agreement with the unified opinion of three glaucoma surgeons. Moreover, we graded the quality of the responses with scores from 1 (poor quality) to 5 (excellent quality), according to the Global Quality Score (GQS) and compared the results. Results: ChatGPT surgical choice was consistent with those of glaucoma specialists in 35/60 cases (58%), compared to 19/60 (32%) of Gemini (p = 0.0001). Gemini was not able to complete the task in 16 cases (27%). Trabeculectomy was the most frequent choice for both chatbots (53% and 50% for ChatGPT and Gemini, respectively). In "challenging" cases, ChatGPT agreed with specialists in 9/20 choices (45%), outperforming Google Gemini performances (4/20, 20%). Overall, GQS scores were 3.5 ± 1.2 and 2.1 ± 1.5 for ChatGPT and Gemini (p = 0.002). This difference was even more marked if focusing only on "challenging" cases (1.5 ± 1.4 vs. 3.0 ± 1.5, p = 0.001). Conclusion: ChatGPT-4 showed a good analysis performance for glaucoma surgical cases, either ordinary or challenging. On the other side, Google Gemini showed strong limitations in this setting, presenting high rates of unprecise or missed answers. [ABSTRACT FROM AUTHOR]

Assessments play a vital role in postgraduate orthodontic education. They are the means to ensure postgraduate students meet the training standards of their respective programs. The visibility of student learning is crucial to the development of a postgraduate's knowledge, skill and attitudes that will eventually define the clinician's education and competence for the practice of orthodontics. This essay presents the argument for making learning visible in postgraduate orthodontic education and explains its importance. It highlights the educational theories that underpin the practice of formative feedback and articulates the significance of formative and summative assessments approaches and evaluation frameworks. Integral to formative assessments is feedback. The essay discusses three levels of feedback – macro, meso and micro – and how each can support and enhance student learning. A brief description on the significance of peer learning introduces five formative assessment classroom practices in an ERASMUS-based postgraduate orthodontic training program conducted at the National University of Singapore. They demonstrate the value of peer learning and feedback that makes learning visible to the teachers and to the postgraduate students. The essay ends with a short exploration on how technology is being harnessed to make learning visible. In particular, the current trajectory of Artificial Intelligence development, such as Open AI's ChatGPT and Google's Gemini, offers educators and students new and innovative ways to enhance teaching and learning. [ABSTRACT FROM AUTHOR]

The article examines Google Bard as an automated written corrective feedback tool, assessing its potential and limitations. Topics include the capabilities of Bard, such as multilingual translation and advanced language tasks, concerns about its use in educational contexts, and the application of Gibson's affordance theory to evaluate how Bard's features support or hinder user interactions.

The article provides an overview of the British television industry and its transition to artificial intelligence (AI). Topics discussed include the impact of AI on the stages of television content delivery, adoption of machine learning models and AI technologies in the distribution and monetisation phases, production and post-production phases, AI tools used as development engines, AI agents used for creative originality and training data, copyright, and intellectual property ownership issues.

Objective: This study aimed to evaluate the performance of large language models (LLMs) in the task of abstract screening in systematic review and meta-analysis studies, exploring their effectiveness, efficiency, and potential integration into existing human expert-based workflows. Methods: We developed automation scripts in Python to interact with the APIs of several LLM tools, including ChatGPT v4.0, ChatGPT v3.5, Google PaLM 2, and Meta Llama 2, and latest tools including ChatGPT v4.0 turbo, ChatGPT v3.5 turbo, Google Gemini 1.0 pro, Meta Llama 3, and Claude 3. This study focused on three databases of abstracts and used them as benchmarks to evaluate the performance of these LLM tools in terms of sensitivity, specificity, and overall accuracy. The results of the LLM tools were compared to human-curated inclusion decisions, gold standard for systematic review and meta-analysis studies. Results: Different LLM tools had varying abilities in abstract screening. Chat GPT v4.0 demonstrated remarkable performance, with balanced sensitivity and specificity, and overall accuracy consistently reaching or exceeding 90%, indicating a high potential for LLMs in abstract screening tasks. The study found that LLMs could provide reliable results with minimal human effort and thus serve as a cost-effective and efficient alternative to traditional abstract screening methods. Conclusion: While LLM tools are not yet ready to completely replace human experts in abstract screening, they show great promise in revolutionizing the process. They can serve as autonomous AI reviewers, contribute to collaborative workflows with human experts, and integrate with hybrid approaches to develop custom tools for increased efficiency. As technology continues to advance, LLMs are poised to play an increasingly important role in abstract screening, reshaping the workflow of systematic review and meta-analysis studies. [ABSTRACT FROM AUTHOR]

