Search Results (32)

Urinary tract infections (UTIs) are a common pathology worldwide, frequently associated with kidney stones. We aimed to determine how artificial intelligence (AI) could assist and enhance human medical activities in this field. We performed a search in PubMed using different sets of keywords. When using the keywords “AI, artificial intelligence, urinary tract infections, Escherichia coli (E. coli)”, we identified 16 papers, 12 of which fulfilled our research criteria. When using the keywords “urolithiasis, AI, artificial intelligence”, we identified 72 results, 30 of which were suitable for analysis. We identified that AI/machine learning can be used to detect Gram-negative bacilli involved in UTIs in a fast and accurate way and to detect antibiotic-resistant genes in E. coli. The most frequent AI applications for urolithiasis can be summarized into three categories: The first category relates to patient follow-up, trying to improve physical and medical conditions after specific urologic surgical procedures. The second refers to urinary stone disease (USD), focused on stone evaluation, using different AI and machine learning systems, regarding the stone’s composition in terms of uric acid, its dimensions, its volume, and its speed of detection. The third category comprises the comparison of the ChatGPT-4, Bing AI, Grok, Claude, and Perplexity chatbots in different applications for urolithiasis. ChatGPT-4 has received the most positive evaluations. In conclusion, the impressive number of papers published on different applications of AI in UTIs and urology suggest that machine learning will be exploited effectively in the near future to optimize patient follow-up, diagnosis, and treatment. Full article

Background/Objectives: In recent years, interest has grown in the clinical applications of artificial intelligence (AI)-based chatbots powered by large language models (LLMs) in oral and maxillofacial surgery (OMFS). However, there are conflicting opinions regarding the accuracy and reliability of the information they provide, raising questions about their potential role as support tools for both clinicians and patients. This systematic review aims to analyze the current literature on the use of conversational agents powered by LLMs in the field of OMFS. Methods: The review was conducted following PRISMA guidelines and the Cochrane Handbook for Systematic Reviews of Interventions. Original studies published between 2023 and 2024 in peer-reviewed English-language journals were included. Sources were identified through major electronic databases, including PubMed, Scopus, Google Scholar, and Web of Science. The risk of bias in the included studies was assessed using the ROBINS-I tool, which evaluates potential bias in study design and conduct. Results: A total of 49 articles were identified, of which 4 met the inclusion criteria. One study showed that ChatGPT provided the most accurate responses compared to Microsoft Copilot (ex-Bing) and Google Gemini (ex-Bard) for questions related to OMFS. Other studies highlighted that ChatGPT-4 can assist surgeons with quick and relevant information, though responses may vary depending on the quality of the questions. Conclusions: Chatbots powered by LLMs can enhance efficiency and decision-making in OMFS routine clinical cases. However, based on the limited number of studies included in this review (four), their performance remains constrained in complex clinical scenarios and in managing emotionally sensitive patient interactions. Further research on clinical validation, prompt formulation, and ethical oversight is essential to safely integrating LLM technologies into OMFS practices. Full article

