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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.

Over the past few weeks, chatbots have become increasingly popular, and these are likely to revolutionize our lives. While the most common three chatbots, namely, OpenAI ChatGPT, Microsoft Bing Chatbot, and Google Bard, remain in their early release days, these chatbots present exciting opportunities to various users, including engineers. This short paper hopes to shed light on the current state of these three AI chatbots' understanding of engineering history, principles, concepts, and societal challenges and compare their responses to those noted by the National Academy of Engineering and the National Society of Professional Engineers. By exploring the capabilities and limitations of these chatbots, this piece hopes to foster discussions on the role of chatbots in engineering and their potential impact on our future. Then, this paper presents a case for the future integration of chatbots as co-creators of knowledge. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has become a vogue topic in the press, and descriptions of its potential impact range from apocalyptic to salvational. Interest in the topic will no doubt stimulate the search for applications to support both the technical and management systems aspects of process safety management. Within our industries, maintaining institutional memory and technical capability is made increasingly challenging by more frequent job movement among younger staff and the loss to the retirement of more senior staff. One would hope that AI could help fill the gaps caused by these factors. However, the author's sampling of current AI capabilities suggests that AI is not yet ready to do so. This paper provides some examples of errors and insufficiencies identified when seeking AI assistance in addressing process safety issues. It also suggests some existing challenges to better "training" of AI to support the needs of the process safety community. It concludes that caution should be applied, especially by less experienced personnel, when seeking AI assistance in addressing process safety–related technical matters. [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]

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]

The rapid progress in conversational AI has given rise to advanced language models capable of generating human‐like texts. Among these models, ChatGPT and Bard, developed by OpenAI and Google AI respectively, have gained significant attention. With their wide range of functionalities, such as human‐like response generation, proficiency in professional exams, complex problem solving, and more, these models have captured interest. This study presents a comprehensive survey exploring and comparing the capabilities and features of ChatGPT and Bard. We delve into their architectures, training methodologies, performance evaluations, and limitations across various domains. Ethical considerations such as biases and potential misconduct are also examined. Our findings highlight ChatGPT's exceptional performance, positioning it as a leading model. This survey is a vital resource for scholars, innovators, and interested parties operating within the domain of conversational artificial intelligence, offering valuable insights for the advancement of cutting‐edge language models. [ABSTRACT FROM AUTHOR]

Since the release of ChatGPT in November 2022, large language model-based chatbots have attracted much attention. Although businesses value their potential for efficiency gains, academics are concerned about their effects on learning and assessments. This Case enables instructors to integrate large language model-based chatbots into the curriculum. Students assume the role of a professional accountant who retrieves an artificial intelligence-written text that has to be evaluated and improved before presenting it to a client. The analysis and writing activity help students gain domain-specific accounting knowledge and improve their writing skills. Students also improve their computer literacy as they learn to engage with artificial intelligence effectively, assess generated output, and evaluate output quality. These learnings enable students to judge in which business scenarios chatbots are beneficial and when original human contributions will likely remain superior. Finally, this activity expands other cognitive skills, such as critical thinking and judgment. JEL Classifications: A22; A23; M40; M41; M49. [ABSTRACT FROM AUTHOR]

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

The discussion of dwelling in this article focuses on T. S. Eliot’s controversial axiom of poetic impersonality as articulated in The Sacred Wood (1920) and practiced in The Waste Land (1922), and on how this axiom is rearticulated by his two younger contemporaries David Jones and Basil Bunting. I argue that in The Anathémata (1952) and Briggflatts (1966), their respective masterpieces, they reintegrate the ego absconditus through their distinct geo-aesthetical self-positioning which gives rise to “the regional impersonal” mode of poetic dwelling. This article explores the complex dialectics between the (neo)modernist claim of impersonality and the affective regional identification of the self-projecting consciousness in the two poems. While sharing Eliot’s regard for the poetic artifact, Jones and Bunting rehabilitate the notion of the poet’s cultural affiliation and representativeness as well as a culturally stimulated consciousness. Their act of self-sublimation is balanced by the material and sensual anchor of their regional allegiance. Further, the Eliotean fissure between the mind that experiences and suffers and the mind that creates resulting in a cascading multiplicity of voices in The Waste Land, is healed in Jones’s and Bunting’s poetic nostos and active mode of dwelling. Also, by giving resonance to numerous names and voices, mostly disembodied and obliterated entities, Jones’s and Bunting’s poetics introduces unifying strategies of impersonation reflecting their definite geo-cultural positioning. Eliot’s original aporia is thus not resolved but re-inhabited.