Gynecologic cancer requires personalized care to improve outcomes. Large language models (LLMs) hold the potential to provide intelligent question‐answering with reliable information about medical queries in clear and plain English, which can be understood by both healthcare providers and patients. We aimed to evaluate two freely available LLMs (ChatGPT and Google's Bard) in answering questions regarding the management of gynecologic cancer. The LLMs' performances were evaluated by developing a set questions that addressed common gynecologic oncologic findings from a patient's perspective and more complex questions to elicit recommendations from a clinician's perspective. Each question was presented to the LLM interface, and the responses generated by the artificial intelligence (AI) model were recorded. The responses were assessed based on the adherence to the National Comprehensive Cancer Network and European Society of Gynecological Oncology guidelines. This evaluation aimed to determine the accuracy and appropriateness of the information provided by LLMs. We showed that the models provided largely appropriate responses to questions regarding common cervical cancer screening tests and BRCA‐related questions. Less useful answers were received to complex and controversial gynecologic oncology cases, as assessed by reviewing the common guidelines. ChatGPT and Bard lacked knowledge of regional guideline variations, However, it provided practical and multifaceted advice to patients and caregivers regarding the next steps of management and follow up. We conclude that LLMs may have a role as an adjunct informational tool to improve outcomes. [ABSTRACT FROM AUTHOR]

Purpose: The diagnosis and management of prostate cancer (PCa), the second most common cancer in men worldwide, are highly complex. Hence, patients often seek knowledge through additional resources, including AI chatbots such as ChatGPT and Google Bard. This study aimed to evaluate the performance of LLMs in providing education on PCa. Methods: Common patient questions about PCa were collected from reliable educational websites and evaluated for accuracy, comprehensiveness, readability, and stability by two independent board-certified urologists, with a third resolving discrepancy. Accuracy was measured on a 3-point scale, comprehensiveness was measured on a 5-point Likert scale, and readability was measured using the Flesch Reading Ease (FRE) score and Flesch–Kincaid FK Grade Level. Results: A total of 52 questions on general knowledge, diagnosis, treatment, and prevention of PCa were provided to three LLMs. Although there was no significant difference in the overall accuracy of LLMs, ChatGPT-3.5 demonstrated superiority over the other LLMs in terms of general knowledge of PCa (p = 0.018). ChatGPT-4 achieved greater overall comprehensiveness than ChatGPT-3.5 and Bard (p = 0.028). For readability, Bard generated simpler sentences with the highest FRE score (54.7, p < 0.001) and lowest FK reading level (10.2, p < 0.001). Conclusion: ChatGPT-3.5, ChatGPT-4 and Bard generate accurate, comprehensive, and easily readable PCa material. These AI models might not replace healthcare professionals but can assist in patient education and guidance. [ABSTRACT FROM AUTHOR]

During the war time dysregulation of negative emotions such as fear, anger, hatred, frustration, sadness, humiliation, and hopelessness can overrule normal societal values, culture, and endanger global peace and security, and mental health in affected societies. Therefore, it is understandable that the range and power of negative emotions may play important roles in consideration of human behavior in any armed conflict. The estimation and assessment of dominant negative emotions during war time are crucial but are challenged by the complexity of emotions' neuro-psycho-physiology. Currently available natural language processing (NLP) tools have comprehensive computational methods to analyze and understand the emotional content of related textual data in war-inflicted societies. Innovative AI-driven technologies incorporating machine learning, neuro-linguistic programming, cloud infrastructure, and novel digital therapeutic tools and applications present an immense potential to enhance mental health care worldwide. This advancement could make mental health services more cost-effective and readily accessible. Due to the inadequate number of psychiatrists and limited psychiatric resources in coping with mental health consequences of war and traumas, new digital therapeutic wearable devices supported by AI tools and means might be promising approach in psychiatry of future. Transformation of negative dominant emotional maps might be undertaken by the simultaneous combination of online cognitive behavioral therapy (CBT) on individual level, as well as usage of emotionally based strategic communications (EBSC) on a public level. The proposed positive emotional transformation by means of CBT and EBSC may provide important leverage in efforts to protect mental health of civil population in war-inflicted societies. AI-based tools that can be applied in design of EBSC stimuli, like Open AI Chat GPT or Google Gemini may have great potential to significantly enhance emotionally based strategic communications by more comprehensive understanding of semantic and linguistic analysis of available text datasets of war-traumatized society. Human in the loop enhanced by Chat GPT and Gemini can aid in design and development of emotionally annotated messages that resonate among targeted population, amplifying the impact of strategic communications in shaping human dominant emotional maps into a more positive by CBT and EBCS. [ABSTRACT FROM AUTHOR]