The gut microbiota plays a central role in host energy metabolism and the development of metabolic disorders, partly through its influence on mitochondrial function. Probiotic supplementation, particularly with Lactobacillus rhamnosus GG, has been proposed as a strategy to modulate the microbiota and improve host metabolic health. Adolescent binge-like alcohol consumption is a critical public health issue known to induce neuroinflammation, oxidative stress, mitochondrial dysfunction, and intestinal dysbiosis, contributing to disorders such as alcoholic liver disease (ALD). This study aimed to evaluate the effects of L. rhamnosus GG supplementation on mitochondrial physiology in Sprague Dawley rats exposed to binge-like ethanol (BEP group) or saline (SP group) during adolescence (postnatal days 30–43). Starting on postnatal day 44, L. rhamnosus GG was administered orally for 28 days. Fecal colonization was confirmed by qPCR, and mitochondrial function was assessed in the liver, heart, and bone marrow through quantification of NADH, ATP, ADP/ATP ratio, total antioxidant capacity, and the expression of mitochondrial genes Higd2a, MnSOD1, and AMPKα1. L. rhamnosus GG supplementation induced tissue-specific mitochondrial adaptations. In the liver, it increased Higd2a expression and restored antioxidant and energy balance in ethanol-exposed rats. In the bone marrow, it reversed ethanol-induced metabolic stress and enhanced AMPKα1 expression. In contrast, in the heart, L. rhamnosus GG had minimal impact on mitochondrial energy markers but increased antioxidant capacity, indicating a more limited, redox-focused effect. These findings suggest that L. rhamnosus GG exerts context-dependent, tissue-specific benefits on mitochondrial physiology, primarily through the modulation of antioxidant defenses, activation of AMPKα1, and remodeling of respiratory complexes. This probiotic may represent a promising therapeutic strategy to mitigate mitochondrial dysfunction associated with early-life alcohol exposure. Full article

Objective: This study aimed to evaluate the compliance of four different artificial intelligence applications (ChatGPT-4.0, Bing AI, Google Bard, and Perplexity) with the American Urological Association (AUA) vesicoureteral reflux (VUR) management guidelines. Materials and Methods: Fifty-one questions derived from the AUA guidelines were asked of each AI application. Two experienced paediatric surgeons independently scored the responses using a five-point Likert scale. Inter-rater agreement was analysed using the intraclass correlation coefficient (ICC). Results: ChatGPT-4.0, Bing AI, Google Bard, and Perplexity received mean scores of 4.91, 4.85, 4.75 and 4.70 respectively. There was no statistically significant difference between the accuracy of the AI applications (p = 0.223). The inter-rater ICC values were above 0.9 for all platforms, indicating a high level of consistency in scoring. Conclusions: The evaluated AI applications agreed highly with the AUA VUR management guidelines. These results suggest that AI applications may be a potential tool for providing guideline-based recommendations in paediatric urology. Full article

Background: As digital health resources become increasingly prevalent, assessing the quality of information provided by publicly available AI tools is vital for evidence-based patient education. Objective: This study evaluates the accuracy and readability of responses from four large language models—ChatGPT 4.0, ChatGPT 3.5, Google Bard, and Microsoft Bing—in providing contraceptive counseling. Methods: A cross-sectional analysis was conducted using standardized contraception questions, established readability indices, and a panel of blinded OB/GYN physician reviewers comparing model responses to an AAFP benchmark. Results: The models varied in readability and evidence adherence; notably, ChatGPT 3.5 provided more evidence-based responses than GPT-4.0, although all outputs exceeded the recommended 6th-grade reading level. Conclusions: Our findings underscore the need for the further refinement of LLMs to balance clinical accuracy with patient-friendly language, supporting their role as a supplement to clinician counseling. Full article

Human communication has long relied on visual media for interaction, and is facilitated by electronic devices that access visual data. Traditionally, this exchange was unidirectional, constrained to text-based queries. However, advancements in human–computer interaction have introduced technologies like reverse image search and large language models (LLMs), enabling both textual and visual queries. These innovations are particularly valuable in Cultural Heritage applications, such as connecting tourists with point-of-interest recognition systems during city visits. This paper investigates the use of various Vision Language Models (VLMs) for Cultural Heritage visual question aswering, including Bing’s search engine with GPT-4 and open models such as Qwen2-VL and Pixtral. Twenty Italian landmarks were selected for the study, including the Colosseum, Milan Cathedral, and Michelangelo’s David. For each landmark, two images were chosen: one from Wikipedia and another from a scientific database or private collection. These images were input into each VLM with textual queries regarding their content. We studied the quality of the responses in terms of their completeness, assessing the impact of various levels of detail in the queries. Additionally, we explored the effect of language (English vs. Italian) on the models’ ability to provide accurate answers. Our findings indicate that larger models, such as Qwen2-VL and Bing+ChatGPT-4, which are trained on multilingual datasets, perform better in both English and Italian. Iconic landmarks like the Colosseum and Florence’s Duomo are easily recognized, and providing context (e.g., the city) improves identification accuracy. Surprisingly, the Wikimedia dataset did not perform as expected, with varying results across models. Open models like Qwen2-VL, which can run on consumer workstations, showed performance similar to larger models. While the algorithms demonstrated strong results, they also generated occasional hallucinated responses, highlighting the need for ongoing refinement of AI systems for Cultural Heritage applications. Full article