BACKGROUND This study aims to evaluate the knowledge levels of chat generative pretrained transformer (ChatGPT), Bing, and Bard programs, which are three different artificial intelligence chatbots offered to the market free of charge by various manufacturers, regarding neuro-ophthalmological diseases, to examine their usability, and to investigate the existence of their superiority to each other. MATERIALS AND METHODS Forty questions related to neuro-ophthalmological diseases were obtained from the study questions’ section of the American Academy and Ophthalmology 2022–2023 Basic and Clinical Science Course Neuro-ophthalmology Book. The questions were posed to the ChatGPT, Bing, and Bard artificial intelligence chatbots. The answers were evaluated as correct or incorrect. The statistical relationship between the correct and incorrect answer rates offered by the artificial intelligence programs was tested. RESULTS The correct answer rates were given by the artificial intelligence programs to the questions asked: ChatGPT – 52.5%; Bing – 55%; and Bard – 65%. There was no statistically significant difference between the correct answer rates of the three artificial intelligence programs (P = 0.489, Pearson’s Chi-square test). CONCLUSION Although information about neuro-ophthalmological diseases can be accessed quickly and accurately using up-to-date artificial intelligence programs, the answers given may not always be correct. Care should always be taken when evaluating the answers to the questions.

Purpose To evaluate the answers to multiple-choice questions about retina and vitreous diseases and treatment modalities of Chat Generative Pre-Trained Transformer (ChatGPT), Bard, and Bing artificial intelligence chatbots, examining the level of knowledge about these subjects, and investigating the existence of their superiority over each other. Methods Forty-six questions related to retinal and vitreous diseases and treatment modalities were asked to ChatGPT, Bing, and Bard chatbots. Results The Bing artificial intelligence chatbot correctly answered 76.1% of the questions. ChatGPT and Bard artificial intelligence chatbots correctly answered 60.9% of the questions. No statistically significant difference was observed between the rates of correct and incorrect answers to the questions on the three artificial intelligence chatbots (P = 0.206). Conclusions Artificial intelligence chatbots can be used to access accurate information about retinal and vitreous diseases and treatment modalities. However, the information obtained may not always be correct, and care should be taken about its use and results.

PURPOSE: The purpose of the study was to evaluate the knowledge level of the Chat Generative Pretrained Transformer (ChatGPT), Bard, and Bing artificial intelligence (AI) chatbots regarding ocular inflammation, uveal diseases, and treatment modalities, and to investigate their relative performance compared to one another. MATERIALS AND METHODS: Thirty-six questions related to ocular inflammation, uveal diseases, and treatment modalities were posed to the ChatGPT, Bard, and Bing AI chatbots, and both correct and incorrect responses were recorded. The accuracy rates were compared using the Chi-squared test. Results: The ChatGPT provided correct answers to 52.8% of the questions, while Bard answered 38.9% correctly, and Bing answered 44.4% correctly. All three AI programs provided identical responses to 20 (55.6%) of the questions, with 45% of these responses being correct and 55% incorrect. No significant difference was observed between the correct and incorrect responses from the three AI chatbots (P = 0.654). Conclusion: AI chatbots should be developed to provide widespread access to accurate information about ocular inflammation, uveal diseases, and treatment modalities. Future research could explore ways to enhance the performance of these chatbots.