This research study delved into the intricacies surrounding adoption of Google's Gemini and its ramifications on employee interactions, daily operational tasks, and overall job satisfaction within organizational contexts. Employing qualitative methodology, the study utilized in-depth interviews as the primary data collection method. Employing a purposive sampling technique, the participant pool comprised a diverse array of eight individuals from various organizational departments of firms in Bangkok, Thailand to ensure inclusion of different perspectives and roles. Through content analysis, the study scrutinized the multifaceted impact of Gemini on the dynamics of organizational operations. The findings revealed that the introduction of Google's Gemini significantly reverberated across three dimensions of workplace dynamics: employee interactions, execution of daily operational tasks, and the overarching sentiment of job satisfaction among employees. While integration of Gemini promises to substantially augment productivity levels and elevate workforce morale, a nuanced and balanced approach is imperative to optimize its benefits while mitigating potential drawbacks. This necessitates a deliberate focus on preserving the human elements indispensable for fostering a dynamic, innovative, and harmonious team environment amidst the technological advancements facilitated by Gemini. [ABSTRACT FROM AUTHOR]

Introduction: Artificial intelligence (AI) platforms such as ChatGPT and Bard are increasingly utilized to answer patient health care questions. We present the first study to blindly evaluate AI-generated responses to common endourology patient questions against official patient education materials. Methods: Thirty-two questions and answers spanning kidney stones, ureteral stents, benign prostatic hyperplasia (BPH), and upper tract urothelial carcinoma were extracted from official Urology Care Foundation (UCF) patient education documents. The same questions were input into ChatGPT 4.0 and Bard, limiting responses to within ±10% of the word count of the corresponding UCF response to ensure fair comparison. Six endourologists blindly evaluated responses from each platform using Likert scales for accuracy, clarity, comprehensiveness, and patient utility. Reviewers identified which response they believed was not AI generated. Finally, Flesch–Kincaid Reading Grade Level formulas assessed the readability of each platform response. Ratings were compared using analysis of variance (ANOVA) and chi-square tests. Results: ChatGPT responses were rated the highest across all categories, including accuracy, comprehensiveness, clarity, and patient utility, while UCF answers were consistently scored the lowest, all p < 0.01. A subanalysis revealed that this trend was consistent across question categories (i.e., kidney stones, BPH, etc.). However, AI-generated responses were more likely to be classified at an advanced reading level, while UCF responses showed improved readability (college or higher reading level: ChatGPT = 100%, Bard = 66%, and UCF = 19%), p < 0.001. When asked to identify which answer was not AI generated, 54.2% of responses indicated ChatGPT, 26.6% indicated Bard, and only 19.3% correctly identified it as the UCF response. Conclusions: In a blind evaluation, AI-generated responses from ChatGPT and Bard surpassed the quality of official patient education materials in endourology, suggesting that current AI platforms are already a reliable resource for basic urologic care information. AI-generated responses do, however, tend to require a higher reading level, which may limit their applicability to a broader audience. [ABSTRACT FROM AUTHOR]

Purpose: This study analyses the performance and proficiency of the three Artificial Intelligence (AI) generative chatbots (ChatGPT-3.5, ChatGPT-4.0, Bard Google AI®) and in answering the Multiple Choice Questions (MCQs) of postgraduate (PG) level orthopaedic qualifying examinations. Methods: A series of 120 mock Single Best Answer' (SBA) MCQs with four possible options named A, B, C and D as answers on various musculoskeletal (MSK) conditions covering Trauma and Orthopaedic curricula were compiled. A standardised text prompt was used to generate and feed ChatGPT (both 3.5 and 4.0 versions) and Google Bard programs, which were then statistically analysed. Results: Significant differences were found between responses from Chat GPT 3.5 with Chat GPT 4.0 (Chi square = 27.2, P < 0.001) and on comparing both Chat GPT 3.5 (Chi square = 63.852, P < 0.001) with Chat GPT 4.0 (Chi square = 44.246, P < 0.001) with. Bard Google AI® had 100% efficiency and was significantly more efficient than both Chat GPT 3.5 with Chat GPT 4.0 (p < 0.0001). Conclusion: The results demonstrate the variable potential of the different AI generative chatbots (Chat GPT 3.5, Chat GPT 4.0 and Bard Google) in their ability to answer the MCQ of PG-level orthopaedic qualifying examinations. Bard Google AI® has shown superior performance than both ChatGPT versions, underlining the potential of such large language processing models in processing and applying orthopaedic subspecialty knowledge at a PG level. [ABSTRACT FROM AUTHOR]