Background/Objectives: Using chatbots to seek healthcare information is becoming more popular. Misinformation and gaps in knowledge exist regarding the risk and benefits of testosterone replacement therapy (TRT). We aimed to assess and compare the quality and readability of responses generated by four AI chatbots. Methods: ChatGPT, Google Bard, Bing Chat, and Perplexity AI were asked the same eleven questions regarding TRT. The responses were evaluated by four reviewers using DISCERN and Patient Education Materials Assessment Tool (PEMAT) questionnaires. Readability was assessed using the Readability Scoring system v2.0. to calculate the Flesch–Kincaid Reading Ease Score (FRES) and the Flesch–Kincaid Grade Level (FKGL). Kruskal–Wallis statistics were completed using GraphPad Prism V10.1.0. Results: Google Bard received the highest DISCERN (56.5) and PEMAT (96% understandability and 74% actionability), demonstrating the highest quality. The readability scores ranged from eleventh-grade level to college level, with Perplexity outperforming the other chatbots. Significant differences were found in understandability between Bing and Google Bard, DISCERN scores between Bing and Google Bard, FRES between ChatGPT and Perplexity, and FKGL scoring between ChatGPT and Perplexity AI. Conclusions: ChatGPT and Google Bard were the top performers based on their quality, understandability, and actionability. Despite Perplexity scoring higher in readability, the generated text still maintained an eleventh-grade complexity. Perplexity stood out for its extensive use of citations; however, it offered repetitive answers despite the diversity of questions posed to it. Google Bard demonstrated a high level of detail in its answers, offering additional value through visual aids. With improvements in technology, these AI chatbots may improve. Until then, patients and providers should be aware of the strengths and shortcomings of each. Full article

The increasing use of artificial intelligence (hereafter AI) in education, particularly through large-scale language models such as ChatGPT and Bing, offers both challenges and opportunities. These models facilitate interaction in conversations and can perform tasks that require natural language processing, from answering questions to solving problems. However, their integration into education raises concerns about the credibility and reliability of the information they provide and about the role of the teacher, emphasizing the need for guided use in educational environments. This article contributes to the discourse from the perspective of the pedagogy of sociology, focusing on the role of chatbots in analyzing texts within the social sciences and humanities fields. Our pilot study, conducted with 17 first-year master’s students studying sociology, reveals that while chatbots can optimize the creation of summaries and the provision of basic information, their reliance on sources such as Wikipedia calls into question the depth and impartiality of the content. In addition, students have criticized chatbots for providing biased or inaccurate outputs. A significant part of our research has compared the epistemological and methodological approaches of chatbots with a traditional, independent literature analysis (deep reading), and we found notable differences in learning outcomes. However, a hybrid approach that combines AI tools with conventional methods offers a promising way to improve learning and teaching strategies and can enhance the critical analytical skills that are crucial for future pedagogies. Full article