Web services play an important role in our daily lives. They are used in a wide range of activities, from online banking and shopping to education, entertainment and social interactions. Therefore, it is essential to ensure that they are kept as secure as possible. However – as is the case with any complex software system – creating a sophisticated software free from any security vulnerabilities is a very challenging task. One method to enhance software security is by employing static code analysis. This technique can be used to identify potential vulnerabilities in the source code before they are exploited by bad actors. This approach has been instrumental in tackling many vulnerabilities, but it is not without limitations. Recent research suggests that static code analysis can benefit from the use of large language models (LLMs). This is a promising line of research, but there are still very few and quite limited studies in the literature on the effectiveness of various LLMs at detecting vulnerabilities in source code. This is the research gap that we aim to address in this work. Our study examined five notable LLM chatbot models: ChatGPT 4, ChatGPT 3.5, Claude, Bard/Gemini1, and Llama-2, assessing their abilities to identify 104 known vulnerabilities spanning the Top-10 categories defined by the Open Worldwide Application Security Project (OWASP). Moreover, we evaluated issues related to these LLMs’ false-positive rates using 97 patched code samples. We specifically focused on PHP vulnerabilities, given its prevalence in web applications. We found that ChatGPT-4 has the highest vulnerability detection rate, with over 61.5% of vulnerabilities found, followed by ChatGPT-3.5 at 50%. Bard has the highest rate of vulnerabilities missed, at 53.8%, and the lowest detection rate, at 13.4%. For all models, there is a significant percentage of vulnerabilities that were classified as partially found, indicating a level of uncertainty or incomplete detection across all tested LLMs. Moreover, we found that ChatGPT-4 and ChatGPT-3.5 are consistently more effective across most categories, compared to other models. Bard and Llama-2 display limited effectiveness in detecting vulnerabilities across the majority of categories listed. Surprisingly, our findings reveal high false positive rates across all LLMs. Even the model demonstrating the best performance (ChatGPT-4) notched a false positive rate of nearly 63%, while several models glaringly under-performed, hitting startlingly bad false positive rates of over 90%. Finally, simultaneously deploying multiple LLMs for static analysis resulted in only a marginal enhancement in the rates of vulnerability detection. We believe these results are generalizable to most other programming languages, and hence far from being limited to PHP only.

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

Abstract This study explores disparities and opportunities in healthcare information provided by AI chatbots. We focused on recommendations for adjuvant therapy in endometrial cancer, analyzing responses across four regions (Indonesia, Nigeria, Taiwan, USA) and three platforms (Bard, Bing, ChatGPT-3.5). Utilizing previously published cases, we asked identical questions to chatbots from each location within a 24-h window. Responses were evaluated in a double-blinded manner on relevance, clarity, depth, focus, and coherence by ten experts in endometrial cancer. Our analysis revealed significant variations across different countries/regions (p

BACKGROUND: This study aims to evaluate the knowledge levels of chat generative pretrained transformer (ChatGPT), Bing, and Bard programs, which are three different artificial intelligence chatbots offered to the market free of charge by various manufacturers, regarding neuro-ophthalmological diseases, to examine their usability, and to investigate the existence of their superiority to each other. MATERIALS AND METHODS: Forty questions related to neuro-ophthalmological diseases were obtained from the study questions' section of the American Academy and Ophthalmology 2022-2023 Basic and Clinical Science Course Neuro-ophthalmology Book. The questions were posed to the ChatGPT, Bing, and Bard artificial intelligence chatbots. The answers were evaluated as correct or incorrect. The statistical relationship between the correct and incorrect answer rates offered by the artificial intelligence programs was tested. RESULTS: The correct answer rates were given by the artificial intelligence programs to the questions asked: ChatGPT - 52.5%; Bing - 55%; and Bard - 65%. There was no statistically significant difference between the correct answer rates of the three artificial intelligence programs (P = 0.489, Pearson's Chi-square test). CONCLUSION: Although information about neuro-ophthalmological diseases can be accessed quickly and accurately using up-to-date artificial intelligence programs, the answers given may not always be correct. Care should always be taken when evaluating the answers to the questions. [ABSTRACT FROM AUTHOR]

Background & Aims: Non-alcoholic fatty liver disease (NAFLD) is related to an increased atherosclerotic cardiovascular disease (ASCVD) risk. This study investigated a potential relationship between liver fibrosis scores (LFS) reflecting NAFLD and ascending aortic dilatation (AAD) Methods: This is an observational and cross-sectional study. Patients were consecutively enrolled from a cardiology clinic. The NAFLD fibrosis score (NFS), fibrosis-4 (FIB-4) index, aspartate aminotransferase (AST) to platelet ratio (APRI), and BARD scores of each patient were calculated. The ascending aortic diameters were evaluated by transthoracic echocardiography according to current clinical guidelines. The patients were allocated into two groups with and without AAD. Results: A total of 272 patients were included in the study. In AAD group, age, patients with hypertension (HT), coronary artery disease (CAD), FIB-4 index, BARD score and the NFS were significantly higher. As compared to the AAD group, body mass index (BMI), hemoglobin, and diuretic use were significantly higher in patients without aortic dilatation. The NFS with AAD, and NFS and FIB-4 index with indexed aortic diameter (AI) showed significant positive correlation (R=0.546, R=0.332, R=0.314 with p<0.001, respectively). In multivariate logistic regression analysis hemoglobin levels (OR=0.728, 95%CI: 0.553-0.958; p=0.023), BMI (OR=0.762, 95%CI: 0.668-0.869, p<0.001), HT (OR=3.269, 95%CI: 1.045-10.220; p=0.042), BARD score (OR=1.248, 95%CIL 0.815-1.955; p=0.044), and FIB-4 index (OR=2.432, 95%CI: 1.395-4.246; p=0.002) were found to be independently related to AAD. Conclusions: Our study demonstrated a statistically significant relationship between NFS, FIB-4 index, BARD score and AAD. The presence of positive correlation among LFS and AAD in our study is remarkable. This may emphasize the increased risk of AAD in NAFLD. [ABSTRACT FROM AUTHOR]