• Use of artificial intelligence in fibroid management. In recent years, the integration of Artificial intelligence (AI) into various fields of medicine including Gynaecology, has shown promising potential. Surgical treatment of fibroid is myomectomy if uterine preservation and fertility are the primary aims. AI usage begins with the involvement of LLM (Large Language Model) from the point when a patient visits a gynecologist, from identifying signs and symptoms to reaching a diagnosis, providing treatment plans, and patient counseling. Use of AI (ChatGPT versus Google Bard) in the surgical management of fibroid. Identifying the patient's problems using LLMs like ChatGPT and Google Bard and giving a treatment option in 8 clinical scenarios of fibroid. Data entry was done using M.S. Excel and was statistically analyzed using Statistical Package for Social Sciences (SPSS Version 26) for M.S. Windows 2010. All results were presented in tabular form. Data were analyzed using nonparametric tests Chi-square tests or Fisher exact test. p values < 0.05 were considered statistically significant. The sensitivity of both techniques was calculated. We have used Cohen's Kappa to know the degree of agreement. We found that on the first attempt, ChatGPT gave general answers in 62.5 % of cases and specific answers in 37.5 % of cases. ChatGPT showed improved sensitivity on successive prompts 37.5 % to 62.5 % on the third prompt. Google Bard could not identify the clinical question in 50 % of cases and gave incorrect answers in 12.5 % of cases (p = 0.04). Google Bard showed the same sensitivity of 25 % on all prompts. AI helps to reduce the time to diagnose and plan a treatment strategy for fibroid and acts as a powerful tool in the hands of a gynecologist. However, the usage of AI by patients for self-treatment is to be avoided and should be used only for education and counseling about fibroids. [ABSTRACT FROM AUTHOR]

OBJECTIVES: Generative artificial intelligence (AI) models such as OpenAI's ChatGPT and Google's Bard have forced educators to consider how these tools will be efficiently utilized to improve medical education. This article investigates current literature on how generative AI is and could be used and implemented in undergraduate medical education (UME). METHODS: A rapid review of the literature was performed utilizing a librarian-generated search strategy to identify articles published before June 30, 2023, in 6 databases (Pubmed, EMBASE.com, Scopus, ERIC via EBSCO, Computer Science Database via EBSCO, and CINAHL via EBSCO). Inclusion criteria were (1) a focus on osteopathic and/or allopathic UME and (2) a defined use or implementation strategy for generative AI. Two reviewers screened all articles, and data extraction was performed by 1 reviewer and confirmed by the other reviewer. RESULTS: A total of 521 relevant articles were screened during this review. Forty-one articles underwent full-text review and data extraction. The majority of the articles were opinion pieces (9), case reports (8), letters to the editor (5), editorials (5), and commentaries (3) about the use of generative AI while 7 articles used qualitative and/or quantitative methods. The literature is best divided into 5 categories of uses for generative AI in UME: nonclinical learning assistant, content developer, virtual patient interaction, clinical decision-making tutor, and medical writing. The literature indicates generative AI tools' greatest potential is for use as a virtual patient and clinical decision-making tutor. CONCLUSIONS: While the possibilities proliferate for generative AI in UME, there remains a dearth of quantitative evidence of its use for improving learner outcomes. The majority of the literature opines the potential for utilization, but only 7 studies formally evaluated the results of using generative AI. Future research should focus on the effectiveness of incorporating generative AI into preclinical and clinical curricula in UME. [ABSTRACT FROM AUTHOR]