Background: In recent years, there has been remarkable growth in AI-based applications in healthcare, with a significant breakthrough marked by the launch of large language models (LLMs) such as ChatGPT and Google Bard. Patients and health professional students commonly utilize these models due to their accessibility. The increasing use of LLMs in healthcare necessitates an evaluation of their ability to generate accurate and reliable responses. Objective: This study assessed the performance of LLMs in answering orthodontic-related queries through a systematic review and meta-analysis. Methods: A comprehensive search of PubMed, Web of Science, Embase, Scopus, and Google Scholar was conducted up to 31 October 2024. The quality of the included studies was evaluated using the Prediction model Risk of Bias Assessment Tool (PROBAST), and R Studio software (Version 4.4.0) was employed for meta-analysis and heterogeneity assessment. Results: Out of 278 retrieved articles, 10 studies were included. The most commonly used LLM was ChatGPT (10/10, 100% of papers), followed by Google’s Bard/Gemini (3/10, 30% of papers), and Microsoft’s Bing/Copilot AI (2/10, 20% of papers). Accuracy was primarily evaluated using Likert scales, while the DISCERN tool was frequently applied for reliability assessment. The meta-analysis indicated that the LLMs, such as ChatGPT-4 and other models, do not significantly differ in generating responses to queries related to the specialty of orthodontics. The forest plot revealed a Standard Mean Deviation of 0.01 [CI: 0.42–0.44]. No heterogeneity was observed between the experimental group (ChatGPT-3.5, Gemini, and Copilot) and the control group (ChatGPT-4). However, most studies exhibited a high PROBAST risk of bias due to the lack of standardized evaluation tools. Conclusions: ChatGPT-4 has been extensively used for a variety of tasks and has demonstrated advanced and encouraging outcomes compared to other LLMs, and thus can be regarded as a valuable tool for enhancing educational and learning experiences. While LLMs can generate comprehensive responses, their reliability is compromised by the absence of peer-reviewed references, necessitating expert oversight in healthcare applications. Full article

The Generative Pre-trained Transformer (GPT) is a highly advanced natural language processing model. This model can generate conversation-style responses to user input. The rapid rise of GPT has transformed academic domains, with studies exploring the potential of chatbots in education. This research investigates the effectiveness of ChatGPT 3.5, ChatGPT 4.0 by OpenAI, and Chatbot Bing by Microsoft in solving statistical exam-type problems in the educational setting. In addition to quantifying the errors made by these chatbots, this study seeks to understand the causes of these errors to provide recommendations. A mixed-methods approach was employed to achieve this goal, including quantitative and qualitative analyses (Grounded Theory with semi-structured interviews). The quantitative stage involves statistical problem-solving exercises for undergraduate engineering students, revealing error rates based on the reason for the error, statistical fields, sub-statistics fields, and exercise types. The quantitative analysis provided crucial information necessary to proceed with the qualitative study. The qualitative stage employs semi-structured interviews with selected chatbots; this includes confrontation between them that generates agreement, disagreement, and differing viewpoints. On some occasions, chatbots tend to maintain rigid positions, lacking the ability to adapt or acknowledge errors. This inflexibility may affect their effectiveness. The findings contribute to understanding the integration of AI tools in education, offering insights for future implementations and emphasizing the need for critical evaluation and responsible use. Full article

Background and Objectives: Despite CTAs being critical for preoperative planning in autologous breast reconstruction, experienced plastic surgeons may have differing preferences for which side of the abdomen to use for unilateral breast reconstruction. Large language models (LLMs) have the potential to assist medical imaging interpretation. This study compares the perforator selection preferences of experienced plastic surgeons with four popular LLMs based on CTA images for breast reconstruction. Materials and Methods: Six experienced plastic surgeons from Australia, the US, Italy, Denmark, and Argentina reviewed ten CTA images, indicated their preferred side of the abdomen for unilateral breast reconstruction and recommended the type of autologous reconstruction. The LLMs were prompted to do the same. The average decisions were calculated, recorded in suitable tables, and compared. Results: The six consultants predominantly recommend the DIEP procedure (83%). This suggests experienced surgeons feel more comfortable raising DIEP than TRAM flaps, which they recommended only 3% of the time. They also favoured MS TRAM and SIEA less frequently (11% and 2%, respectively). Three LLMs—ChatGPT-4o, ChatGPT-4, and Bing CoPilot—exclusively recommended DIEP (100%), while Claude suggested DIEP 90% and MS TRAM 10%. Despite minor variations in side recommendations, consultants and AI models clearly preferred DIEP. Conclusions: Consultants and LLMs consistently preferred DIEP procedures, indicating strong confidence among experienced surgeons, though LLMs occasionally deviated in recommendations, highlighting limitations in their image interpretation capabilities. This emphasises the need for ongoing refinement of AI-assisted decision support systems to ensure they align more closely with expert clinical judgment and enhance their reliability in clinical practice. Full article