Objectives To evaluate the accuracy of ChatGPT and Bard in answering pathology examination questions requiring image interpretation. Methods The study evaluated ChatGPT-4 and Bard's performance using 86 multiple-choice questions, with 17 (19.8%) focusing on general pathology and 69 (80.2%) on systemic pathology. Of these, 62 (72.1%) included microscopic images, and 57 (66.3%) were first-order questions focusing on diagnosing the disease. The authors presented these artificial intelligence (AI) tools with questions, both with and without clinical contexts, and assessed their answers against a reference standard set by pathologists. Results ChatGPT-4 achieved a 100% (n = 86) accuracy rate in questions with clinical context, surpassing Bard's 87.2% (n = 75). Without context, the accuracy of both AI tools declined significantly, with ChatGPT-4 at 52.3% (n = 45) and Bard at 38.4% (n = 33). ChatGPT-4 consistently outperformed Bard across various categories, particularly in systemic pathology and first-order questions. A notable issue identified was Bard's tendency to "hallucinate" or provide plausible but incorrect answers, especially without clinical context. Conclusions This study demonstrated the potential of ChatGPT and Bard in pathology education, stressing the importance of clinical context for accurate AI interpretations of pathology images. It underlined the need for careful AI integration in medical education. [ABSTRACT FROM AUTHOR]

Large language models (LLMs) like ChatGPT and Bard have the potential to greatly benefit healthcare, including the field of uveitis care. LLMs can provide accessible advice for patients, helping them make decisions about when to seek medical attention or escalate their treatment. They can also assist in the diagnosis of uveitis, although more research specific to uveitis is needed. LLMs can improve the consultation experience by engaging with patients and providing empathetic responses. Additionally, they can aid in medication management and streamline administrative processes. LLMs have the potential to accelerate research in uveitis by automating tasks such as data analysis and identifying potential drug targets. However, there are ethical considerations to address, including patient autonomy, bias, transparency, and accountability. Efforts should be made to ensure equity and accessibility in the implementation of LLMs, and regulatory frameworks should be developed to ensure responsible use. The future directions of LLMs in uveitis care include integrating them with visual and auditory processing and training them in real-time. While LLMs show promise, more research is needed to develop them with reliability and replicability. [Extracted from the article]

This paper analyses the phenomenology and epistemology of chatbots such as ChatGPT and Bard. The computational architecture underpinning these chatbots are large language models (LLMs), which are generative artificial intelligence (AI) systems trained on a massive dataset of text extracted from the Web. We conceptualise these LLMs as multifunctional computational cognitive artifacts, used for various cognitive tasks such as translating, summarizing, answering questions, information-seeking, and much more. Phenomenologically, LLMs can be experienced as a "quasi-other"; when that happens, users anthropomorphise them. For most users, current LLMs are black boxes, i.e., for the most part, they lack data transparency and algorithmic transparency. They can, however, be phenomenologically and informationally transparent, in which case there is an interactional flow. Anthropomorphising and interactional flow can, in some users, create an attitude of (unwarranted) trust towards the output LLMs generate. We conclude this paper by drawing on the epistemology of trust and testimony to examine the epistemic implications of these dimensions. Whilst LLMs generally generate accurate responses, we observe two epistemic pitfalls. Ideally, users should be able to match the level of trust that they place in LLMs to the degree that LLMs are trustworthy. However, both their data and algorithmic opacity and their phenomenological and informational transparency can make it difficult for users to calibrate their trust correctly. The effects of these limitations are twofold: users may adopt unwarranted attitudes of trust towards the outputs of LLMs (which is particularly problematic when LLMs hallucinate), and the trustworthiness of LLMs may be undermined. [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]