(1) Background: Large language models (LLMs) are revolutionising various scientific fields by providing advanced support tools. However, the effectiveness of these applications depends on extensive, up-to-date databases to ensure certainty and predictive power. Transparency about information sources in Medicine remains a significant issue. (2) Methods: To evaluate Google Gemini's accuracy and reproducibility in endodontic diagnosis and treatment, 60 questions were designed based on the European Society of Endodontology Position Statements. Thirty questions were randomly selected and answered using Gemini during April 2023. Two endodontic experts independently scored the answers using a 3-point Likert scale. Discrepancies were resolved by a third expert. The relative frequency and absolute percentage of responses were detailed. Accuracy was assessed using the Wald binomial method, and repeatability was assessed using percentage agreement, Brennan and Prediger's coefficient, Conger's generalised kappa, Fleiss' kappa, Gwet's AC, and Krippendorff's alpha, all with 95% confidence intervals. Statistical analysis was performed using STATA software. (3) Results: A total of 900 answers were generated. The percentage of correct answers varied from 0% to 100% per question. Overall accuracy was 37.11% with a 95% confidence interval of 34.02–40.32%; (4) Conclusions: Gemini is not currently designed for medical use and therefore needs to be used with caution when considered for this purpose. [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: Many patients use artificial intelligence (AI) chatbots as a rapid source of health information. This raises important questions about the reliability and effectiveness of AI chatbots in delivering accurate and understandable information. Purpose: To evaluate and compare the accuracy, conciseness, and readability of responses from OpenAI ChatGPT-4 and Google Bard to patient inquiries concerning the novel 177Lu-PSMA-617 therapy for prostate cancer. Materials and Methods: Two experts listed the 12 most commonly asked questions by patients on 177Lu-PSMA-617 therapy. These twelve questions were prompted to OpenAI ChatGPT-4 and Google Bard. AI-generated responses were distributed using an online survey platform (Qualtrics) and blindly rated by eight experts. The performances of the AI chatbots were evaluated and compared across three domains: accuracy, conciseness, and readability. Additionally, potential safety concerns associated with AI-generated answers were also examined. The Mann-Whitney U and chi-square tests were utilized to compare the performances of AI chatbots. Results: Eight experts participated in the survey, evaluating 12 AI-generated responses across the three domains of accuracy, conciseness, and readability, resulting in 96 assessments (12 responses x 8 experts) for each domain per chatbot. ChatGPT-4 provided more accurate answers than Bard (2.95 ± 0.671 vs 2.73 ± 0.732, p=0.027). Bard's responses had better readability than ChatGPT-4 (2.79 ± 0.408 vs 2.94 ± 0.243, p=0.003). Both ChatGPT-4 and Bard achieved comparable conciseness scores (3.14 ± 0.659 vs 3.11 ± 0.679, p=0.798). Experts categorized the AI-generated responses as incorrect or partially correct at a rate of 16.6% for ChatGPT-4 and 29.1% for Bard. Bard's answers contained significantly more misleading information than those of ChatGPT-4 (p = 0.039). Conclusion: AI chatbots have gained significant attention, and their performance is continuously improving. Nonetheless, these technologies still need further improvements to be considered reliable and credible sources for patients seeking medical information on 177Lu-PSMA-617 therapy. [ABSTRACT FROM AUTHOR]

Background Objectives Methods Results Conclusions Artificial intelligence (AI) tools have the potential to revolutionize many facets of medicine and medical sciences research. Numerous AI tools have been developed and are in continuous states of iterative improvement in their functionality.This study aimed to assess the performance of three AI tools: The Literature, Microsoft's Copilot and Google's Gemini in performing literature reviews on a range of dermatology topics.Each tool was asked to write a literature review on five topics. The topics chosen have recently had peer‐reviewed systematic reviews published. The outputs of each took were graded on their evidence and analysis, conclusions and references on a 5‐point Likert scale by three dermatologists who are working in clinical practice, have completed the UK dermatology postgraduate training examination and are partaking in continued professional development.Across all five topics chosen, the literature reviews written by Gemini scored the highest. The mean score for Gemini for each review was 10.53, significantly higher than the mean scores achieved by The Literature (7.73) and Copilot (7.4) (p < 0.001).This paper shows that AI‐generated literature reviews can provide real‐time summaries of medical literature across a range of dermatology topics, but limitations to their comprehensiveness and accuracy are apparent. [ABSTRACT FROM AUTHOR]

As educational technology advances, the integration of Artificial Intelligence (AI)-driven chatbots in academic contexts becomes increasingly relevant. This study explored the performance of three advanced chatbots-- ChatGPT 3.5, Claude, and Google Bard--in responding to entrance exam questions for Master's and PhD. programs in Medical Laboratory Sciences in Iran in 2023. Multiple-choice questions from entrance exams in Medical Laboratory Sciences Master's and PhD. programs held in 2023 were presented to ChatGPT 3.5, Claude, and Google Bard, and their responses were evaluated. The three chatbots--ChatGPT 3.5, Claude, and Google Bard--exhibited an overall accuracy of 38%, 42%, and 37%, respectively, showcasing a comparable baseline proficiency in addressing a variety of questions. Subject-specific analysis highlighted their strengths and weaknesses in different scientific domains. Our study shows that while the evaluated chatbots showed some ability in answering medical laboratory science questions, their performance remains insufficient for success in postgraduate entrance exams. [ABSTRACT FROM AUTHOR]