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. Full article

Chronic kidney disease (CKD) patients can benefit from personalized education on lifestyle and nutrition management strategies to enhance healthcare outcomes. The potential use of chatbots, introduced in 2022, as a tool for educating CKD patients has been explored. A set of 15 questions on lifestyle modification and nutrition, derived from a thorough review of three specific KDIGO guidelines, were developed and posed in various formats, including original, paraphrased with different adverbs, incomplete sentences, and misspellings. Four versions of AI were used to answer these questions: ChatGPT 3.5 (March and September 2023 versions), ChatGPT 4, and Bard AI. Additionally, 20 questions on lifestyle modification and nutrition were derived from the NKF KDOQI guidelines for nutrition in CKD (2020 Update) and answered by four versions of chatbots. Nephrologists reviewed all answers for accuracy. ChatGPT 3.5 produced largely accurate responses across the different question complexities, with occasional misleading statements from the March version. The September 2023 version frequently cited its last update as September 2021 and did not provide specific references, while the November 2023 version did not provide any misleading information. ChatGPT 4 presented answers similar to 3.5 but with improved reference citations, though not always directly relevant. Bard AI, while largely accurate with pictorial representation at times, occasionally produced misleading statements and had inconsistent reference quality, although an improvement was noted over time. Bing AI from November 2023 had short answers without detailed elaboration and sometimes just answered “YES”. Chatbots demonstrate potential as personalized educational tools for CKD that utilize layman’s terms, deliver timely and rapid responses in multiple languages, and offer a conversational pattern advantageous for patient engagement. Despite improvements observed from March to November 2023, some answers remained potentially misleading. ChatGPT 4 offers some advantages over 3.5, although the differences are limited. Collaboration between healthcare professionals and AI developers is essential to improve healthcare delivery and ensure the safe incorporation of chatbots into patient care. Full article

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. Full article

Background: Large language models (LLMs) are becoming increasingly important as they are being used more frequently for providing medical information. Our aim is to evaluate the effectiveness of electronic artificial intelligence (AI) large language models (LLMs), such as ChatGPT-4, BingAI, and Gemini in responding to patient inquiries about retinopathy of prematurity (ROP). Methods: The answers of LLMs for fifty real-life patient inquiries were assessed using a 5-point Likert scale by three ophthalmologists. The models’ responses were also evaluated for reliability with the DISCERN instrument and the EQIP framework, and for readability using the Flesch Reading Ease (FRE), Flesch-Kincaid Grade Level (FKGL), and Coleman-Liau Index. Results: ChatGPT-4 outperformed BingAI and Gemini, scoring the highest with 5 points in 90% (45 out of 50) and achieving ratings of “agreed” or “strongly agreed” in 98% (49 out of 50) of responses. It led in accuracy and reliability with DISCERN and EQIP scores of 63 and 72.2, respectively. BingAI followed with scores of 53 and 61.1, while Gemini was noted for the best readability (FRE score of 39.1) but lower reliability scores. Statistically significant performance differences were observed particularly in the screening, diagnosis, and treatment categories. Conclusion: ChatGPT-4 excelled in providing detailed and reliable responses to ROP-related queries, although its texts were more complex. All models delivered generally accurate information as per DISCERN and EQIP assessments. Full article