• First study to compare nutrients in meal plans created by ChatGPT and Bard. • Most meal plans fell below the requirements for energy exceeded them for protein and were below the requirements for vitamin D and fluoride. • ChatGPT occasionally recommended vitamin B 12 supplementation for vegan meal plans. • Bard included milk-based products in vegan diets. • Currently, ChatGPT offers a slight advantage over Bard in the generation of meal plans. Artificial intelligence chatbots based on large language models have recently emerged as an alternative to traditional online searches and are also entering the nutrition space. In this study, we wanted to investigate whether the artificial intelligence chatbots ChatGPT and Bard (now Gemini) can create meal plans that meet the dietary reference intake (DRI) for different dietary patterns. We further hypothesized that nutritional adequacy could be improved by modifying the prompts used. Meal plans were generated by 3 accounts for different dietary patterns (omnivorous, vegetarian, and vegan) using 2 distinct prompts resulting in 108 meal plans total. The nutrient content of the plans was subsequently analyzed and compared to the DRIs. On average, the meal plans contained less energy and carbohydrates but mostly exceeded the DRI for protein. Vitamin D and fluoride fell below the DRI for all plans, whereas only the vegan plans contained insufficient vitamin B 12. ChatGPT suggested using vitamin B 12 supplements in 5 of 18 instances, whereas Bard never recommended supplements. There were no significant differences between the prompts or the tools. Although the meal plans generated by ChatGPT and Bard met most DRIs, there were some exceptions, particularly for vegan diets. These tools maybe useful for individuals looking for general dietary inspiration, but they should not be relied on to create nutritionally adequate meal plans, especially for individuals with restrictive dietary needs. We compared the nutritional adequacy of meal plans generated by the artificial intelligence tools ChatGPT and Bard for omnivorous, vegetarian, and vegan meal plans. Although most plans exceeded protein requirements, they consistently fell short in energy and carbohydrate content. Deficiencies in vitamin D, fluoride, and vitamin B 12 for vegan plans were noted. ChatGPT occasionally recommended vitamin B 12 supplementation for vegans, whereas Bard did not. Bard further included milk-based products in the vegan plans. Overall, the analysis underscores strengths and limitations, especially for more restrictive dietary patterns in artificial intelligence–generated meal plans. [Display omitted] [ABSTRACT FROM AUTHOR]

Purpose: Patients may seek online information to better understand medical imaging procedures. The purpose of this study was to assess the accuracy of information provided by 2 popular artificial intelligence (AI) chatbots pertaining to common imaging scenarios' risks, benefits, and alternatives. Methods: Fourteen imaging-related scenarios pertaining to computed tomography (CT) or magnetic resonance imaging (MRI) were used. Factors including the use of intravenous contrast, the presence of renal disease, and whether the patient was pregnant were included in the analysis. For each scenario, 3 prompts for outlining the (1) risks, (2) benefits, and (3) alternative imaging choices or potential implications of not using contrast were inputted into ChatGPT and Bard. A grading rubric and a 5-point Likert scale was used by 2 independent reviewers to grade responses. Prompt variability and chatbot context dependency were also assessed. Results: ChatGPT's performance was superior to Bard's in accurately responding to prompts per Likert grading (4.36 ± 0.63 vs 3.25 ± 1.03 seconds, P <.0001). There was substantial agreement between independent reviewer grading for ChatGPT (κ = 0.621) and Bard (κ = 0.684). Response text length was not statistically different between ChatGPT and Bard (2087 ± 256 characters vs 2162 ± 369 characters, P =.24). Response time was longer for ChatGPT (34 ± 2 vs 8 ± 1 seconds, P <.0001). Conclusions: ChatGPT performed superior to Bard at outlining risks, benefits, and alternatives to common imaging scenarios. Generally, context dependency and prompt variability did not change chatbot response content. Due to the lack of detailed scientific reasoning and inability to provide patient-specific information, both AI chatbots have limitations as a patient information resource. [ABSTRACT FROM AUTHOR]