The internet, particularly social media, has become a popular resource for learning about health and investigating one's own health conditions. The development of artificial intelligence (AI) chatbots has been fueled by the increasing availability of digital health data and advances in natural language processing techniques. While these chatbots are more accessible than before, they sometimes fail to provide accurate information. We used representative chatbots currently available (Chat Generative Pretrained Transformer-3.5, Bing Chat, and Google Bard) to answer questions commonly asked by brain tumor patients. The simulated situations with questions were made and selected by the brain tumor committee. These questions are commonly asked by brain tumor patients. The goal of the study was introduced to each chatbot, the situation was explained, and questions were asked. All responses were collected without modification. The answers were shown to the committee members, and they were asked to judge the responses while blinded to the type of chatbot. There was no significant difference in accuracy and communication ability among the 3 groups (P = 0.253, 0.090, respectively). For empathy, Bing Chat and Google Bard were superior to Chat Generative Pretrained Transformer (P = 0.004, 0.002, respectively). The purpose of this study was not to assess or verify the relative superiority of each chatbot. Instead, the aim was to identify the shortcomings and changes needed if AI chatbots are to be used for patient medical purposes. AI-based chatbots are a convenient way for patients and the general public to access medical information. Under such circumstances, medical professionals must ensure that the information provided to chatbot users is accurate and safe. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has become increasingly used in neurosurgery. Generative pretrained transformers (GPTs) have been of particular interest. However, ethical concerns regarding the incorporation of AI into the field remain underexplored. We delineate key ethical considerations using a novel GPT-based, human-modified approach, synthesize the most common considerations, and present an ethical framework for the involvement of AI in neurosurgery. GPT-4, ChatGPT, Bing Chat/Copilot, You, Perplexity.ai, and Google Bard were queried with the prompt "How can artificial intelligence be ethically incorporated into neurosurgery?". Then, a layered GPT-based thematic analysis was performed. The authors synthesized the results into considerations for the ethical incorporation of AI into neurosurgery. Separate Pareto analyses with 20% threshold and 10% threshold were conducted to determine salient themes. The authors refined these salient themes. Twelve key ethical considerations focusing on stakeholders, clinical implementation, and governance were identified. Refinement of the Pareto analysis of the top 20% most salient themes in the aggregated GPT outputs yielded 10 key considerations. Additionally, from the top 10% most salient themes, 5 considerations were retrieved. An ethical framework for the use of AI in neurosurgery was developed. It is critical to address the ethical considerations associated with the use of AI in neurosurgery. The framework described in this manuscript may facilitate the integration of AI into neurosurgery, benefitting both patients and neurosurgeons alike. We urge neurosurgeons to use AI only for validated purposes and caution against automatic adoption of its outputs without neurosurgeon interpretation. [ABSTRACT FROM AUTHOR]

The development of artificial intelligence (AI) raises ethical concerns about its side effects on the attitudes and behaviors of clinicians and medical practitioners. The authors aim to understand the medical ethics of AI-based chatbots and to suggest coping strategies for an emerging landscape of increased access and potential ambiguity using AI. This study examines the medical ethics of AI-based chatbots (Chat generative pretrained transformer [GPT], Bing Chat, and Google's Bard) using multiple-choice questions. ChatGPT and Bard correctly answered all questions (5/5), while Bing Chat correctly answered only 3 of 5 questions. ChatGPT explained answers simply. Bing Chat explained answers with references, and Bard provided additional explanations with details. AI has the potential to revolutionize medical fields by improving diagnosis accuracy, surgical planning, and treatment outcomes. By analyzing large amounts of data, AI can identify patterns and make predictions, aiding neurosurgeons in making informed decisions for increased patient wellbeing. As AI usage increases, the number of cases involving AI-entrusted judgments will rise, leading to the gradual emergence of ethical issues across interdisciplinary fields. The medical field will be no exception. This study suggests the need for safety measures to regulate medical ethics in the context of advancing AI. A system should be developed to verify and predict pertinent issues. [ABSTRACT FROM AUTHOR]

Introduction: This study aimed to evaluate the accuracy, completeness, precision, and readability of outputs generated by three large language models (LLMs); these are GPT by OpenAI, BARD by Google, and Bing by Microsoft, in comparison to patient education material on pelvic organ prolapse (POP) provided by the Royal College of Obstetricians and Gynecologists (RCOG). Methods: A total of 15 questions were retrieved from the RCOG website and input into the three LLMs. Two independent reviewers evaluated the outputs for accuracy, completeness, and precision. Readability was assessed using the Simplified Measure of Gobbledygook (SMOG) score and the Flesch-Kincaid Grade Level (FKGL) score. Results: Significant differences were observed in completeness and precision metrics. ChatGPT ranked highest in completeness (66.7%), while Bing led in precision (100%). No significant differences were observed in accuracy across all models. In terms of readability, ChatGPT exhibited higher difficulty than BARD, Bing, and the original RCOG answers. Conclusion: While all models displayed a variable degree of correctness, ChatGPT excelled in completeness, significantly surpassing BARD and Bing. However, Bing led in precision, providing the most relevant and concise answers. Regarding readability, ChatGPT exhibited higher difficulty. We observed that while all LLMs showed varying degrees of correctness in answering RCOG questions on patient information for POP, ChatGPT was the most comprehensive, but its answers were harder to read. Bing, on the other hand, was the most precise. The findings highlight the potential of LLMs in health information dissemination and the need for careful interpretation of their outputs. Highlights of the Study: Studies have been performed to compare the content production performance of large language models (LLMs). The analysis of the concordance of LLMs is in concordance with authoritative gynecology and obstetrics texts. This establishes a new framework for evaluating the medical content of artificial intelligence. This study also initiates readability evaluation of patient information generated by artificial intelligence in women's health. [ABSTRACT FROM AUTHOR]

This article presents a comparative analysis of three large language models (LLMs) in answering patient questions about melanoma. The LLMs evaluated were ChatGPT 3.5, ChatGPT 4.0, and Google Bard. Dermatologists scored the responses generated by the LLMs based on accuracy, reproducibility, readability, and comprehensiveness. The results showed that ChatGPT 3.5 performed the best, highlighting the limitations of relying solely on LLMs for medical information. Larger studies are needed to fully understand the impact of LLMs on the dissemination of medical information. [Extracted from the article]

This paper focused on how lecturers in linguistics can leverage GenAI to enhance their teaching proficiency and student engagement. The paper used a quantitative approach, including 293 university lecturers teaching linguistics. The TPACK theoretical model was adopted, wherein two main segments of the theory, namely technological knowledge (TK) and technological content knowledge (TCK), were used to explore the importance of leveraging GenAI to enhance teaching proficiency. The results indicated that these tools greatly enhanced the linguistics lecturers' teaching proficiency. The study revealed that a significant majority of lecturers acknowledged using Quizizz for test and exercise generation in linguistics, followed by a substantial portion who utilized the ChatGPT series for teaching and learning enhancement without compromising academic integrity. Additionally, many lecturers reported frequent usage of Education Copilot for lesson planning and student record tracking, as well as Google Bard (now Geminin) for teaching enhancement and increased student engagement. The study also found high acceptance rates among lecturers for the effectiveness of ChatGPT, Bard, Quizizz, and Educational Copilot in translation courses, with similarly positive responses for discourse analysis courses, teaching proficiency enhancement, and student engagement across various pedagogical areas. Thus, these tools can enhance teaching and learning at the university level and students' engagement when the lecturers have the Tk and the TCK. [ABSTRACT FROM AUTHOR]

The article discusses three stress tests that the future European Commission will face in relation to European antitrust policy. The first stress test evaluates the effectiveness of the EU's Article 22 referral policy and the Commission's ability to examine mergers globally. The second stress test pertains to the EU's Digital Markets Act (DMA) and its ability to regulate digital competition. The third stress test evaluates the EU's regulation of artificial intelligence (AI) and its impact on competition and innovation. The article argues that the Commission is likely to fail the first and second stress tests but has the potential to succeed in the third. The author criticizes the Commission's new guidance on Article 22 referrals, stating that it violates the principles of subsidiarity and legal certainty. The article concludes by providing guidance on how to respond to each stress test. [Extracted from the article]