This study explores disparities and opportunities in healthcare information provided by AI chatbots. We focused on recommendations for adjuvant therapy in endometrial cancer, analyzing responses across four regions (Indonesia, Nigeria, Taiwan, USA) and three platforms (Bard, Bing, ChatGPT-3.5). Utilizing previously published cases, we asked identical questions to chatbots from each location within a 24-h window. Responses were evaluated in a double-blinded manner on relevance, clarity, depth, focus, and coherence by ten experts in endometrial cancer. Our analysis revealed significant variations across different countries/regions (p < 0.001). Interestingly, Bing's responses in Nigeria consistently outperformed others (p < 0.05), excelling in all evaluation criteria (p < 0.001). Bard also performed better in Nigeria compared to other regions (p < 0.05), consistently surpassing them across all categories (p < 0.001, with relevance reaching p < 0.01). Notably, Bard's overall scores were significantly higher than those of ChatGPT-3.5 and Bing in all locations (p < 0.001). These findings highlight disparities and opportunities in the quality of AI-powered healthcare information based on user location and platform. This emphasizes the necessity for more research and development to guarantee equal access to trustworthy medical information through AI technologies. [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]

Les Printemps de la Médecine Interne sont des journées de formations pour les internistes francophones. Les cas cliniques présentés à ces journées sont complexes. Cette étude vise à évaluer les capacités diagnostiques des intelligences artificielles non spécialisées (modèles de langage) ChatGPT-4 et Bard en les confrontant aux énigmes des Printemps de la Médecine Interne. Les cas cliniques des Printemps de la Médecine Interne 2021 et 2022 ont été soumis à deux modèles de langage : ChatGPT-4 et Bard. En cas de mauvaise réponse, une seconde tentative était offerte. Nous avons ensuite comparé les réponses des experts internistes humains à celles des intelligences artificielles. Sur les 12 cas cliniques soumis, les experts internistes ont trouvé neuf diagnostics, ChatGPT-4 trois et Bard un. Un des cas résolus par ChatGPT-4 n'a pas été résolu par l'expert interniste. Les intelligences artificielles avaient un temps de réponse de quelques secondes. À l'heure actuelle, les compétences diagnostiques de ChatGPT-4 et Bard sont inférieures à celles d'experts humains pour la résolution de cas cliniques complexes mais sont très prometteuses. Accessibles au grand public depuis peu, elles ont déjà des capacités impressionnantes, interrogeant le rôle du médecin diagnosticien. Il faudrait envisager d'adapter les règles ou les sujets des futurs Printemps de la Médecine Interne afin qu'ils ne soient pas résolus par un modèle de langage grand public. The "Printemps de la Médecine Interne" are training days for Francophone internists. The clinical cases presented during these days are complex. This study aims to evaluate the diagnostic capabilities of non-specialized artificial intelligence (language models) ChatGPT-4 and Bard by confronting them with the puzzles of the "Printemps de la Médecine Interne". Clinical cases from the "Printemps de la Médecine Interne" 2021 and 2022 were submitted to two language models: ChatGPT-4 and Bard. In case of a wrong answer, a second attempt was offered. We then compared the responses of human internist experts to those of artificial intelligence. Of the 12 clinical cases submitted, human internist experts diagnosed nine, ChatGPT-4 diagnosed three, and Bard diagnosed one. One of the cases solved by ChatGPT-4 was not solved by the internist expert. The artificial intelligence had a response time of a few seconds. Currently, the diagnostic skills of ChatGPT-4 and Bard are inferior to those of human experts in solving complex clinical cases but are very promising. Recently made available to the general public, they already have impressive capabilities, questioning the role of the diagnostic physician. It would be advisable to adapt the rules or subjects of future "Printemps de la Médecine Interne" so that they are not solved by a public language model. [ABSTRACT FROM AUTHOR]

AI chatbots have emerged as powerful tools for providing text-based solutions to a wide range of everyday challenges. Selecting the appropriate chatbot is crucial for optimising outcomes. This paper presents a comprehensive comparative analysis of five leading chatbots: ChatGPT, Bard, Llama, Ernie, and Grok. The analysis is based on a systematic review of 28 scholarly articles. The review indicates that ChatGPT, developed by OpenAI, excels in educational, medical, humanities, and writing applications but struggles with real-time data accuracy and lacks open-source flexibility. Bard, powered by Google, leverages real-time internet data for problem solving and shows potential in competitive quiz environments, albeit with performance variability and inconsistencies in responses. Llama, an open-source model from Meta, demonstrates significant promise in medical contexts, natural language processing, and personalised educational tools, yet it requires substantial computational resources. Ernie, developed by Baidu, specialises in Chinese language tasks, thus providing localised advantages that may not extend globally due to restrictive policies. Grok, developed by Xai and still in its early stages, shows promise in providing engaging, real-time interactions, humour, and mathematical reasoning capabilities, but its full potential remains to be evaluated through further development and empirical testing. The findings underscore the context-dependent utility of each model and the absence of a singularly superior chatbot. Future research should expand to include a wider range of fields, explore practical applications, and address concerns related to data privacy, ethics, security, and the responsible deployment of these technologies. [ABSTRACT FROM AUTHOR]