In an era where dietary habits significantly impact health, technological interventions can offer personalized and accessible food choices. This paper introduces Chef Dalle, a recipe recommendation system that leverages multi-model and multimodal human-computer interaction (HCI) techniques to provide personalized cooking guidance. The application integrates voice-to-text conversion via Whisper and ingredient image recognition through GPT-Vision. It employs an advanced recipe filtering system that utilizes user-provided ingredients to fetch recipes, which are then evaluated through multi-model AI through integrations of OpenAI, Google Gemini, Claude, and/or Anthropic APIs to deliver highly personalized recommendations. These methods enable users to interact with the system using voice, text, or images, accommodating various dietary restrictions and preferences. Furthermore, the utilization of DALL-E 3 for generating recipe images enhances user engagement. User feedback mechanisms allow for the refinement of future recommendations, demonstrating the system's adaptability. Chef Dalle showcases potential applications ranging from home kitchens to grocery stores and restaurant menu customization, addressing accessibility and promoting healthier eating habits. This paper underscores the significance of multimodal HCI in enhancing culinary experiences, setting a precedent for future developments in the field. [ABSTRACT FROM AUTHOR]

Artificial intelligence and large language models (LLMs) have recently gained attention as promising tools in various healthcare domains, offering potential benefits in clinical decision-making, medical education, and research. The Advanced Burn Life Support (ABLS) program is a didactic initiative endorsed by the American Burn Association, aiming to provide knowledge on the immediate care of severely burn patients. The aim of the study was to compare the performance of three LLMs (ChatGPT-3.5, ChatGPT-4, and Google Bard) on the ABLS exam. The ABLS exam consists of 50 questions with 5 multiple-choice answers. The passing threshold is 80% of correct answers. The 3 LLMs were queried with the 50 questions included in the latest version of the ABLS exam, on July 18th, 2023. ChatGPT-3.5 scored 86% (43 out of 50), ChatGPT-4 scored 90% (45 out of 50), and Bard scored 70% (35 out of 50). No difference was measured between ChatGPT-3.5 and ChatGPT-4 (P =.538) and between ChatGPT-3.5 and Bard (P =.054), despite the borderline P -value. ChatGPT-4 performed significantly better than Bard (P =.012). Out of the 50 questions, 78% (n = 39) were direct questions, while 12% (n = 11) were presented as clinical scenarios. No difference in the rate of wrong answers was found based on the type of question for the 3 LLMs. ChatGPT-3.5 and ChatGPT-4 demonstrated high accuracy at the ABLS exam and outperformed Google Bard. However, the potential multiple applications of LLMs in emergency burn and trauma care necessitate appropriate surveillance and most likely should represent a tool to complement human cognition. [ABSTRACT FROM AUTHOR]

Background: Artificial intelligence (AI) chatbots are emerging educational tools for students in healthcare science. However, assessing their accuracy is essential prior to adoption in educational settings. This study aimed to assess the accuracy of predicting the correct answers from three AI chatbots (ChatGPT-4, Microsoft Copilot and Google Gemini) in the Italian entrance standardized examination test of healthcare science degrees (CINECA test). Secondarily, we assessed the narrative coherence of the AI chatbots' responses (i.e., text output) based on three qualitative metrics: the logical rationale behind the chosen answer, the presence of information internal to the question, and presence of information external to the question. Methods: An observational cross-sectional design was performed in September of 2023. Accuracy of the three chatbots was evaluated for the CINECA test, where questions were formatted using a multiple-choice structure with a single best answer. The outcome is binary (correct or incorrect). Chi-squared test and a post hoc analysis with Bonferroni correction assessed differences among chatbots performance in accuracy. A p-value of < 0.05 was considered statistically significant. A sensitivity analysis was performed, excluding answers that were not applicable (e.g., images). Narrative coherence was analyzed by absolute and relative frequencies of correct answers and errors. Results: Overall, of the 820 CINECA multiple-choice questions inputted into all chatbots, 20 questions were not imported in ChatGPT-4 (n = 808) and Google Gemini (n = 808) due to technical limitations. We found statistically significant differences in the ChatGPT-4 vs Google Gemini and Microsoft Copilot vs Google Gemini comparisons (p-value < 0.001). The narrative coherence of AI chatbots revealed "Logical reasoning" as the prevalent correct answer (n = 622, 81.5%) and "Logical error" as the prevalent incorrect answer (n = 40, 88.9%). Conclusions: Our main findings reveal that: (A) AI chatbots performed well; (B) ChatGPT-4 and Microsoft Copilot performed better than Google Gemini; and (C) their narrative coherence is primarily logical. Although AI chatbots showed promising accuracy in predicting the correct answer in the Italian entrance university standardized examination test, we encourage candidates to cautiously incorporate this new technology to supplement their learning rather than a primary resource. Trial registration: Not required. [ABSTRACT FROM AUTHOR]