This study conducts a comparative analysis of two prominent generative artificial intelligence (GAI) tools, ChatGPT and Bard, specifically in the context of supply chain management. Using a dataset of 150 certified supply chain professional questions, the models are evaluated on the basis of accuracy, relevance, and clarity, and t tests are employed to assess differences between the tools. ChatGPT outperforms Bard in both accuracy and relevance, with statistically significant results, whereas Bard demonstrated a slight edge in readability, scoring higher on the Flesch readability ease scale. Both models exhibited moderate to high cosine similarity for the majority of the questions, indicating closely aligned outputs. However, variations in their performance arose from differences in their underlying architectures – ChatGPT’s iterative improvement process balances utility and safety, whereas Bard is designed with stricter safeguards to minimize misuse. These findings have important implications for the integration of GAI tools in educational settings, such as developing supply chain curricula and training materials requiring high accuracy and relevance. Additionally, the results suggest broader applications of the GAI in supply chain decision-making, operational efficiency improvements, and enhanced stakeholder communication. The study also highlights the importance of continuous model adaptation to ensure the ethical, safe, and effective use of AI technologies in professional settings. Future research could explore how real-time feedback loops impact AI performance and how diverse training datasets influence model accuracy and relevance across different industries, further advancing the role of AI in complex domains such as supply chain management.

Abstract The rapid progress in conversational AI has given rise to advanced language models capable of generating human‐like texts. Among these models, ChatGPT and Bard, developed by OpenAI and Google AI respectively, have gained significant attention. With their wide range of functionalities, such as human‐like response generation, proficiency in professional exams, complex problem solving, and more, these models have captured interest. This study presents a comprehensive survey exploring and comparing the capabilities and features of ChatGPT and Bard. We delve into their architectures, training methodologies, performance evaluations, and limitations across various domains. Ethical considerations such as biases and potential misconduct are also examined. Our findings highlight ChatGPT's exceptional performance, positioning it as a leading model. This survey is a vital resource for scholars, innovators, and interested parties operating within the domain of conversational artificial intelligence, offering valuable insights for the advancement of cutting‐edge language models.

Generative artificial intelligence (AI) is a type of AI that can create new content, such as text, images or music. Generative AI may revolutionise healthcare communication through changing the way that health education is delivered to both patients and health practitioners. Chatbots, as one implementation example of generative AI, interact with users via text- or voice-based conversations and employ natural language-processing techniques to comprehend and interpret user inputs and generate suitable responses. This article provides an overview of generative AI and explores the characteristics of two brands of chatbot systems, ChatGPT and Bard. Potential future research directions are described for lymphoedema care. [ABSTRACT FROM AUTHOR]

Objective In the digital age, patients turn to online sources for lumbar spine fusion information, necessitating a careful study of large language models (LLMs) like chat generative pre-trained transformer (ChatGPT) for patient education. Methods Our study aims to assess the response quality of Open AI (artificial intelligence)’s ChatGPT 3.5 and Google’s Bard to patient questions on lumbar spine fusion surgery. We identified 10 critical questions from 158 frequently asked ones via Google search, which were then presented to both chatbots. Five blinded spine surgeons rated the responses on a 4-point scale from ‘unsatisfactory’ to ‘excellent.’ The clarity and professionalism of the answers were also evaluated using a 5-point Likert scale. Results In our evaluation of 10 questions across ChatGPT 3.5 and Bard, 97% of responses were rated as excellent or satisfactory. Specifically, ChatGPT had 62% excellent and 32% minimally clarifying responses, with only 6% needing moderate or substantial clarification. Bard’s responses were 66% excellent and 24% minimally clarifying, with 10% requiring more clarification. No significant difference was found in the overall rating distribution between the 2 models. Both struggled with 3 specific questions regarding surgical risks, success rates, and selection of surgical approaches (Q3, Q4, and Q5). Interrater reliability was low for both models (ChatGPT: k = 0.041, p = 0.622; Bard: k = -0.040, p = 0.601). While both scored well on understanding and empathy, Bard received marginally lower ratings in empathy and professionalism. Conclusion ChatGPT3.5 and Bard effectively answered lumbar spine fusion FAQs, but further training and research are needed to solidify LLMs’ role in medical education and healthcare communication.

Background: Artificial intelligence (AI) has the potential to transform preoperative planning for breast reconstruction by enhancing the efficiency, accuracy, and reliability of radiology reporting through automatic interpretation and perforator identification. Large language models (LLMs) have recently advanced significantly in medicine. This study aimed to evaluate the proficiency of contemporary LLMs in interpreting computed tomography angiography (CTA) scans for deep inferior epigastric perforator (DIEP) flap preoperative planning. Methods: Four prominent LLMs, ChatGPT-4, BARD, Perplexity, and BingAI, answered six questions on CTA scan reporting. A panel of expert plastic surgeons with extensive experience in breast reconstruction assessed the responses using a Likert scale. In contrast, the responses’ readability was evaluated using the Flesch Reading Ease score, the Flesch-Kincaid Grade level, and the Coleman-Liau Index. The DISCERN score was utilized to determine the responses’ suitability. Statistical significance was identified through a t-test, and P-values < 0.05 were considered significant. Results: BingAI provided the most accurate and useful responses to prompts, followed by Perplexity, ChatGPT, and then BARD. BingAI had the greatest Flesh Reading Ease (34.7±5.5) and DISCERN (60.5±3.9) scores. Perplexity had higher Flesch-Kincaid Grade level (20.5±2.7) and Coleman-Liau Index (17.8±1.6) scores than other LLMs. Conclusion: LLMs exhibit limitations in their capabilities of reporting CTA for preoperative planning of breast reconstruction, yet the rapid advancements in technology hint at a promising future. AI stands poised to enhance the education of CTA reporting and aid preoperative planning. In the future, AI technology could provide automatic CTA interpretation, enhancing the efficiency, accuracy, and reliability of CTA reports.

In postoperative care, patient education and follow-up are pivotal for enhancing the quality of care and satisfaction. Artificial intelligence virtual assistants (AIVA) and large language models (LLMs) like Google BARD and ChatGPT-4 offer avenues for addressing patient queries using natural language processing (NLP) techniques. However, the accuracy and appropriateness of the information vary across these platforms, necessitating a comparative study to evaluate their efficacy in this domain. We conducted a study comparing AIVA (using Google Dialogflow) with ChatGPT-4 and Google BARD, assessing the accuracy, knowledge gap, and response appropriateness. AIVA demonstrated superior performance, with significantly higher accuracy (mean: 0.9) and lower knowledge gap (mean: 0.1) compared to BARD and ChatGPT-4. Additionally, AIVA’s responses received higher Likert scores for appropriateness. Our findings suggest that specialized AI tools like AIVA are more effective in delivering precise and contextually relevant information for postoperative care compared to general-purpose LLMs. While ChatGPT-4 shows promise, its performance varies, particularly in verbal interactions. This underscores the importance of tailored AI solutions in healthcare, where accuracy and clarity are paramount. Our study highlights the necessity for further research and the development of customized AI solutions to address specific medical contexts and improve patient outcomes.

Medical institutions continuously create a substantial amount of data that is used for scientific research. One of the departments with a great amount of archived data is the pathology department. Pathology archives hold the potential to create a case series of valuable rare entities or large cohorts of common entities. The major problem in creation of these databases is data extraction which is still commonly done manually and is highly laborious and error prone. For these reasons, we offer using large language models to overcome these challenges. Ten pathology reports of selected resection specimens were retrieved from electronic archives of Koç University Hospital for the initial set. These reports were de-identified and uploaded to ChatGPT and Google Bard. Both algorithms were asked to turn the reports in a synoptic report format that is easy to export to a data editor such as Microsoft Excel or Google Sheets. Both programs created tables with Google Bard facilitating the creation of a spreadsheet from the data automatically. In conclusion, we propose the use of AI-assisted data extraction for academic research purposes, as it may enhance efficiency and precision compared to manual data entry.